azure blob storage

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📢 [Public Preview] Accelerating BlobNFS throughput & scale with FUSE for superior performance
Azure Blob Storage can be mounted and accessed like a local file system using BlobFuse, which is a FUSE-based driver for the Blob REST API. Customers choose BlobFuse for AI/ML, HPC, analytics and backup workloads. It provides exceptionally high throughput along with benefits like local caching and security integration via Azure Entra ID. For customers requiring NFS 3.0 protocol support or POSIX compliance, Azure Blob Storage also natively supports NFSv3 (aka BlobNFS). It enables Azure Blob storage access for customers’ legacy applications without requiring changes. BlobNFS is accessed via the Linux NFS client combined with our AZNFS mount helper package, which streamlines mounting and reliably connecting to Blob Storage’s NFS endpoints. Please note that BlobNFS only supports access over a virtual network since Azure Entra ID based auth isn’t yet available on NFS 3.0. Today, we’re excited to announce an update to AZNFS (3.0) for BlobNFS, which now uses the same libfuse3 library that powers BlobFuse bringing significant improvements in performance and scale. The updated AZNFS for BlobNFS delivers significantly higher throughput, larger file support, better metadata performance, and removes user group limits, enhancing performance for demanding workloads. Maximize Virtual Machine throughput: AZNFS now supports up to 256 TCP connections (up from16 in native NFS client) allowing throughput to reach VM NIC bandwidth (the maximum data transfer rate of virtual machine’s network interface card) or storage account limits. This benefits HPC workloads by ensuring high throughput for large dataset operations. Additionally, a small number (4 or fewer) of parallel file reads/writes can now fully saturate the VM NIC bandwidth even for larger VM sizes. Enhanced read/write speed: The updated AZNFS client outperforms native NFS client for read and write scenarios. For example, single file read/write performance is improved by a factor of 5x and 3x respectively, which can be useful for large database backup tasks requiring high single file throughput for writing and reading backup files. Refer to the link for a detailed performance comparison. Removal of the user's group limit: Linux NFS clients with a local Identity server can pass access permissions for up to 16 groups of a user, restricting resource access for users belonging to more than 16 groups. This update allows FUSE to handle permission checks, removing the 16-group limitation. Improved metadata query performance: READDIR can query more directory entries in one call. The Linux client has a limit of 1MB, whereas the updated AZNFS can now reach up to 3 MB. Customers with numerous files will experience quicker listing and metadata operations with reduced latency. This will be beneficial for workloads like EDA (Electronic Design Automation) and HPC (High Performance Computing) which often involve reading metadata for considerable number of files before selecting a subset for processing. Support for large file sizes (up to 5TB): The new release can support larger file sizes for sequential write patterns. Due to larger block sizes possible with AZNFS, users can create larger files up to the 5TB limit. With Linux clients, under best conditions, the max. file sizes were limited to ~3TB. CAD tools producing simulation and checkpoint data files over 3TB will benefit from this improvement. The following charts compare performance between updated AZNFS and the Native Linux client. Please refer the detailed benchmarks for more details. [Test parameters - VM: Standard D96ds v5, File size: 100GB, Linux NFS is with nconnect =16, Linux kernel 5.x.x ; Test used: DD test] Note: The VM supports higher read throughput than write throughput. For updated AZNFS, throughput starting from 4 parallel file read/write operations is constrained by VM NIC bandwidth, or it can scale higher. Getting Started Please register for the preview using this form. Please refer the link for instructions on how to install and use latest version of AZNFS. For any queries or feedback, please contact us at aznfs@microsoft.com. References: What is BlobFuse? - BlobFuse2 - Azure Storage | Microsoft Learn Network File System (NFS) 3.0 protocol support for Azure Blob Storage Mount Blob Storage by using the Network File System (NFS) 3.0 protocol on Linux Instructions to install and use latest version of AZNFS · Azure/AZNFS-mount Wiki
niranja1890
Jul 03, 2025 Place Azure Storage Blog
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Building a Scalable Web Crawling and Indexing Pipeline with Azure storage and AI Search
In the ever-evolving world of data management, keeping search indexes up-to-date with dynamic data can be challenging. Traditional approaches, such as manual or scheduled indexing, are resource-intensive, delay-prone, and difficult to scale. Azure Blob Trigger combined with an AI Search Indexer offers a cutting-edge solution to overcome these challenges, enabling real-time, scalable, and enriched data indexing. This blog explores how Blob Trigger, integrated with Azure Cognitive Search, transforms the indexing process by automating workflows and enriching data with AI capabilities. It highlights the step-by-step process of configuring Blob Storage, creating Azure Functions for triggers, and seamlessly connecting with an AI-powered search index. The approach leverages Azure's event-driven architecture, ensuring efficient and cost-effective data management.
navprsingh
May 02, 2025 Place Azure Storage Blog
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Automating Data Management: Azure Storage Actions Overview
Azure Storage Data Management Solutions/Services: 1. Movement: An end-to-end experience to discover, plan and move data into Azure in a performant, cost effective, secure, and reliable way 2. Insights: Store and manage metrics and metadata enabling deep insights into the data estate 3. Actions: Flexible, scalable, serverless platform to effortlessly process data for data management, protection, security and governance Big Data Challenges: Organizations must manage ever-increasing data volumes Data Management Data Movement Tagging & Classification Security and Access Control Data Protection Orchestration Customer challenges: Customers have increasingly large volumes of data in hundreds of storage accounts with billions of objects Challenging to process millions of objects for bulk operations Lifecycle management, data protection, object tagging and security operations require increasing complexity Out of box policies in storage can be constricted and extensibility is limited Introducing Azure Storage Actions A fully managed platform that helps you automate data management tasks.Process billions of blobs in your storage account effortlessly. Supports Azure Blob Storage and Azure Data Lake Storage How Storage Actions Works? Event-Condition-Action framework Schedule-based and on-demand execution Conditional processing of blobs based on blob properties Use native blob operations as actions on the blob Serverless Fully managed infrastructure Deploy in minutes – eliminates need for any complex software or infrastructure Auto-scales with your storage No-code composition & simplified management Use clicks to compose tasks Easily apply tasks to multiple storage accounts Monitor task execution across your storage execution with aggregate metrics and drilldowns Storage Actions Overview Data Protection – blob immutability, legal holds and blob expiry Cost optimization – tiering or deleting blobs Managing blob tags Undelete blobs Copy blobs, folder operations Key Concepts How to start with Storage Actions: Login to Portal: Search for Azure Storage Actions Create a task à Define Conditions: [[and(endsWith(Name, 'pdf'), equals(BlobType, 'BlockBlob'))]] The query [[and(endsWith(Name, 'pdf'), equals(BlobType, 'BlockBlob'))]] is a logical expression used to filter and retrieve specific items from a dataset. Here's a breakdown of its components: endsWith(Name, 'pdf'): This part of the query checks if the Name attribute of an item ends with the string 'pdf'. Essentially, it filters items whose names end with .pdf, indicating that they are PDF files. equals(BlobType, 'BlockBlob'): This part of the query checks if the BlobType attribute of an item is equal to 'BlockBlob'. This is used to filter items that are of the type BlockBlob, which is a type of storage blob in cloud storage systems. and(...): The and operator combines the two conditions above. It ensures that only items meeting both criteria are retrieved. In other words, the query will return items that are PDF files (Name ends with .pdf) and are of the type BlockBlob. In summary, this query is used to find items that are PDF files stored as BlockBlob in a dataset. If above query verified, it would set tags process = true, and set blob immutability policy to locked Create Assignment: Once task will run it will create report which will get added in Storage account container Example Use Cases Retention Management: Automatically manage the retention and expiry durations of audio files using a combination of index tags and creation times 4. Version History Management: Manage the retention and lifecycle of datasets using metadata and tags for optimal protection and cost 4. One-off Processing: Define tasks to rehydrate large datasets from the archive tier, reset tags on part of a dataset, or clean-up redundant and outdated datasets
Shivani650
Apr 30, 2025 Place Azure Storage Blog
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Holding forensic evidence: The role of hybrid cloud in successful preservation and compliance
Disclaimer: The following is a post authored by our partner Tiger Technology. Tiger Technology has been a valued partner in the Azure Storage ecosystem for many years and we are happy to have them share details on their innovative solution! Police departments worldwide are grappling with a digital explosion. From body camera footage to social media captures, the volume and variety of evidence have surged, creating a storage, and management challenge like never before. A single police department needing to store 2–5 petabytes of data—and keep some of it for 100 years. How can they preserve the integrity of this data, make it cost-effective, and ensure compliance with legal requirements? The answer lies in hybrid cloud solutions, specifically Microsoft Azure Blob Storage paired with Tiger Bridge. These solutions are empowering law enforcement to manage, and store evidence at scale, without disrupting workflows. But what exactly is hybrid cloud, and why is it a game-changer for digital evidence management? What is a hybrid cloud? A hybrid cloud combines public or private cloud services with on-premises infrastructure. It gives organizations the flexibility to mix, and match environments, allowing them to choose the best fit for specific applications, and data. This flexibility is especially valuable in highly regulated industries like law enforcement, where strict data privacy, and compliance rules govern how evidence is stored, processed, and accessed. Hybrid cloud also facilitates a smoother transition to public cloud solutions. For instance, when a data center reaches capacity, hybrid setups allow agencies to scale dynamically while maintaining control over their most sensitive data. It’s not just about storage—it's about creating a robust, compliant infrastructure for managing enormous volumes of evidence. What makes digital evidence so complex? Digital evidence encompasses any information stored or transmitted in binary form that can be used in court. It includes computer hard drives, phone records, social media posts, surveillance footage, etc. The challenge isn’t just collecting this data—it’s preserving its integrity. Forensic investigators must adhere to strict chain-of-custody protocols to prove in court that the evidence: Is authentic and unaltered, Has been securely stored with limited access, Is readily available when needed. With the surge in data volumes and complexity, traditional storage systems often fall short. That’s where hybrid cloud solutions shine, offering scalable, secure, and cost-effective options that keep digital evidence admissible while meeting compliance standards. The challenges police departments face Digital evidence is invaluable. Storing and managing it is a challenging task, and requires dealing with several aspects: Short-term storage problems The sheer scale of data can overwhelm local systems. Evidence must first be duplicated using forensic imaging to protect the original file. But housing these duplicates, especially with limited budgets, strains existing resources. Long-term retention demands In some jurisdictions, evidence must be retained for decades—sometimes up to a century. Physical storage media, like hard drives or SSDs, degrade over time and are expensive to maintain. Transitioning this data to cloud cold storage offers a more durable and cost-effective solution. Data integrity and legal admissibility Even the slightest suspicion of tampering can render evidence inadmissible. Courts require robust proof of authenticity and integrity, including cryptographic hashes and digital timestamps. Failing to maintain a clear chain of custody could jeopardize critical cases. Solving the storage puzzle with hybrid cloud For law enforcement agencies, managing sensitive evidence isn't just about storage—it's about creating a system that safeguards data integrity, ensures compliance, and keeps costs under control. Traditional methods fall short in meeting these demands as the volume of digital evidence continues to grow. This is where hybrid cloud technology stands out, offering a powerful combination of on-premises infrastructure and cloud capabilities. Microsoft Azure, a leader in cloud solutions, brings critical features to the table, ensuring evidence remains secure, accessible, and compliant with strict legal standards. But storage alone isn't enough. Efficient file management is equally crucial for managing vast datasets while maintaining workflow efficiency. Tools like Tiger Bridge complement Microsoft Azure by bridging the gap between local and cloud storage, adding intelligence and flexibility to how evidence is preserved and accessed. Microsoft Azure Blob Storage Azure Blob Storage is massively scalable and secure object storage. For the purposes of law enforcement, among other features, it offers: Automatic Tiering: Automatically moves data between hot and cold tiers, optimizing costs, Durability: Up to sixteen 9s (99.99999999999999%) of durability ensures data integrity for decades. Metadata management: Add custom tags or blob indexes, such as police case classifications, to automate retention reviews. Microsoft Azure ensures evidence is secure, accessible, and compliant with legal standards. Tiger Bridge: Smart File Management Tiger Bridge enhances Microsoft Azure’s capabilities by seamlessly integrating local and cloud storage with powerful features tailored for forensic evidence management. Tiger Bridge is a software-only solution that integrates seamlessly with Windows servers. It handles file replication, space reclaiming, and archiving—all while preserving existing workflows and ensuring data integrity and disaster recovery. With Tiger Bridge, police departments can transition to hybrid cloud storage without adding hardware or altering processes. Data replication Tiger Bridge replicates files from on-premises storage to cloud storage, ensuring a secure backup. Replication policies run transparently in the background, allowing investigators to work uninterrupted. Files are duplicated based on user-defined criteria, such as priority cases or evidence retention timelines. Space reclamation Once files are replicated to the cloud, Tiger Bridge replaces local copies with “nearline” stubs. These stubs look like the original files but take up virtually no space. When a file is needed, it’s automatically retrieved from the cloud, reducing storage strain on local servers. Data archiving For long-term storage, Tiger Bridge moves files from hot cloud tiers to cold and / or archive storage. Files in the archive tier are replaced with "offline" stubs. These files are not immediately accessible but can be manually retrieved and rehydrated when necessary. This capability allows law enforcement agencies to save on costs while still preserving access to critical evidence. Checksum for data integrity On top of strong data integrity and data protection features already built-in in Azure Storage Blob service, Tiger Bridge goes a step further in ensuring data integrity by generating checksums for newly replicated files. These cryptographic signatures allow agencies to verify that files in the cloud are identical to the originals stored on premises. This feature is essential for forensic applications, where the authenticity of evidence must withstand courtroom scrutiny. Data integrity verification is done during uploads and retrievals, ensuring that files remain unaltered while stored in the cloud. For law enforcement, checksum validation provides peace of mind, ensuring that evidence remains admissible in court and meets strict regulatory requirements Disaster Recovery In the event of a local system failure, Tiger Bridge allows for immediate recovery. All data remains accessible in the cloud, and reinstalling Tiger Bridge on a new server re-establishes access without needing to re-download files. A real-life scenario Imagine a police department dealing with petabytes of video evidence from body cameras, surveillance footage, and digital device extractions. A simple, yet effective typical real-life scenario follows the similar patterns: Investigators collect and image evidence files, Tiger Bridge replicates this data to Azure Blob Storage, following predefined rules, Active cases remain in Azure’s hot tier, while archival data moves to cost-effective cold storage, Metadata tags in Azure help automate case retention reviews, flagging files eligible for deletion. This approach ensures evidence is accessible when needed, secure from tampering, and affordable to store long-term. The results speak for themselves. Adopting a hybrid cloud strategy delivers tangible benefits: Operational efficiency: Evidence is readily accessible without the need for extensive hardware investments and maintenance. Cost savings: Automating data tiering reduces storage costs while maintaining accessibility. Workflow continuity: Investigators can maintain existing processes with minimal disruption. Enhanced compliance: Robust security measures and chain-of-custody tracking ensure legal standards are met. A future-proof solution for digital forensics As digital evidence grows in both volume and importance, police organizations must evolve their storage strategies. Hybrid cloud solutions like Azure Blob Storage and Tiger Bridge offer a path forward: scalable, secure, and cost-effective evidence management designed for the demands of modern law enforcement. The choice is clear: Preserve the integrity of justice by adopting tools built for the future. About Tiger Technology Tiger Technology helps organizations with mission-critical deployments optimize their on-premises storage and enhance their workflows through cloud services. The company is a validated ISV partner for Microsoft in three out of five Azure Storage categories: Primary and Secondary Storage; Archive, Backup and BCDR, and Data Governance, Management, and Migration. Tiger Bridge SaaS offering on Azure Marketplace is an Azure benefit-eligible, data management software enabling seamless hybrid cloud infrastructure. Installed in the customer’s on-premises or cloud environment, Tiger Bridge intelligently connects file data across file and object storage anywhere for data lifecycle management, global file access, Disaster Recovery, data migration and access to insights. Tiger Bridge supports all Azure Blob Storage tiers, including cold and archive tiers for long-term archival of data. Read more by Tiger Technology on the Tech Community Blog: Modernization through Tiger Bridge Hybrid Cloud Data Services On-premises-first hybrid workflows in healthcare. Why start with digital pathology?
dukicn
Apr 28, 2025 Place Azure Storage Blog
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Announcing General Availability of Next generation Azure Data Box Devices
Today, we’re excited to announce the General Availability of Azure Data Box 120 and Azure Data Box 525, our next-generation compact, NVMe-based Data Box devices. These devices are currently available for customers to order in the US, US Gov, Canada, EU and the UK Azure regions, with broader availability coming soon. Since the preview announcement at Ignite '24, we have successfully ingested petabytes of data, encompassing multiple orders serving customers across various industry verticals. Customers have expressed delight over the reliability and efficiency of the new devices with up to 10x improvement in data transfer rates, highlighting them as a valuable and essential asset for large-scale data migration projects. These new device offerings reflect insights gained from working with our customers over the years and understanding their evolving data transfer needs. They incorporate several improvements to accelerate offline data transfers to Azure, including: Fast copy - Built with NVMe drives for high-speed transfers and improved reliability and support for faster network connections Ease of use - larger capacity offering (525 TB) in a compact form-factor for easy handling Resilient - Ruggedized devices built to withstand rough conditions during transport Secure - Enhanced physical, hardware and software security features Broader availability – Presence planned in more Azure regions, meeting local compliance standards and regulations What’s new? Improved Speed & Efficiency NVMe-based devices offer faster data transfer rates, providing a 10x improvement in data transfer speeds to the device as compared to previous generation devices. With a dataset comprised of mostly large (TB-sized) files, on average half a petabyte can be copied to the device in under two days. High-speed transfers to Azure with data upload up to 5x faster for medium to large files, reducing the lead time for your data to become accessible in the Azure cloud. Improved networking with support for up to 100 GbE connections, as compared to 10 GbE on the older generation of devices. Two options with usable capacity of 120 TB and 525 TB in a compact form factor meeting OSHA requirements. Devices ship the next day air in most regions. Learn more about the performance improvements on Data Box 120 and Data Box 525. Enhanced Security The new devices come with several new physical, hardware and software security enhancements. This is in addition to the built in Azure security baseline for Data Box and Data Box service security measures currently supported by the service. Secure boot functionality with hardware root of trust and Trusted Platform Module (TPM) 2.0. Custom tamper-proof screws and built-in intrusion detection system to detect unauthorized device access. AES 256-bit BitLocker software encryption for data at rest is currently available. Hardware encryption via the RAID controller, which will be enabled by default on these devices, is coming soon. Furthermore, once available, customers can enable double encryption through both software and hardware encryption to meet their sensitive data transfer requirements. These ISTA 6A compliant devices are built to withstand rough conditions during shipment while keeping both the device and your data safe and intact. Learn more about the enhanced security features on Data Box 120 and Data Box 525. Broader Azure region coverage A recurring request from our customers has been wider regional availability of higher-capacity devices to accelerate large migrations. We’re happy to share that Azure Data Box 525 will be available across US, US Gov, EU, UK and Canada with broader presence in EMEA and APAC regions coming soon. This marks a significant improvement in the availability of a large-capacity device as compared to the current Data Box Heavy which is available only in the US and Europe. What our customers have to say For the last several months, we’ve been working directly with our customers of all industries and sizes to leverage the next generation devices for their data migration needs. Customers love the larger capacity with form-factor familiarity, seamless set up and faster copy. “We utilized Azure Data Box for a bulk migration of Unix archive data. The data, originating from IBM Spectrum Protect, underwent pre-processing before being transferred to Azure blobs via the NFS v4 protocol. This offline migration solution enabled us to efficiently manage our large-scale data transfer needs, ensuring a seamless transition to the Azure cloud. Azure Data Box proved to be an indispensable tool in handling our specialized migration scenario, offering a reliable and efficient method for data transfer.” – ST Microelectronics Backup & Storage team “This new offering brings significant advantages, particularly by simplifying our internal processes. With deployments ranging from hundreds of terabytes to even petabytes, we previously relied on multiple regular Data Box devices—or occasionally Data Box Heavy devices—which required extensive operational effort. The new solution offers sizes better aligned with our needs, allowing us to achieve optimal results with fewer logistical steps. Additionally, the latest generation is faster and provides more connectivity options at data centre premises, enhancing both efficiency and flexibility for large-scale data transfers.” - Lukasz Konarzewski, Senior Data Architect, Commvault “We have had a positive experience overall with the new Data Box devices to move our data to Azure Blob storage. The devices offer easy plug and play installation, detailed documentation especially for the security features and good data copy performance. We would definitely consider using it again for future large data migration projects.” – Bas Boeijink, Cloud Engineer, Eurofiber Cloud Infra Upcoming changes to older SKUs availability Note that in regions where the next-gen devices are available, new orders for Data Box 80 TB and Data Box Heavy devices cannot be placed post May 31, 2025. We will however continue to process and support all existing orders. Order your device today! The devices are currently available for customers to order in the US, Canada, EU, UK, and US Gov Azure regions. We will continue to expand to more regions in the upcoming months. Azure Data Box provides customers with one of the most cost-effective solutions for data migration, offering competitive pricing with the lowest cost per TB among offline data transfer solutions. You can learn more about the pricing across various regions by visiting our pricing page. You can use the Azure portal to select the requisite SKU suitable for your migration needs and place the order. Learn more about the all-new Data Box devices here. We are committed to continuing to deliver innovative solutions to lower the barrier for bringing data to Azure. Your feedback is important to us. Tell us what you think about the new Azure Data Box devices by writing to us at DataBoxPM@microsoft.com – we can’t wait to hear from you.
Anusha_Subramanian
Apr 16, 2025 Place Azure Storage Blog
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Azure Blob Storage SFTP: General Availability of ACLs (Access Control Lists) of local users
We are excited to announce the general availability of ACLs (Access Control Lists) for Azure Blob Storage SFTP local users. ACLs make it simple and intuitive for administrators to manage fine-grained access control to blobs and directories for Azure Blob Storage SFTP local users. Azure Blob Storage SFTP Azure Blob storage supports the SSH File Transfer Protocol (SFTP) natively. SFTP on Azure Blob Storage lets you securely connect to and interact with the contents of your storage account by using an SFTP client, allowing you to use SFTP for file access, file transfer, and file management. Learn more here. Azure Blob Storage SFTP is used by a significant number of our customers, who have shared overwhelmingly positive feedback. It eliminates the need for third-party or custom SFTP solutions involving cumbersome maintenance steps such as VM orchestration. Local users Azure Blob Storage SFTP utilizes a new form of identity management called local users. Local users must use either a password or a Secure Shell (SSH) private key credential for authentication. You can have a maximum of 25,000 local users for a storage account. Learn more about local users here. Access Control for local users There are two ways in which access control can be attained for local users. 1. Container permissions By using container permissions, you can choose which containers you want to grant access to and what level of access you want to provide (Read, Write, List, Delete, Create, Modify Ownership, and Modify Permissions). Those permissions apply to all directories and subdirectories in the container. Learn more here. 2. ACLs for local users What are ACLs? ACLs (Access Control Lists) let you grant "fine-grained" access, such as write access to a specific directory or file, which isn’t possible with Container Permissions. More fine-grained access control has been a popular ask amongst our customers, and we are very excited to make this possible now with ACLs. A common ACL use case is to restrict a user's access to a specific directory without letting that user access other directories within the same container. This can be repeated for multiple users so that they each have granular access to their own directory. Without ACLs, this would require a container per local user. ACLs also make it easier for administrators to manage access for multiple local users with the help of groups. Learn more about ACLs for local users here. How to set and modify the ACL of a file or a directory? You can set and modify the permission level of the owning user, owning group, and all other users of an ACL by using an SFTP client. You can also change the owning user or owning group of a blob or directory. These operations require 'Modify Permissions' and 'Modify Ownership' container permissions, respectively. Note: Owning users can now also modify the owning group and permissions of a blob or directory without container permissions. This is a new feature enhancement added during the General Availability phase of ACLs for local users. For any user that is not the owning user, container permissions are still required. Learn more here. These enhancements significantly improve the management and usability of Azure Blob Storage SFTP by providing more granular access control over the container model and extending customer options. Please reach out to blobsftp@microsoft.com for feedback about SFTP for Azure Blob Storage. We look forward to your continued support as we strive to deliver the best possible solutions for your needs.
JeevanManoj
Apr 10, 2025 Place Azure Storage Blog
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ABFS Driver for Azure Blob Storage
Announcing the Release of Azure Blob Filesystem (ABFS) Driver with Support for Blob Endpoint in Flat Namespace (FNS) Storage Accounts.
ParijatD
Apr 09, 2025 Place Azure Storage Blog
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Building an AI-Powered ESG Consultant Using Azure AI Services: A Case Study
In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. Watch Our Video The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. Watch Our Video
Aditya_vp
Apr 06, 2025 Place Azure
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Simplifying Kubernetes Data Protection with CloudCasa and Microsoft Azure Blob Storage
NOTE: This article is co-authored with our partner, CloudCasa. Ensuring reliable backups and fast recoveries in these hybrid setups is challenging. CloudCasa’s partnership with Microsoft Azure Blob Storage offers a robust solution to this challenge, delivering scalable, secure, and cost-effective data protection for Kubernetes cloud and hybrid cloud workloads. In this blog, we will look at how CloudCasa and Azure Blob solve common data protection challenges. We will explore how they simplify backups for Azure Kubernetes Service (AKS), offer centralized protection for hybrid clusters with Azure Arc, and give you a single view for managing VM and container backups. We will also cover how they help with disaster recovery in Kubernetes environments and how they push data to Azure Blob in an efficient manner. By the end, you will see how this partnership prepares your data protection strategy for the future. Seamless Integration with the Azure Ecosystem A good backup solution should not create more work. It should blend into the tools you already use. CloudCasa is purpose-built to integrate effortlessly with the Microsoft Azure ecosystem. This integration provides robust data protection for Kubernetes and hybrid cloud environments through: CloudCasa for AKS: Azure Kubernetes Service (AKS) is a popular way to run Kubernetes in Microsoft Azure. CloudCasa for AKS gives you an easy way to protect your containers, volumes, and metadata. You can set schedules and policies in CloudCasa to back up entire AKS clusters. If something goes wrong, you can quickly restore your workloads to the same or a different AKS cluster. Learn more about CloudCasa for AKS. CloudCasa for Azure Arc: Azure Arc extends Azure services to on-premises, multi-cloud, and edge environments. That means you can manage servers and Kubernetes clusters from a single point. CloudCasa for Azure Arc takes advantage of this central view. It lets you protect your Arc-enabled Kubernetes clusters wherever they run. Instead of juggling multiple backup tools for each environment, you can protect them all from a single place. Learn more about CloudCasa for Azure Arc. Azure Marketplace: Deploying and paying for new services often involves many steps. But CloudCasa is available on the Azure Marketplace, making it easy to start. You can sign up with a few clicks and have the charges appear on your Azure bill. This straightforward setup reduces friction and helps teams begin protecting their workloads right away. Supporting Hybrid Cloud and Kubernetes Workloads Modern organizations rarely keep all of their data in a single place. Some workloads might still run on-premises, while others run in different clouds. Kubernetes clusters can be scattered across these environments. Without a proper solution, managing backups for all these clusters can get messy. CloudCasa manages backups in a clever way. By default, it uses incremental backups whenever possible, which means it only moves the data that has changed since the last backup. This minimizes network usage and lowers storage costs. All these backups are stored in Azure Blob, which is both scalable and cost-effective. Unified Backup Management for Arc-Enabled Clusters When you connect your Kubernetes clusters to Azure Arc, you get a single view of all those clusters in the Azure portal. CloudCasa fits into this model by letting you create and apply backup policies across your hybrid environment. Instead of setting backup rules cluster-by-cluster, you can handle them from one place. This unification saves time and helps you avoid errors. A good backup is only valuable if you can recover from it quickly and without hassle. CloudCasa simplifies this process. You can restore your containers, PVs (Persistent Volumes), and cluster configurations back to their original places. This limits downtime and keeps your business moving. If your cluster is no longer available, you can also restore to a new location, including AKS, to keep your workloads running. Streamlining Kubernetes Disaster Recovery Disaster recovery (DR) is a top priority for many organizations. Traditional DR methods might involve copying data to a secondary site or region, then running complex scripts to bring everything back online. In a Kubernetes world, DR can be simpler if done the right way. Containers let you package your applications and dependencies in a portable format. If you have regular backups, you can quickly spin up your applications on another cluster if one site goes down. CloudCasa focuses on container-aware backups, so your entire environment—services, pods, configuration, and persistent data—can be restored. You don’t have to worry about missing parts of your cluster during recovery. If disaster strikes, you might need to create a brand-new cluster in Azure Kubernetes Service. CloudCasa makes this smoother. You can use your existing backups to set up a cluster in AKS, restoring namespaces, deployments, and volumes. This approach gives you a quick path to recovery without diving into manual steps or complicated scripts. Short-Term Recovery and Long-Term Archiving Azure Blob storage can keep multiple versions of your backups. This is helpful if you need to recover something quickly, like a file that was deleted by mistake. You can also use Azure Blob for long-term archiving, storing older backups at lower cost in access tiers like Cool or Archive. That means you meet compliance needs without breaking the bank, and you can still restore data if needed down the line. Sending backup data to the cloud must be efficient, both in terms of resource usage and network costs. If your backups overload your network or compute resources, it can slow down your day-to-day operations. CloudCasa is built to avoid these problems. Lightweight Agent Technology CloudCasa has a lightweight agent that runs in your Kubernetes clusters. This agent takes care of backup tasks without draining your compute and network resources. The agent only captures what it needs. With incremental backups, you don’t send the same data over and over again, which keeps your workload overhead low. CloudCasa includes metrics that show how much data is being backed up and how quickly. You can also see if there are any issues during the backup process. These insights help you spot bottlenecks and optimize your data protection approach. You can also control how often backups happen, which helps reduce unplanned spikes in storage costs or network usage. Cost-Effective Scalability Azure Blob follows a pay-as-you-go model. You only pay for what you use, whether it’s the size of stored data or how often you read and write that data. As your Kubernetes clusters grow or you add more clusters, CloudCasa and Azure Blob scale with you. There is no need to worry about running out of space or making large upfront investments in storage hardware. The technology landscape keeps changing. Businesses need solutions that can adapt to new platforms, new workloads, and new regulations. CloudCasa and Azure Blob offer that flexibility. Scalable Data Protection for Hybrid and Cloud-Native Environments Today you might be running a few clusters on-premises and a couple in the cloud. Next year, you might expand to more regions or adopt new container platforms. CloudCasa’s agent-based design and Azure Blob’s elastic storage model are built to handle this. You can protect more clusters as you go, without complex setup or hardware upgrades. In many industries, regulations require you to keep data for a certain period and show that it’s stored in a durable, secure way. Azure Blob provides high durability with multiple copies of your data in different locations. CloudCasa’s backups are encrypted in transit and at rest. This means you can meet compliance needs and still have control over your data retention policies. Advanced Recovery Capabilities As you expand, your recovery needs may become more complex. You may have to recover apps across different regions or clouds. CloudCasa gives you the option to restore to different clusters, whether they are on Azure or on another provider. This means you can change course when needed, without being tied to a single environment. And because Azure Blob keeps your data in a secure and highly available way, you can trust that it will be there when you need it. Conclusion Protecting Kubernetes workloads can be simpler than you think. With CloudCasa and Microsoft Azure Blob, you get a scalable and secure backup solution that works across hybrid and cloud-native environments. You also have a unified tool for both container and VM backups, which reduces management overhead. In case of disaster, CloudCasa helps you recover quickly to AKS or your original environment. And because everything is stored in Azure Blob, you benefit from its pay-as-you-go model and global infrastructure. By adopting this integrated approach, you can focus on delivering applications and services, rather than juggling multiple backup systems. CloudCasa makes it easy to set backup schedules and policies, track performance, and restore data wherever you run your workloads. You gain peace of mind knowing your data is protected and can be recovered quickly, without complicated scripts or risky manual steps. Next steps To learn more, visit CloudCasa for AKS and CloudCasa for Azure Arc on the Azure Marketplace, explore CloudCasa.io, or at casa@cloudcasa.io to get started. Additionally, we have a demo video showcasing the capabilities of both AKS and Azure Arc together, providing a comprehensive overview of how these solutions can benefit your organization.
dmalbrough
Mar 20, 2025 Place Azure Storage Blog
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Microsoft Purview Protection Policies for Azure Data Lake & Blob Storage Available in All Regions
Organizations today face a critical challenge: ensuring consistent and automated data governance across rapidly expanding data estates. Driven by the growth of AI and the increasing reliance on vast data volumes for model training, Chief Data Officers (CDOs) and Chief Information Security Officers (CISOs) must prevent unintentional exposure of sensitive data (PII, credit card information) while adhering to data and legal regulations. Many organizations rely on Azure Blob Storage and ADLS for storing vast amounts of data, offering scalable, secure, and highly available cloud storage solutions. While solutions like RBAC (role-based access control), ABAC (attribute-based access control), and ACLs (Access Control Lists) offer secure ways to manage data access, they can operate on metadata such as file paths, tags, or container names. These mechanisms are effective for implementing restrictive data governance by controlling who can access specific files or containers. However, there are scenarios were implementing automatic access controls based on the sensitivity of the content itself is necessary. For example, identifying and protecting sensitive information like credit card numbers within a blob requires more granular control. Ensuring that sensitive content is restricted to specific roles and applications across the organization is crucial, especially as enterprises focus on building new applications and infusing AI into current solutions. This is where integrated solutions like Microsoft Information Protection (MIP) come into play. Microsoft Information Protection (MIP) protection policies provide a solution by enabling organizations to scan and label data based on the content stored in the blob. This allows for applying access controls directly related to the data asset content across storage accounts. By eliminating the need for in-house scanning and labeling, MIP streamlines compliance and helps in applying consistent data governance using a centralized solution. The Solution: Microsoft Purview Information Protection (MIP) Protection Policies for Governance & Compliance Microsoft Purview Information Protection (MIP) provides an efficient and centralized approach to data protection by automatically restricting access to storage data assets based on sensitivity labels discovered through automated scanning and leveraging Protection policies (learn more). This feature builds upon Microsoft Purview's existing capability (learn more) to scan and label sensitive data assets, ensuring robust data protection. This not only enhances data governance but also ensures that data is managed in a way that protects sensitive information, reducing the risk of unauthorized access and maintaining the security and trust of customers. Enhancing Data Governance with MIP Protection policies: Contoso, a multinational corporation, handles large volumes of data stored in Azure Storage (Blob/ADLS). Different users, such as financial auditors, legal advisors, compliance officers, and data analysts, need access to different blobs in the Storage account. These blobs are updated daily with new content, and there can be sensitive data across these blobs. Given the diverse nature of the stored data, Contoso needed an access control method that could restrict access based on data asset sensitivity. For instance, data analysts access the blob named "logs" where log files are uploaded. If these files contain PII or financial data, which should only be accessed by financial officers, the access permissions need to be dynamically updated based on the changing sensitivity of the stored data. MIP protection policies can address this challenge efficiently by automatically limiting access to data based on sensitivity labels found through automated scanning. Key Benefits: Auto-labelling: Automatically apply sensitivity labels to Azure Storage based on detection of sensitive information types. Automated Protection: Automatically restrict access to data with specific sensitivity labels, ensuring consistent data protection. Storage Data Owners can selectively enable specific storage accounts for policy enforcement, providing flexibility and control. Like a protection policy that restricted access to data labeled as "Highly Confidential" to only specific groups or users. For instance, blobs labeled with "logs" were accessible only to data analysts. With MIP, the labels are updated based on content changes, and the protection policy can deny access if the content if any “Highly Confidential” data is identified. Enterprise-level Control: Information Protection policies are applied to blobs and resource sets, ensuring that only authorized Azure Entra ID users or M365 user groups can access sensitive data. Unauthorized users will be prevented from reading the blob or resource set. Centralized Policy Management: Create, manage, and enforce protection policies across Azure Storage from a single, unified interface in Microsoft Purview. Enterprise admins have granular control over which storage accounts enforce protection coverage based on the account’s sensitivity label. By using Microsoft Purview Information Protection (MIP) Protection Policies, Contoso was able to achieve secure and consistent data governance, and centralized policy management, effectively addressing their data security challenges Prerequisites Microsoft 365 E5 licenses and setup of pay as you go billing model. To understand pay as you go billing by assets protected, see the pay-as-you-go billing model. For information about the specific licenses required, see this information on sensitivity labels. Microsoft 365 E5 trial licenses can be attained for your tenant by navigating here from your environment. Getting Started The public preview of Protection Policies supports the following Azure Storage services: Azure Blob Storage Azure Data Lake Storage To enable Protection Policies for your Azure Storage accounts: Navigate to the Microsoft Purview portal> Information Protection card > Policies. Configure or use an existing sensitivity label in Microsoft Purview Information Protection that’s scoped to “Files & other data assets” Create an auto-labelling to apply a specific sensitivity label to scoped assets in Azure Storage based on Microsoft out-of-the-box sensitive info types detected. Run scans on assets for auto-labelling to apply. Create a protection policy and associate it with your desired sensitivity labels. Apply the policy to your Azure Blob Storage or ADLS Gen2 accounts. Limitations During the public preview, please note the following limitations: Currently a maximum of 10 storage accounts are supported in one protection policy, and they must be selected under Edit for them to be enabled. Changing pattern rules will re-apply labels on all storage accounts. During the public preview, there might be delays in label synchronization, which could prevent MIP policies from functioning effectively. If customer storage account enables CMK, the storage account MIP policy will not work. Next Steps With the Public Preview, MIP Protection policies is now available in all regions, and any storage account registered on the Microsoft Purview Data Map can create and apply protection policies to implement consistent data governance strategies across their data in Azure Storage. We encourage you to try out this feature and provide feedback. Your input is crucial in shaping this feature as we work towards general availability.
Ayushjuyal
Feb 22, 2025 Place Azure Storage Blog
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auto;\r\n}\r\n.custom_widget_MicrosoftFooter_c-uhff-nav_1w55e_69 {\r\n margin: 0 auto;\r\n max-width: calc(100rem + 10%);\r\n padding: 0 5%;\r\n box-sizing: inherit;\r\n &:before,\r\n &:after {\r\n content: ' ';\r\n display: table;\r\n clear: left;\r\n }\r\n @media only screen and (max-width: 1083px) {\r\n padding-left: 0.75rem;\r\n }\r\n .custom_widget_MicrosoftFooter_c-heading-4_1w55e_97 {\r\n color: #616161;\r\n word-break: break-word;\r\n font-size: 0.9375rem;\r\n line-height: 1.25rem;\r\n padding: 2.25rem 0 0.25rem;\r\n font-weight: 600;\r\n }\r\n .custom_widget_MicrosoftFooter_c-uhff-nav-row_1w55e_113 {\r\n .custom_widget_MicrosoftFooter_c-uhff-nav-group_1w55e_115 {\r\n display: block;\r\n float: left;\r\n min-height: 0.0625rem;\r\n vertical-align: text-top;\r\n padding: 0 0.75rem;\r\n width: 100%;\r\n zoom: 1;\r\n &:first-child {\r\n padding-left: 0;\r\n @media only screen and (max-width: 1083px) {\r\n padding-left: 0.75rem;\r\n }\r\n }\r\n @media only screen and (min-width: 540px) and (max-width: 1082px) {\r\n width: 33.33333%;\r\n }\r\n @media only screen and (min-width: 1083px) {\r\n width: 16.6666666667%;\r\n }\r\n ul.custom_widget_MicrosoftFooter_c-list_1w55e_155.custom_widget_MicrosoftFooter_f-bare_1w55e_155 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n margin-top: 0;\r\n margin-bottom: 0;\r\n padding-left: 0;\r\n list-style-type: none;\r\n li {\r\n word-break: break-word;\r\n padding: 0.5rem 0;\r\n margin: 0;\r\n }\r\n }\r\n }\r\n }\r\n}\r\n.custom_widget_MicrosoftFooter_c-uhff-base_1w55e_187 {\r\n background: #f2f2f2;\r\n margin: 0 auto;\r\n max-width: calc(100rem + 10%);\r\n padding: 1.875rem 5% 1rem;\r\n &:before,\r\n &:after {\r\n content: ' ';\r\n display: table;\r\n }\r\n &:after {\r\n clear: both;\r\n }\r\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_1w55e_213 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n float: left;\r\n margin: 0.1875rem 0;\r\n }\r\n a.custom_widget_MicrosoftFooter_c-uhff-ccpa_1w55e_213:hover {\r\n text-decoration: underline;\r\n }\r\n ul.custom_widget_MicrosoftFooter_c-list_1w55e_155 {\r\n font-size: 0.6875rem;\r\n line-height: 1rem;\r\n float: right;\r\n margin: 0.1875rem 0;\r\n color: #616161;\r\n li {\r\n padding: 0 1.5rem 0.25rem 0;\r\n display: inline-block;\r\n }\r\n }\r\n .custom_widget_MicrosoftFooter_c-list_1w55e_155.custom_widget_MicrosoftFooter_f-bare_1w55e_155 {\r\n padding-left: 0;\r\n list-style-type: none;\r\n }\r\n @media only screen and (max-width: 1083px) {\r\n display: flex;\r\n flex-wrap: wrap;\r\n padding: 1.875rem 1.5rem 1rem;\r\n }\r\n}\r\n.custom_widget_MicrosoftFooter_social-share_1w55e_275 {\r\n position: fixed;\r\n top: 60%;\r\n transform: translateY(-50%);\r\n left: 0;\r\n z-index: 1000;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 {\r\n list-style: none;\r\n padding: 0;\r\n margin: 0;\r\n display: block;\r\n flex-direction: column;\r\n background-color: white;\r\n width: 3.125rem;\r\n border-radius: 0 0.4375rem 0.4375rem 0;\r\n}\r\n.custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311 {\r\n border-top-right-radius: 7px;\r\n}\r\n.custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311:hover {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-email-image_1w55e_325:hover {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333:hover .custom_widget_MicrosoftFooter_linkedin-icon_1w55e_311 {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333:hover .custom_widget_MicrosoftFooter_social-share-email-image_1w55e_325 {\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_social-link-footer_1w55e_333 img {\r\n width: 1.875rem;\r\n height: auto;\r\n transition: filter 0.3s ease;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list_1w55e_359 {\r\n width: 3.125rem;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-rss-image_1w55e_365 {\r\n width: 1.875rem;\r\n height: auto;\r\n transition: filter 0.3s ease;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li {\r\n width: 3.125rem;\r\n height: 3.125rem;\r\n padding: 0.5rem;\r\n box-sizing: border-box;\r\n border: 2px solid white;\r\n display: inline-block;\r\n text-align: center;\r\n opacity: 1;\r\n visibility: visible;\r\n transition: border 0.3s ease; /* Smooth transition effect */\r\n border-left: none;\r\n border-bottom: none; /* Apply bottom border to only last item */\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-linkedin_1w55e_405 {\r\n background-color: #0474b4;\r\n border-top-right-radius: 5px; /* Rounded top right corner of first item*/\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-facebook_1w55e_413 {\r\n background-color: #3c5c9c;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-xicon_1w55e_419 {\r\n background-color: #000;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-reddit_1w55e_425 {\r\n background-color: #fc4404;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-bluesky_1w55e_431 {\r\n background-color: #f0f2f5;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-rss_1w55e_437 {\r\n background-color: #ec7b1c;\r\n}\r\n.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443 {\r\n background-color: #848484;\r\n border-bottom-right-radius: 5px; /* Rounded bottom right corner of last item*/\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443 {\r\n border-bottom: 2px solid white; /* Add bottom border only to the last item */\r\n height: 3.25rem; /* Increase last child height to make in align with the hover label */\r\n}\r\n.custom_widget_MicrosoftFooter_x-icon_1w55e_459 {\r\n filter: invert(100%);\r\n transition: filter 0.3s ease;\r\n width: 1.25rem !important;\r\n height: auto;\r\n padding-top: 0.3125rem !important;\r\n}\r\n.custom_widget_MicrosoftFooter_bluesky-icon_1w55e_473 {\r\n filter: invert(20%) sepia(100%) saturate(3000%) hue-rotate(180deg);\r\n transition: filter 0.3s ease;\r\n padding-top: 0.3125rem !important;\r\n width: 1.5625rem !important;\r\n}\r\n.custom_widget_MicrosoftFooter_share-icon_1w55e_487 {\r\n border: 2px solid transparent;\r\n display: inline-block;\r\n position: relative;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li:hover {\r\n border: 2px solid white;\r\n border-left: none;\r\n border-bottom: none;\r\n border-radius: 0;\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li.custom_widget_MicrosoftFooter_social-share-list-mail_1w55e_443:hover {\r\n border-bottom: 2px solid white; /* Add bottom border only to the last item */\r\n}\r\n.custom_widget_MicrosoftFooter_sharing-options_1w55e_291 li:hover .custom_widget_MicrosoftFooter_label_1w55e_519 {\r\n opacity: 1;\r\n visibility: visible;\r\n border: 2px solid white;\r\n box-sizing: border-box;\r\n border-left: 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[Public Preview] Accelerating BlobNFS throughput & scale with FUSE for superior performance","conversation":{"__ref":"Conversation:conversation:4426147"},"id":"message:4426147","revisionNum":6,"uid":4426147,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:446007"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":239},"postTime":"2025-07-03T02:55:24.492-07:00","lastPublishTime":"2025-07-03T02:55:24.492-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure Blob Storage can be mounted and accessed like a local file system using BlobFuse, which is a FUSE-based driver for the Blob REST API. Customers choose BlobFuse for AI/ML, HPC, analytics and backup workloads. It provides exceptionally high throughput along with benefits like local caching and security integration via Azure Entra ID. \n For customers requiring NFS 3.0 protocol support or POSIX compliance, Azure Blob Storage also natively supports NFSv3 (aka BlobNFS). It enables Azure Blob storage access for customers’ legacy applications without requiring changes. BlobNFS is accessed via the Linux NFS client combined with our AZNFS mount helper package, which streamlines mounting and reliably connecting to Blob Storage’s NFS endpoints. Please note that BlobNFS only supports access over a virtual network since Azure Entra ID based auth isn’t yet available on NFS 3.0. \n Today, we’re excited to announce an update to AZNFS (3.0) for BlobNFS, which now uses the same libfuse3 library that powers BlobFuse bringing significant improvements in performance and scale. The updated AZNFS for BlobNFS delivers significantly higher throughput, larger file support, better metadata performance, and removes user group limits, enhancing performance for demanding workloads. \n \n Maximize Virtual Machine throughput: AZNFS now supports up to 256 TCP connections (up from16 in native NFS client) allowing throughput to reach VM NIC bandwidth (the maximum data transfer rate of virtual machine’s network interface card) or storage account limits. This benefits HPC workloads by ensuring high throughput for large dataset operations. Additionally, a small number (4 or fewer) of parallel file reads/writes can now fully saturate the VM NIC bandwidth even for larger VM sizes. \n Enhanced read/write speed: The updated AZNFS client outperforms native NFS client for read and write scenarios. For example, single file read/write performance is improved by a factor of 5x and 3x respectively, which can be useful for large database backup tasks requiring high single file throughput for writing and reading backup files. Refer to the link for a detailed performance comparison. \n Removal of the user's group limit: Linux NFS clients with a local Identity server can pass access permissions for up to 16 groups of a user, restricting resource access for users belonging to more than 16 groups. This update allows FUSE to handle permission checks, removing the 16-group limitation. \n Improved metadata query performance: READDIR can query more directory entries in one call. The Linux client has a limit of 1MB, whereas the updated AZNFS can now reach up to 3 MB. Customers with numerous files will experience quicker listing and metadata operations with reduced latency. This will be beneficial for workloads like EDA (Electronic Design Automation) and HPC (High Performance Computing) which often involve reading metadata for considerable number of files before selecting a subset for processing. \n Support for large file sizes (up to 5TB): The new release can support larger file sizes for sequential write patterns. Due to larger block sizes possible with AZNFS, users can create larger files up to the 5TB limit. With Linux clients, under best conditions, the max. file sizes were limited to ~3TB. CAD tools producing simulation and checkpoint data files over 3TB will benefit from this improvement. \n \n The following charts compare performance between updated AZNFS and the Native Linux client. Please refer the detailed benchmarks for more details. \n [Test parameters - VM: Standard D96ds v5, File size: 100GB, Linux NFS is with nconnect =16, Linux kernel 5.x.x ; Test used: DD test] \n Note: The VM supports higher read throughput than write throughput. For updated AZNFS, throughput starting from 4 parallel file read/write operations is constrained by VM NIC bandwidth, or it can scale higher. \n \n \n \n Getting Started \n Please register for the preview using this form. \n Please refer the link for instructions on how to install and use latest version of AZNFS. For any queries or feedback, please contact us at aznfs@microsoft.com. \n \n References: \n What is BlobFuse? - BlobFuse2 - Azure Storage | Microsoft Learn \n Network File System (NFS) 3.0 protocol support for Azure Blob Storage \n Mount Blob Storage by using the Network File System (NFS) 3.0 protocol on Linux \n Instructions to install and use latest version of AZNFS · Azure/AZNFS-mount Wiki 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a Scalable Web Crawling and Indexing Pipeline with Azure storage and AI Search","conversation":{"__ref":"Conversation:conversation:4295042"},"id":"message:4295042","revisionNum":28,"uid":4295042,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:2733696"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In the ever-evolving world of data management, keeping search indexes up-to-date with dynamic data can be challenging. Traditional approaches, such as manual or scheduled indexing, are resource-intensive, delay-prone, and difficult to scale. Azure Blob Trigger combined with an AI Search Indexer offers a cutting-edge solution to overcome these challenges, enabling real-time, scalable, and enriched data indexing. \n This blog explores how Blob Trigger, integrated with Azure Cognitive Search, transforms the indexing process by automating workflows and enriching data with AI capabilities. It highlights the step-by-step process of configuring Blob Storage, creating Azure Functions for triggers, and seamlessly connecting with an AI-powered search index. The approach leverages Azure's event-driven architecture, ensuring efficient and cost-effective data management. ","introduction":"This blog post covers a customer implementation of knowledge base ingestion into Azure Blob Storage, and an AI Search based indexing pipeline along with links to the source code. A future post will cover how the curated knowledge base and search index are combined into a Retrieval Augmented Generation (RAG) template for natural-language based enterprise knowledge retrieval and response. ","metrics":{"__typename":"MessageMetrics","views":1325},"postTime":"2025-04-17T07:00:00.013-07:00","lastPublishTime":"2025-04-17T07:00:00.013-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" A leading telecommunications sector customer in Europe was facing challenges with the existing search functionality on their public website. End users could search for information, but the results were often scattered across multiple links, lacked summarization, and did not consistently provide relevant or accurate results. This inefficiency resulted in poor user experience, making it difficult for customers to quickly find the required information. Additionally, the organization's knowledge assets were spread across multiple sources, including public websites, On-premises SharePoint repositories and specific internal locations containing various document types such as PDFs, Word documents, and text files. The lack of a centralized search mechanism hindered efficient information retrieval and prevented the organization from leveraging its data effectively. To address these challenges, we developed an advanced search solution built upon knowledge base data ingestion and consolidation into Azure Storage, and automated blob-trigger based indexing and vectorization with Azure AI Search. \n Implementation of the search solution \n Data Ingestion: \n Data Ingestion involves collecting structured and unstructured data from multiple sources, including the public website, on-premises SharePoint, and designated internal locations. The following diagram explains how the different types of data (PDF/ text / HTML) are scraped from public websites using an Azure Function app and written to Blob storage. \n \n \n Data Processing & Indexing: \n Involves transforming, generating vector embeddings and indexing the data ingested into Azure Storage to enhance searchability, while ensuring that the most relevant and up to date results appear first. For this purpose, we built an AI Search Indexer Pipeline integrated with Azure Blob storage. The pipeline runs with automated blob triggers, to ensure updates to the ingested knowledge base trigger the pipeline for index freshness and accuracy. \n \n Figure: AI Search Indexer - Data processing \n AI Search Indexer Pipeline integration with Azure Storage \n Azure Blob Storage provides a scalable and cost-effective storage platform to manage unstructured data ingested from the web-crawler. Blob Storage integrates with Azure AI Search - a powerful cognitive search service that transforms unstructured data into searchable content. Indexer pipeline leverages a blob-triggered Azure Function to update an Azure AI Search indexer with a skillset—enabling the processing of diverse file types stored in Azure Blob Storage. This implementation choice enables the following outcomes: \n ✔ Triggering an Azure Function whenever a new file is uploaded, modified, or deleted in Blob Storage. ✔ Updating the Azure AI Search indexer dynamically so the latest data is always available for AI-driven search and retrieval. ✔ Applying cognitive skills (OCR, entity recognition, translation, etc.) to extract valuable insights from unstructured files. \n \n Figure: Data processing and indexing workflow \n Solution Details - Key Components \n Web Crawler with Scrapy \n \n Scrapy, a popular Python web crawling framework, is used to extract unstructured data from public websites. \n \n \n Configurable spiders allow users to customize what data is scraped and how it's stored in Azure blob storage. \n \n Azure Blob Storage account \n \n Acts as the primary storage for the crawled unstructured data. \n \n \n Ensures scalability, secure storage and access of unstructured data. \n \n Blob Trigger with Azure Functions \n \n Monitors changes in the data stored in Blob (create, update, delete). \n \n \n Triggers an action to re-index the data in Azure AI Search whenever a change is detected. \n \n Azure AI Search Indexer \n \n Indexes the crawled data to make it searchable. \n \n \n Automatically syncs with the Blob data source, ensuring up-to-date content in the search index. \n \n \n Step-by-Step Implementation  \n \n \n Data ingestion: Az function for ingestion which crawls public website \n \n Crawling: Set up an Azure Function for periodic crawling based on the starting page. Parse different file types (PDF / Word / HTML / Text) and place them in an Azure Blob Storage account for further indexing by AI Search. \n Code Example: Function APP \n \n \n \n \n \n \n Data Processing: Setting up Blob Trigger for Crawled data \n \n \n \n \n Configuration: Set up a blob trigger within the AI Search indexer to fetch the data from Blob storage to a search index. Whenever a new file is created in Blob storage, the trigger will fire to update the index. \n Data Indexing: Create AI Search Index, Indexer, Data Source & Skillset \n Create an index and schema. The index serves as the schema for the documents that Azure AI Search processes. This schema includes the fields you want to make searchable, such as title, content, and metadata. Create a data source connecting the Blob Storage account with the index, create skillset for different skill types – for example, text extraction, language detection, OCR, or entity recognition. Configure the indexer schedule to run on-demand or on a schedule. \n Code Example for creating Index, Indexer, Skillset, Data source: Index Code, Data Source, Indexer \n The configuration of the blob-trigger based indexer, including connection to the Blob Storage account along with indexer skillsets to extract relevant information and finally, the use of AI Search’s built-in Azure OpenAI embedding skill to generate and store vector embeddings in AI Search, concludes the final piece of the indexing pipeline. \n \n \n \n \n \n Key Benefits \n This solution streamlines data ingestion and indexing by automating the process through an Azure Function with Blob triggers. Unlike the conventional approaches, which require manually connecting each service and uploading data to the index, this method significantly reduces effort while enabling data and index freshness. \n Conventional vs. AI Search Indexer with Blob Trigger Approach \n \n Feature \n \n Conventional Azure Function Approach \n \n AI Search Indexer / Blob Trigger based Approach \n \n Architecture Complexity \n \n High, with multiple Azure Functions and API calls \n \n Low, with integrated data source and skillset in Azure AI Search \n \n Document Processing \n \n Manual triggering with custom error handling \n \n Automated with skillset-based processing and error handling \n \n Scalability \n \n Limited by Azure Function execution times and scaling rules \n \n Highly scalable with built-in support in Azure AI Search \n \n Skillset and Enrichment \n \n Requires external API calls to OpenAI \n \n Built-in skillset in Azure AI Search \n \n Code Maintenance \n \n High, due to separate workflows for OpenAI and Search AI calls \n \n Minimal, with configuration-driven skillset and indexing \n \n Cost Efficiency \n \n Potentially high due to multiple API calls \n \n Cost-effective with a single integrated indexing and enrichment flow \n \n More specifically, the solution delivers the following outcomes: \n \n Real-Time Indexing and Updates \n \n The automated pipeline efficiently crawls public websites, extracts content, and stores it in Azure Blob Storage. Any modifications to the data source trigger updates in Azure AI Search, ensuring real-time indexing. This enables seamless content discovery and retrieval with minimal latency. \n \n \n Enhanced Security & Resilience \n \n This solution enhances security and resilience by operating entirely within Azure’s internal services, eliminating the need for external function app calls. By leveraging Azure’s built-in security controls, it minimizes exposure to external threats while ensuring high availability. \n \n \n Cost-Efficient & Scalable \n \n The solution is designed to optimize costs while providing scalability. Azure AI Search’s pay-as-you-go pricing ensures cost-effectiveness, and the architecture supports automatic scaling to handle varying workloads without manual intervention. \n \n \n Simplified Configuration and Management \n \n With a publicly available GitHub repository containing the necessary code for Azure AI Search and deployment pipelines, this solution is easy to configure and deploy. It is ideal for organizations managing dynamic content, as it reduces operational complexity and accelerates production time. \n \n \n Built-in AI-Powered Enrichment \n \n The indexing pipeline incorporates AI-powered enrichment capabilities, such as OCR, entity recognition, language detection, sentiment analysis and even the Azure OpenAI embedding skill. These skills simplify implementation and enable deeper insights into the knowledge base. \n \n \n \n Conclusion \n Using a Blob Trigger with an AI Search Indexer is a modern, efficient, and scalable approach to dynamically index data. It eliminates the limitations of traditional indexing methods by automating data ingestion, enriching content with AI capabilities, and ensuring secure, cost-effective, and resilient operations. This approach is particularly suited for scenarios with dynamic or high-volume data requirements, offering significant advantages to storage admins and developers alike \n \n Links & Reference for detailed implementation \n \n Indexer overview - Azure AI Search | Microsoft Learn \n RAG tutorial: Build an indexing pipeline - Azure AI Search | Microsoft Learn \n \n \n GitHub Repository Details: The complete implementation can be found in the GitHub repository. Key files include: \n \n \n \n \n spider.py: Defines Scrapy spiders for web crawling used within Az function. \n \n \n \n \n \n \n azure_function.py: Azure Function push data to Blob Trigger. \n \n \n \n \n \n \n indexer.py, datasource.py, skillset.py: Python scripts for defining the Azure Cognitive Search index, data source, and skillset. \n \n \n \n \n \n \n Bicep/main/main. Json: Infrastructure as Code (IaC) template for deploying the entire solution. \n \n \n \n \n \n \n Instruction Manual: This consists of complete instructions end to end on Infra resources created via Bicep template. Then AZ function deployment and then setting up the Azure AI search indexer. \n \n \n \n  #AzureSearch #CognitiveSearch #AIIndexer #AzureBlobStorage #DocumentIndexing #AIIntegration #Azure Storage #Storage 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Data Management: Azure Storage Actions Overview","conversation":{"__ref":"Conversation:conversation:4409649"},"id":"message:4409649","revisionNum":1,"uid":4409649,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:1064936"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"A fully managed platform that helps you automate data management tasks.Process billions of blobs in your storage account effortlessly. Supports Azure Blob Storage and Azure Data Lake Storage","metrics":{"__typename":"MessageMetrics","views":329},"postTime":"2025-04-30T06:17:15.838-07:00","lastPublishTime":"2025-04-30T06:17:15.838-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure Storage Data Management Solutions/Services: \n 1. Movement: An end-to-end experience to discover, plan and move data into Azure in a performant, cost effective, secure, and reliable way \n 2. Insights: Store and manage metrics and metadata enabling deep insights into the data estate \n 3. Actions: Flexible, scalable, serverless platform to effortlessly process data for data management, protection, security and governance \n Big Data Challenges: \n Organizations must manage ever-increasing data volumes \n \n Data Management \n Data Movement \n Tagging & Classification \n Security and Access Control \n Data Protection \n Orchestration \n \n Customer challenges: \n \n Customers have increasingly large volumes of data in hundreds of storage accounts with billions of objects \n Challenging to process millions of objects for bulk operations \n Lifecycle management, data protection, object tagging and security operations require increasing complexity \n Out of box policies in storage can be constricted and extensibility is limited \n \n Introducing Azure Storage Actions \n A fully managed platform that helps you automate data management tasks.Process billions of blobs in your storage account effortlessly. Supports Azure Blob Storage and Azure Data Lake Storage \n How Storage Actions Works? \n Event-Condition-Action framework \n \n Schedule-based and on-demand execution \n Conditional processing of blobs based on blob properties \n Use native blob operations as actions on the blob \n \n Serverless \n \n Fully managed infrastructure \n Deploy in minutes – eliminates need for any complex software or infrastructure \n Auto-scales with your storage \n \n No-code composition & simplified management \n \n Use clicks to compose tasks \n Easily apply tasks to multiple storage accounts \n Monitor task execution across your storage execution with aggregate metrics and drilldowns \n \n Storage Actions Overview \n \n Data Protection – blob immutability, legal holds and blob expiry \n Cost optimization – tiering or deleting blobs \n Managing blob tags \n Undelete blobs \n Copy blobs, folder operations \n \n Key Concepts \n \n How to start with Storage Actions: \n Login to Portal: Search for Azure Storage Actions \n Create a task à Define Conditions: \n \n [[and(endsWith(Name, 'pdf'), equals(BlobType, 'BlockBlob'))]] \n The query [[and(endsWith(Name, 'pdf'), equals(BlobType, 'BlockBlob'))]] is a logical expression used to filter and retrieve specific items from a dataset. Here's a breakdown of its components: \n \n endsWith(Name, 'pdf'): This part of the query checks if the Name attribute of an item ends with the string 'pdf'. Essentially, it filters items whose names end with .pdf, indicating that they are PDF files. \n equals(BlobType, 'BlockBlob'): This part of the query checks if the BlobType attribute of an item is equal to 'BlockBlob'. This is used to filter items that are of the type BlockBlob, which is a type of storage blob in cloud storage systems. \n and(...): The and operator combines the two conditions above. It ensures that only items meeting both criteria are retrieved. In other words, the query will return items that are PDF files (Name ends with .pdf) and are of the type BlockBlob. \n \n In summary, this query is used to find items that are PDF files stored as BlockBlob in a dataset. \n If above query verified, it would set tags process = true, and set blob immutability policy to locked \n Create Assignment: \n \n Once task will run it will create report which will get added in Storage account container \n \n Example Use Cases \n \n Retention Management: Automatically manage the retention and expiry durations of audio files using a combination of index tags and creation times 4. \n Version History Management: Manage the retention and lifecycle of datasets using metadata and tags for optimal protection and cost 4. \n One-off Processing: Define tasks to rehydrate large datasets from the archive tier, reset tags on part of a dataset, or clean-up redundant and outdated datasets \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4137","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LW1uV2xPbw?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LUVXWmlJMg?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LURYRjNmeg?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LVZtVXRZeg?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LXEweERvTg?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LXNyODVkZA?revision=1\"}"}}],"totalCount":6,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA5NjQ5LW1uV2xPbw?revision=1"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401933":{"__typename":"Conversation","id":"conversation:4401933","topic":{"__typename":"BlogTopicMessage","uid":4401933},"lastPostingActivityTime":"2025-04-28T07:58:24.748-07:00","solved":false},"User:user:213849":{"__typename":"User","uid":213849,"login":"dukicn","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-2.svg?time=0"},"id":"user:213849"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLThRUFRycw?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLThRUFRycw?revision=7","title":"1200x627_Digital-Evidence-Hybrid-Cloud_Blog Preview.jpg","associationType":"COVER","width":1200,"height":627,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLTFMbjBxYg?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLTFMbjBxYg?revision=7","title":"Tiger-Bridge-Hybrid-Cloud.png","associationType":"BODY","width":1520,"height":1260,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLXRDeFJKQQ?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLXRDeFJKQQ?revision=7","title":"1200x627_How-it-Works-Bridge_diagram.jpg","associationType":"BODY","width":1200,"height":627,"altText":""},"BlogTopicMessage:message:4401933":{"__typename":"BlogTopicMessage","subject":"Holding forensic evidence: The role of hybrid cloud in successful preservation and compliance","conversation":{"__ref":"Conversation:conversation:4401933"},"id":"message:4401933","revisionNum":7,"uid":4401933,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:213849"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Supporting digital forensics with Azure Blob Storage and Tiger Bridge File Management","metrics":{"__typename":"MessageMetrics","views":244},"postTime":"2025-04-28T07:58:24.748-07:00","lastPublishTime":"2025-04-28T07:58:24.748-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Disclaimer: The following is a post authored by our partner Tiger Technology. Tiger Technology has been a valued partner in the Azure Storage ecosystem for many years and we are happy to have them share details on their innovative solution! \n \n Police departments worldwide are grappling with a digital explosion. From body camera footage to social media captures, the volume and variety of evidence have surged, creating a storage, and management challenge like never before. A single police department needing to store 2–5 petabytes of data—and keep some of it for 100 years. \n How can they preserve the integrity of this data, make it cost-effective, and ensure compliance with legal requirements? The answer lies in hybrid cloud solutions, specifically Microsoft Azure Blob Storage paired with Tiger Bridge. These solutions are empowering law enforcement to manage, and store evidence at scale, without disrupting workflows. \n But what exactly is hybrid cloud, and why is it a game-changer for digital evidence management? \n What is a hybrid cloud? \n A hybrid cloud combines public or private cloud services with on-premises infrastructure. It gives organizations the flexibility to mix, and match environments, allowing them to choose the best fit for specific applications, and data. This flexibility is especially valuable in highly regulated industries like law enforcement, where strict data privacy, and compliance rules govern how evidence is stored, processed, and accessed. \n Hybrid cloud also facilitates a smoother transition to public cloud solutions. For instance, when a data center reaches capacity, hybrid setups allow agencies to scale dynamically while maintaining control over their most sensitive data. It’s not just about storage—it's about creating a robust, compliant infrastructure for managing enormous volumes of evidence. \n What makes digital evidence so complex? \n Digital evidence encompasses any information stored or transmitted in binary form that can be used in court. It includes computer hard drives, phone records, social media posts, surveillance footage, etc. \n The challenge isn’t just collecting this data—it’s preserving its integrity. Forensic investigators must adhere to strict chain-of-custody protocols to prove in court that the evidence: \n \n Is authentic and unaltered, \n Has been securely stored with limited access, \n Is readily available when needed. \n \n With the surge in data volumes and complexity, traditional storage systems often fall short. That’s where hybrid cloud solutions shine, offering scalable, secure, and cost-effective options that keep digital evidence admissible while meeting compliance standards. \n The challenges police departments face \n Digital evidence is invaluable. Storing and managing it is a challenging task, and requires dealing with several aspects: \n \n Short-term storage problems \n The sheer scale of data can overwhelm local systems. Evidence must first be duplicated using forensic imaging to protect the original file. But housing these duplicates, especially with limited budgets, strains existing resources. \n \n Long-term retention demands \n In some jurisdictions, evidence must be retained for decades—sometimes up to a century. Physical storage media, like hard drives or SSDs, degrade over time and are expensive to maintain. Transitioning this data to cloud cold storage offers a more durable and cost-effective solution. \n \n Data integrity and legal admissibility\n Even the slightest suspicion of tampering can render evidence inadmissible. Courts require robust proof of authenticity and integrity, including cryptographic hashes and digital timestamps. Failing to maintain a clear chain of custody could jeopardize critical cases. \n \n \n Solving the storage puzzle with hybrid cloud \n For law enforcement agencies, managing sensitive evidence isn't just about storage—it's about creating a system that safeguards data integrity, ensures compliance, and keeps costs under control. Traditional methods fall short in meeting these demands as the volume of digital evidence continues to grow. \n This is where hybrid cloud technology stands out, offering a powerful combination of on-premises infrastructure and cloud capabilities. Microsoft Azure, a leader in cloud solutions, brings critical features to the table, ensuring evidence remains secure, accessible, and compliant with strict legal standards. \n But storage alone isn't enough. Efficient file management is equally crucial for managing vast datasets while maintaining workflow efficiency. Tools like Tiger Bridge complement Microsoft Azure by bridging the gap between local and cloud storage, adding intelligence and flexibility to how evidence is preserved and accessed. \n \n Microsoft Azure Blob Storage \n Azure Blob Storage is massively scalable and secure object storage. For the purposes of law enforcement, among other features, it offers: \n \n Automatic Tiering: Automatically moves data between hot and cold tiers, optimizing costs, \n Durability: Up to sixteen 9s (99.99999999999999%) of durability ensures data integrity for decades. \n Metadata management: Add custom tags or blob indexes, such as police case classifications, to automate retention reviews. \n \n Microsoft Azure ensures evidence is secure, accessible, and compliant with legal standards. \n Tiger Bridge: Smart File Management \n Tiger Bridge enhances Microsoft Azure’s capabilities by seamlessly integrating local and cloud storage with powerful features tailored for forensic evidence management. \n Tiger Bridge is a software-only solution that integrates seamlessly with Windows servers. It handles file replication, space reclaiming, and archiving—all while preserving existing workflows and ensuring data integrity and disaster recovery. \n With Tiger Bridge, police departments can transition to hybrid cloud storage without adding hardware or altering processes. \n \n Data replication Tiger Bridge replicates files from on-premises storage to cloud storage, ensuring a secure backup. Replication policies run transparently in the background, allowing investigators to work uninterrupted. Files are duplicated based on user-defined criteria, such as priority cases or evidence retention timelines. \n \n Space reclamation \n Once files are replicated to the cloud, Tiger Bridge replaces local copies with “nearline” stubs. These stubs look like the original files but take up virtually no space. When a file is needed, it’s automatically retrieved from the cloud, reducing storage strain on local servers. \n \n Data archiving For long-term storage, Tiger Bridge moves files from hot cloud tiers to cold and / or archive storage. Files in the archive tier are replaced with \"offline\" stubs. These files are not immediately accessible but can be manually retrieved and rehydrated when necessary. This capability allows law enforcement agencies to save on costs while still preserving access to critical evidence. \n \n Checksum for data integrity \n On top of strong data integrity and data protection features already built-in in Azure Storage Blob service, Tiger Bridge goes a step further in ensuring data integrity by generating checksums for newly replicated files. These cryptographic signatures allow agencies to verify that files in the cloud are identical to the originals stored on premises. This feature is essential for forensic applications, where the authenticity of evidence must withstand courtroom scrutiny. Data integrity verification is done during uploads and retrievals, ensuring that files remain unaltered while stored in the cloud. For law enforcement, checksum validation provides peace of mind, ensuring that evidence remains admissible in court and meets strict regulatory requirements \n \n Disaster Recovery In the event of a local system failure, Tiger Bridge allows for immediate recovery. All data remains accessible in the cloud, and reinstalling Tiger Bridge on a new server re-establishes access without needing to re-download files. \n A real-life scenario \n Imagine a police department dealing with petabytes of video evidence from body cameras, surveillance footage, and digital device extractions. A simple, yet effective typical real-life scenario follows the similar patterns: \n \n Investigators collect and image evidence files, \n Tiger Bridge replicates this data to Azure Blob Storage, following predefined rules, \n Active cases remain in Azure’s hot tier, while archival data moves to cost-effective cold storage, \n Metadata tags in Azure help automate case retention reviews, flagging files eligible for deletion. \n \n This approach ensures evidence is accessible when needed, secure from tampering, and affordable to store long-term. \n \n \n The results speak for themselves. Adopting a hybrid cloud strategy delivers tangible benefits: \n \n Operational efficiency: Evidence is readily accessible without the need for extensive hardware investments and maintenance. \n Cost savings: Automating data tiering reduces storage costs while maintaining accessibility. \n Workflow continuity: Investigators can maintain existing processes with minimal disruption. \n Enhanced compliance: Robust security measures and chain-of-custody tracking ensure legal standards are met. \n \n A future-proof solution for digital forensics \n As digital evidence grows in both volume and importance, police organizations must evolve their storage strategies. Hybrid cloud solutions like Azure Blob Storage and Tiger Bridge offer a path forward: scalable, secure, and cost-effective evidence management designed for the demands of modern law enforcement. \n The choice is clear: Preserve the integrity of justice by adopting tools built for the future. \n About Tiger Technology \n Tiger Technology helps organizations with mission-critical deployments optimize their on-premises storage and enhance their workflows through cloud services. The company is a validated ISV partner for Microsoft in three out of five Azure Storage categories: Primary and Secondary Storage; Archive, Backup and BCDR, and Data Governance, Management, and Migration. \n \n Tiger Bridge SaaS offering on Azure Marketplace is an Azure benefit-eligible, data management software enabling seamless hybrid cloud infrastructure. Installed in the customer’s on-premises or cloud environment, Tiger Bridge intelligently connects file data across file and object storage anywhere for data lifecycle management, global file access, Disaster Recovery, data migration and access to insights. Tiger Bridge supports all Azure Blob Storage tiers, including cold and archive tiers for long-term archival of data. \n \n Read more by Tiger Technology on the Tech Community Blog: \n \n Modernization through Tiger Bridge Hybrid Cloud Data Services \n On-premises-first hybrid workflows in healthcare. Why start with digital pathology? \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"10917","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLThRUFRycw?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLTFMbjBxYg?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLXRDeFJKQQ?revision=7\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDAxOTMzLThRUFRycw?revision=7"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4404549":{"__typename":"Conversation","id":"conversation:4404549","topic":{"__typename":"BlogTopicMessage","uid":4404549},"lastPostingActivityTime":"2025-04-16T03:07:29.634-07:00","solved":false},"User:user:2783987":{"__typename":"User","uid":2783987,"login":"Anusha_Subramanian","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-4.svg?time=0"},"id":"user:2783987"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LVBicjVIeA?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LVBicjVIeA?revision=4","title":"download.png","associationType":"COVER","width":1332,"height":1080,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LWtEVkx4Qg?revision=4\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LWtEVkx4Qg?revision=4","title":"clipboard_image-1-1744742054381.png","associationType":"BODY","width":1375,"height":704,"altText":""},"BlogTopicMessage:message:4404549":{"__typename":"BlogTopicMessage","subject":"Announcing General Availability of Next generation Azure Data Box Devices","conversation":{"__ref":"Conversation:conversation:4404549"},"id":"message:4404549","revisionNum":4,"uid":4404549,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:2783987"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":641},"postTime":"2025-04-16T03:07:29.634-07:00","lastPublishTime":"2025-04-16T03:07:29.634-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Today, we’re excited to announce the General Availability of Azure Data Box 120 and Azure Data Box 525, our next-generation compact, NVMe-based Data Box devices. These devices are currently available for customers to order in the US, US Gov, Canada, EU and the UK Azure regions, with broader availability coming soon. Since the preview announcement at Ignite '24, we have successfully ingested petabytes of data, encompassing multiple orders serving customers across various industry verticals. Customers have expressed delight over the reliability and efficiency of the new devices with up to 10x improvement in data transfer rates, highlighting them as a valuable and essential asset for large-scale data migration projects. \n These new device offerings reflect insights gained from working with our customers over the years and understanding their evolving data transfer needs. They incorporate several improvements to accelerate offline data transfers to Azure, including: \n \n Fast copy - Built with NVMe drives for high-speed transfers and improved reliability and support for faster network connections \n \n \n Ease of use - larger capacity offering (525 TB) in a compact form-factor for easy handling \n \n \n Resilient - Ruggedized devices built to withstand rough conditions during transport \n \n \n Secure - Enhanced physical, hardware and software security features \n \n \n Broader availability – Presence planned in more Azure regions, meeting local compliance standards and regulations \n \n What’s new? \n Improved Speed & Efficiency \n \n NVMe-based devices offer faster data transfer rates, providing a 10x improvement in data transfer speeds to the device as compared to previous generation devices. With a dataset comprised of mostly large (TB-sized) files, on average half a petabyte can be copied to the device in under two days. \n \n \n High-speed transfers to Azure with data upload up to 5x faster for medium to large files, reducing the lead time for your data to become accessible in the Azure cloud. \n \n \n Improved networking with support for up to 100 GbE connections, as compared to 10 GbE on the older generation of devices. \n \n \n Two options with usable capacity of 120 TB and 525 TB in a compact form factor meeting OSHA requirements. Devices ship the next day air in most regions. \n \n Learn more about the performance improvements on Data Box 120 and Data Box 525. \n Enhanced Security \n The new devices come with several new physical, hardware and software security enhancements. This is in addition to the built in Azure security baseline for Data Box and Data Box service security measures currently supported by the service. \n \n Secure boot functionality with hardware root of trust and Trusted Platform Module (TPM) 2.0. \n \n \n Custom tamper-proof screws and built-in intrusion detection system to detect unauthorized device access. \n \n \n AES 256-bit BitLocker software encryption for data at rest is currently available. Hardware encryption via the RAID controller, which will be enabled by default on these devices, is coming soon. Furthermore, once available, customers can enable double encryption through both software and hardware encryption to meet their sensitive data transfer requirements. \n \n \n These ISTA 6A compliant devices are built to withstand rough conditions during shipment while keeping both the device and your data safe and intact. \n \n Learn more about the enhanced security features on Data Box 120 and Data Box 525. \n Broader Azure region coverage \n A recurring request from our customers has been wider regional availability of higher-capacity devices to accelerate large migrations. We’re happy to share that Azure Data Box 525 will be available across US, US Gov, EU, UK and Canada with broader presence in EMEA and APAC regions coming soon. This marks a significant improvement in the availability of a large-capacity device as compared to the current Data Box Heavy which is available only in the US and Europe. \n What our customers have to say \n For the last several months, we’ve been working directly with our customers of all industries and sizes to leverage the next generation devices for their data migration needs. Customers love the larger capacity with form-factor familiarity, seamless set up and faster copy. \n “We utilized Azure Data Box for a bulk migration of Unix archive data. The data, originating from IBM Spectrum Protect, underwent pre-processing before being transferred to Azure blobs via the NFS v4 protocol. This offline migration solution enabled us to efficiently manage our large-scale data transfer needs, ensuring a seamless transition to the Azure cloud. Azure Data Box proved to be an indispensable tool in handling our specialized migration scenario, offering a reliable and efficient method for data transfer.” – ST Microelectronics Backup & Storage team \n \n “This new offering brings significant advantages, particularly by simplifying our internal processes. With deployments ranging from hundreds of terabytes to even petabytes, we previously relied on multiple regular Data Box devices—or occasionally Data Box Heavy devices—which required extensive operational effort. The new solution offers sizes better aligned with our needs, allowing us to achieve optimal results with fewer logistical steps. Additionally, the latest generation is faster and provides more connectivity options at data centre premises, enhancing both efficiency and flexibility for large-scale data transfers.” - Lukasz Konarzewski, Senior Data Architect, Commvault \n \n “We have had a positive experience overall with the new Data Box devices to move our data to Azure Blob storage. The devices offer easy plug and play installation, detailed documentation especially for the security features and good data copy performance. We would definitely consider using it again for future large data migration projects.” – Bas Boeijink, Cloud Engineer, Eurofiber Cloud Infra \n Upcoming changes to older SKUs availability \n Note that in regions where the next-gen devices are available, new orders for Data Box 80 TB and Data Box Heavy devices cannot be placed post May 31, 2025. We will however continue to process and support all existing orders. \n Order your device today! \n The devices are currently available for customers to order in the US, Canada, EU, UK, and US Gov Azure regions. We will continue to expand to more regions in the upcoming months. Azure Data Box provides customers with one of the most cost-effective solutions for data migration, offering competitive pricing with the lowest cost per TB among offline data transfer solutions. You can learn more about the pricing across various regions by visiting our pricing page. You can use the Azure portal to select the requisite SKU suitable for your migration needs and place the order. \n \n \n \n Learn more about the all-new Data Box devices here. We are committed to continuing to deliver innovative solutions to lower the barrier for bringing data to Azure. Your feedback is important to us. Tell us what you think about the new Azure Data Box devices by writing to us at DataBoxPM@microsoft.com – we can’t wait to hear from you. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7445","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LVBicjVIeA?revision=4\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LWtEVkx4Qg?revision=4\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00NDA0NTQ5LVBicjVIeA?revision=4"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4402747":{"__typename":"Conversation","id":"conversation:4402747","topic":{"__typename":"BlogTopicMessage","uid":4402747},"lastPostingActivityTime":"2025-04-10T05:22:49.416-07:00","solved":false},"User:user:969110":{"__typename":"User","uid":969110,"login":"JeevanManoj","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS05NjkxMTAtRU10ZkVP?image-coordinates=157%2C677%2C2572%2C3093"},"id":"user:969110"},"BlogTopicMessage:message:4402747":{"__typename":"BlogTopicMessage","subject":"Azure Blob Storage SFTP: General Availability of ACLs (Access Control Lists) of local users","conversation":{"__ref":"Conversation:conversation:4402747"},"id":"message:4402747","revisionNum":3,"uid":4402747,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:969110"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":1629},"postTime":"2025-04-10T00:41:48.882-07:00","lastPublishTime":"2025-04-10T00:41:48.882-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" We are excited to announce the general availability of ACLs (Access Control Lists) for Azure Blob Storage SFTP local users. ACLs make it simple and intuitive for administrators to manage fine-grained access control to blobs and directories for Azure Blob Storage SFTP local users. \n Azure Blob Storage SFTP \n Azure Blob storage supports the SSH File Transfer Protocol (SFTP) natively. SFTP on Azure Blob Storage lets you securely connect to and interact with the contents of your storage account by using an SFTP client, allowing you to use SFTP for file access, file transfer, and file management. Learn more here. \n Azure Blob Storage SFTP is used by a significant number of our customers, who have shared overwhelmingly positive feedback. It eliminates the need for third-party or custom SFTP solutions involving cumbersome maintenance steps such as VM orchestration. \n Local users \n Azure Blob Storage SFTP utilizes a new form of identity management called local users. Local users must use either a password or a Secure Shell (SSH) private key credential for authentication. You can have a maximum of 25,000 local users for a storage account. Learn more about local users here. \n Access Control for local users \n There are two ways in which access control can be attained for local users. \n 1. Container permissions \n By using container permissions, you can choose which containers you want to grant access to and what level of access you want to provide (Read, Write, List, Delete, Create, Modify Ownership, and Modify Permissions). Those permissions apply to all directories and subdirectories in the container. Learn more here. \n 2. ACLs for local users \n What are ACLs? \n ACLs (Access Control Lists) let you grant \"fine-grained\" access, such as write access to a specific directory or file, which isn’t possible with Container Permissions. More fine-grained access control has been a popular ask amongst our customers, and we are very excited to make this possible now with ACLs. A common ACL use case is to restrict a user's access to a specific directory without letting that user access other directories within the same container. This can be repeated for multiple users so that they each have granular access to their own directory. Without ACLs, this would require a container per local user. ACLs also make it easier for administrators to manage access for multiple local users with the help of groups. Learn more about ACLs for local users here. \n How to set and modify the ACL of a file or a directory? \n You can set and modify the permission level of the owning user, owning group, and all other users of an ACL by using an SFTP client. You can also change the owning user or owning group of a blob or directory. These operations require 'Modify Permissions' and 'Modify Ownership' container permissions, respectively. \n Note: Owning users can now also modify the owning group and permissions of a blob or directory without container permissions. This is a new feature enhancement added during the General Availability phase of ACLs for local users. For any user that is not the owning user, container permissions are still required. Learn more here. \n These enhancements significantly improve the management and usability of Azure Blob Storage SFTP by providing more granular access control over the container model and extending customer options. Please reach out to blobsftp@microsoft.com for feedback about SFTP for Azure Blob Storage. We look forward to your continued support as we strive to deliver the best possible solutions for your needs. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3662","kudosSumWeight":3,"repliesCount":3,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4397320":{"__typename":"Conversation","id":"conversation:4397320","topic":{"__typename":"BlogTopicMessage","uid":4397320},"lastPostingActivityTime":"2025-04-09T03:45:13.335-07:00","solved":false},"User:user:2950988":{"__typename":"User","uid":2950988,"login":"ParijatD","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yOTUwOTg4LXFDU2NHUQ?image-coordinates=0%2C0%2C2400%2C2400"},"id":"user:2950988"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MzIwLVIyS3lGdw?revision=2\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MzIwLVIyS3lGdw?revision=2","title":"Untitled design.png","associationType":"COVER","width":3080,"height":1733,"altText":""},"BlogTopicMessage:message:4397320":{"__typename":"BlogTopicMessage","subject":"ABFS Driver for Azure Blob Storage","conversation":{"__ref":"Conversation:conversation:4397320"},"id":"message:4397320","revisionNum":2,"uid":4397320,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:2950988"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Announcing the Release of Azure Blob Filesystem (ABFS) Driver with Support for Blob Endpoint in Flat Namespace (FNS) Storage Accounts. ","introduction":"Announcing the Release of Azure Blob Filesystem (ABFS) Driver with Support for Blob Endpoint in Flat Namespace (FNS) Storage Accounts","metrics":{"__typename":"MessageMetrics","views":397},"postTime":"2025-04-09T01:03:38.156-07:00","lastPublishTime":"2025-04-09T01:03:38.156-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" We are excited to announce the release of the Azure Blob Filesystem (ABFS) driver with support for the Blob endpoint in flat namespace (FNS) storage accounts. This new capability is designed to help our analytics customers seamlessly migrate from the legacy WASB (Windows Azure Storage Blob) driver to the ABFS driver for accessing Azure Blob Storage. While we continue to recommend all big data and analytics customers to use Azure Data Lake Gen2 (ADLS Gen2) with the ABFS driver over the DFS endpoint, this new option should help customers transition to a supported scenario immediately, while they plan to ultimately move to ADLS Gen2 with HNS enabled accounts. \n Note: The WASB driver codebase has not received security updates or maintenance for an extended period and could render systems exposed to potential exploits. We strongly recommend all customers to prioritize immediate migration to the ABFS driver for Hadoop instances to protect critical data assets and infrastructure from emerging threats. \n \n The Legacy WASB Driver and Its Deprecation \n The legacy WASB driver is a Java-based library that enables Hadoop and Hadoop-like applications to persist data in a remote object storage account like Azure Blob storage via the Azure Blob Storage REST API. While WASB is still part of Apache Hadoop releases, it has not seen active development or maintenance for several years, which may impact its reliability and performance. \n Microsoft's release of ADLS Gen2 included the ABFS driver, which is meant to replace the WASB driver. We collaborated with all our analytics platform partners to ensure a high-quality standard was achieved with ABFS \n What's New in This Version of the ABFS Driver \n The new version of the ABFS driver includes several enhancements and features designed to improve performance and usability for FNS accounts. Some of the key updates include: \n \n Support for Blob Endpoint in FNS Accounts: The ABFS driver now supports the Blob endpoint # in FNS accounts, allowing customers to use the ABFS driver without needing to upgrade to HNS \n Improved Performance: The ABFS driver has been optimized to provide better performance compared to the WASB driver. For example, the ABFS driver makes significantly fewer metadata calls and write calls, resulting in faster data operations \n \n # While the ABFS driver might function over the DFS endpoint in an FNS account, this configuration is not supported. We recommend that customers initialize the driver on the Blob endpoint when working with flat namespace (FNS) accounts. \n \n Enhanced AAD Based Authentication: The ABFS driver has a more robust support for AAD based authentication via oAuth 2.0 tokens. The oAuth 2.0 implementation includes native integration with Azure Identity libraries and a more sophisticated token acquisition, caching and renewal compared to what’s available in WASB. \n \n Performance Comparison: WASB vs. ABFS in FNS \n The ABFS driver demonstrated significant improvements over the WASB driver in FNS accounts. Here are some key highlights: \n \n Improved Transactions per Second (TPS): For common Read and Write operations, the ABFS driver has shown a median 55% and 20% improvement respectively compared to the WASB driver. \n Lesser Metadata Calls: The ABFS driver makes approximately 3.66 times fewer metadata calls compared to the WASB driver \n Fewer Write Calls: The ABFS driver makes about 1.66 times fewer write calls than the WASB driver \n Improved Runtime: The ABFS driver shows a significant reduction in runtime for various operations, making it more efficient for handling large datasets \n \n These improvements highlight the enhanced performance and efficiency of the ABFS driver, making it a superior choice for customers currently using the WASB driver. \n \n Migrating from WASB to ABFS Driver \n For customers currently using the WASB driver, we recommend two migration options to transition to the ABFS driver: \n \n Upgrade to HNS and Use ABFS Driver: \n \n The first and preferred option is to upgrade your storage accounts to HNS (hierarchical namespace) and use the ABFS driver. This option allows you to leverage the full capabilities of ADLS Gen2, including improved performance, security, and reliability. Upgrading to HNS enables the hierarchical structure, which is beneficial for managing large datasets and performing complex analytics operations. \n To upgrade your accounts to HNS, follow the detailed instructions provided here - Upgrading Azure Blob Storage to Azure Data Lake Storage | Microsoft Learn. \n \n Remain in FNS and Use ABFS Driver: \n \n If upgrading to HNS is not immediately feasible, the second option is to remain in FNS configuration temporarily and move to using the new ABFS driver. With the new support for the Blob endpoint in FNS accounts, you can still benefit from the ABFS driver's enhanced performance and capabilities without changing your account configuration. \n \n \n \n Call To Action \n The release of the Azure Blob Filesystem (ABFS) driver with support for the Blob endpoint in flat namespace (FNS) accounts marks a significant milestone in our efforts to provide a seamless and efficient data storage solution for our customers. We encourage all customers currently using the WASB driver to migrate to the ABFS driver, either by upgrading their storage account to HNS or by remaining in FNS. By doing so, you can take advantage of the enhanced performance, security, and reliability offered by the driver. \n If you have any questions on the driver or need assistance with the migration process, please drop us a note at askabfs@microsoft.com . \n \n Reference: \n ABFS Driver for Namespace Disabled Accounts (Flat Namespace) \n Azure Blob Storage REST APIs (Blob Endpoint) \n Hadoop Azure Commit List ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"5786","kudosSumWeight":3,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MzIwLVIyS3lGdw?revision=2\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":{"__typename":"UploadedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzk3MzIwLVIyS3lGdw?revision=2"},"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4401352":{"__typename":"Conversation","id":"conversation:4401352","topic":{"__typename":"ForumTopicMessage","uid":4401352},"lastPostingActivityTime":"2025-04-06T05:04:18.138-07:00","solved":false},"Forum:board:Azure":{"__typename":"Forum","id":"board:Azure","displayId":"Azure","nodeType":"board","conversationStyle":"FORUM","title":"Azure","shortTitle":"Azure","parent":{"__ref":"Category:category:Azure"}},"User:user:2982888":{"__typename":"User","uid":2982888,"login":"Aditya_vp","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yOTgyODg4LWdTVUdqVA?image-coordinates=0%2C0%2C2448%2C2448"},"id":"user:2982888"},"ForumTopicMessage:message:4401352":{"__typename":"ForumTopicMessage","subject":"Building an AI-Powered ESG Consultant Using Azure AI Services: A Case Study","conversation":{"__ref":"Conversation:conversation:4401352"},"id":"message:4401352","revisionNum":1,"uid":4401352,"depth":0,"board":{"__ref":"Forum:board:Azure"},"author":{"__ref":"User:user:2982888"},"metrics":{"__typename":"MessageMetrics","views":108},"postTime":"2025-04-06T05:04:18.138-07:00","lastPublishTime":"2025-04-06T05:04:18.138-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. Watch Our Video The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. Watch Our Video ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7964","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:4387407":{"__typename":"Conversation","id":"conversation:4387407","topic":{"__typename":"BlogTopicMessage","uid":4387407},"lastPostingActivityTime":"2025-03-20T12:00:11.396-07:00","solved":false},"User:user:2633582":{"__typename":"User","uid":2633582,"login":"dmalbrough","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS0yNjMzNTgyLTYxMjg4OGkzNjkyMzQ0RjAzNTk1RTI2"},"id":"user:2633582"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LTJ6TTI2Uw?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LTJ6TTI2Uw?revision=8","title":"cloudcasa-blog.png","associationType":"BODY","width":624,"height":482,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LUVycG4zTQ?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LUVycG4zTQ?revision=8","title":"cloudcasa-blog2.png","associationType":"BODY","width":624,"height":415,"altText":""},"BlogTopicMessage:message:4387407":{"__typename":"BlogTopicMessage","subject":"Simplifying Kubernetes Data Protection with CloudCasa and Microsoft Azure Blob Storage","conversation":{"__ref":"Conversation:conversation:4387407"},"id":"message:4387407","revisionNum":8,"uid":4387407,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:2633582"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"Data protection in Kubernetes environments can be complicated. On top of that, many companies run a mix of on-premises and cloud workloads. ","metrics":{"__typename":"MessageMetrics","views":199},"postTime":"2025-03-20T12:00:11.396-07:00","lastPublishTime":"2025-03-20T12:00:11.396-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" NOTE: This article is co-authored with our partner, CloudCasa. \n Ensuring reliable backups and fast recoveries in these hybrid setups is challenging. CloudCasa’s partnership with Microsoft Azure Blob Storage offers a robust solution to this challenge, delivering scalable, secure, and cost-effective data protection for Kubernetes cloud and hybrid cloud workloads. \n In this blog, we will look at how CloudCasa and Azure Blob solve common data protection challenges. We will explore how they simplify backups for Azure Kubernetes Service (AKS), offer centralized protection for hybrid clusters with Azure Arc, and give you a single view for managing VM and container backups. We will also cover how they help with disaster recovery in Kubernetes environments and how they push data to Azure Blob in an efficient manner. By the end, you will see how this partnership prepares your data protection strategy for the future. \n Seamless Integration with the Azure Ecosystem \n A good backup solution should not create more work. It should blend into the tools you already use. CloudCasa is purpose-built to integrate effortlessly with the Microsoft Azure ecosystem. This integration provides robust data protection for Kubernetes and hybrid cloud environments through: \n \n CloudCasa for AKS: Azure Kubernetes Service (AKS) is a popular way to run Kubernetes in Microsoft Azure. CloudCasa for AKS gives you an easy way to protect your containers, volumes, and metadata. You can set schedules and policies in CloudCasa to back up entire AKS clusters. If something goes wrong, you can quickly restore your workloads to the same or a different AKS cluster. \n \n Learn more about CloudCasa for AKS. \n \n \n CloudCasa for Azure Arc: Azure Arc extends Azure services to on-premises, multi-cloud, and edge environments. That means you can manage servers and Kubernetes clusters from a single point. CloudCasa for Azure Arc takes advantage of this central view. It lets you protect your Arc-enabled Kubernetes clusters wherever they run. Instead of juggling multiple backup tools for each environment, you can protect them all from a single place. \n \n Learn more about CloudCasa for Azure Arc. \n \n \n Azure Marketplace: Deploying and paying for new services often involves many steps. But CloudCasa is available on the Azure Marketplace, making it easy to start. You can sign up with a few clicks and have the charges appear on your Azure bill. This straightforward setup reduces friction and helps teams begin protecting their workloads right away. \n \n \n \n Supporting Hybrid Cloud and Kubernetes Workloads \n Modern organizations rarely keep all of their data in a single place. Some workloads might still run on-premises, while others run in different clouds. Kubernetes clusters can be scattered across these environments. Without a proper solution, managing backups for all these clusters can get messy. CloudCasa manages backups in a clever way. By default, it uses incremental backups whenever possible, which means it only moves the data that has changed since the last backup. This minimizes network usage and lowers storage costs. All these backups are stored in Azure Blob, which is both scalable and cost-effective. \n Unified Backup Management for Arc-Enabled Clusters \n When you connect your Kubernetes clusters to Azure Arc, you get a single view of all those clusters in the Azure portal. CloudCasa fits into this model by letting you create and apply backup policies across your hybrid environment. Instead of setting backup rules cluster-by-cluster, you can handle them from one place. This unification saves time and helps you avoid errors. \n A good backup is only valuable if you can recover from it quickly and without hassle. CloudCasa simplifies this process. You can restore your containers, PVs (Persistent Volumes), and cluster configurations back to their original places. This limits downtime and keeps your business moving. If your cluster is no longer available, you can also restore to a new location, including AKS, to keep your workloads running. \n Streamlining Kubernetes Disaster Recovery \n Disaster recovery (DR) is a top priority for many organizations. Traditional DR methods might involve copying data to a secondary site or region, then running complex scripts to bring everything back online. In a Kubernetes world, DR can be simpler if done the right way. Containers let you package your applications and dependencies in a portable format. If you have regular backups, you can quickly spin up your applications on another cluster if one site goes down. CloudCasa focuses on container-aware backups, so your entire environment—services, pods, configuration, and persistent data—can be restored. \n \n You don’t have to worry about missing parts of your cluster during recovery. If disaster strikes, you might need to create a brand-new cluster in Azure Kubernetes Service. CloudCasa makes this smoother. You can use your existing backups to set up a cluster in AKS, restoring namespaces, deployments, and volumes. This approach gives you a quick path to recovery without diving into manual steps or complicated scripts. \n Short-Term Recovery and Long-Term Archiving \n Azure Blob storage can keep multiple versions of your backups. This is helpful if you need to recover something quickly, like a file that was deleted by mistake. You can also use Azure Blob for long-term archiving, storing older backups at lower cost in access tiers like Cool or Archive. That means you meet compliance needs without breaking the bank, and you can still restore data if needed down the line. Sending backup data to the cloud must be efficient, both in terms of resource usage and network costs. If your backups overload your network or compute resources, it can slow down your day-to-day operations. CloudCasa is built to avoid these problems. \n Lightweight Agent Technology \n CloudCasa has a lightweight agent that runs in your Kubernetes clusters. This agent takes care of backup tasks without draining your compute and network resources. The agent only captures what it needs. With incremental backups, you don’t send the same data over and over again, which keeps your workload overhead low. CloudCasa includes metrics that show how much data is being backed up and how quickly. You can also see if there are any issues during the backup process. These insights help you spot bottlenecks and optimize your data protection approach. You can also control how often backups happen, which helps reduce unplanned spikes in storage costs or network usage. \n Cost-Effective Scalability \n Azure Blob follows a pay-as-you-go model. You only pay for what you use, whether it’s the size of stored data or how often you read and write that data. As your Kubernetes clusters grow or you add more clusters, CloudCasa and Azure Blob scale with you. There is no need to worry about running out of space or making large upfront investments in storage hardware. The technology landscape keeps changing. Businesses need solutions that can adapt to new platforms, new workloads, and new regulations. CloudCasa and Azure Blob offer that flexibility. \n Scalable Data Protection for Hybrid and Cloud-Native Environments \n Today you might be running a few clusters on-premises and a couple in the cloud. Next year, you might expand to more regions or adopt new container platforms. CloudCasa’s agent-based design and Azure Blob’s elastic storage model are built to handle this. You can protect more clusters as you go, without complex setup or hardware upgrades. In many industries, regulations require you to keep data for a certain period and show that it’s stored in a durable, secure way. Azure Blob provides high durability with multiple copies of your data in different locations. CloudCasa’s backups are encrypted in transit and at rest. This means you can meet compliance needs and still have control over your data retention policies. \n Advanced Recovery Capabilities \n As you expand, your recovery needs may become more complex. You may have to recover apps across different regions or clouds. CloudCasa gives you the option to restore to different clusters, whether they are on Azure or on another provider. This means you can change course when needed, without being tied to a single environment. And because Azure Blob keeps your data in a secure and highly available way, you can trust that it will be there when you need it. \n \n Conclusion \n Protecting Kubernetes workloads can be simpler than you think. With CloudCasa and Microsoft Azure Blob, you get a scalable and secure backup solution that works across hybrid and cloud-native environments. You also have a unified tool for both container and VM backups, which reduces management overhead. In case of disaster, CloudCasa helps you recover quickly to AKS or your original environment. And because everything is stored in Azure Blob, you benefit from its pay-as-you-go model and global infrastructure. \n By adopting this integrated approach, you can focus on delivering applications and services, rather than juggling multiple backup systems. CloudCasa makes it easy to set backup schedules and policies, track performance, and restore data wherever you run your workloads. You gain peace of mind knowing your data is protected and can be recovered quickly, without complicated scripts or risky manual steps. \n Next steps \n To learn more, visit CloudCasa for AKS and CloudCasa for Azure Arc on the Azure Marketplace, explore CloudCasa.io, or at casa@cloudcasa.io to get started. Additionally, we have a demo video showcasing the capabilities of both AKS and Azure Arc together, providing a comprehensive overview of how these solutions can benefit your organization. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"9756","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LTJ6TTI2Uw?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00Mzg3NDA3LUVycG4zTQ?revision=8\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:4382887":{"__typename":"Conversation","id":"conversation:4382887","topic":{"__typename":"BlogTopicMessage","uid":4382887},"lastPostingActivityTime":"2025-02-22T10:34:11.303-08:00","solved":false},"User:user:1620854":{"__typename":"User","uid":1620854,"login":"Ayushjuyal","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-5.svg?time=0"},"id":"user:1620854"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyODg3LUIyRUIwNw?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyODg3LUIyRUIwNw?revision=7","title":"url_upload.jpg","associationType":"COVER","width":7360,"height":4912,"altText":""},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyODg3LTltSVpFbA?revision=7\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS00MzgyODg3LTltSVpFbA?revision=7","title":"image.png","associationType":"BODY","width":941,"height":846,"altText":""},"BlogTopicMessage:message:4382887":{"__typename":"BlogTopicMessage","subject":"Microsoft Purview Protection Policies for Azure Data Lake & Blob Storage Available in All Regions","conversation":{"__ref":"Conversation:conversation:4382887"},"id":"message:4382887","revisionNum":7,"uid":4382887,"depth":0,"board":{"__ref":"Blog:board:AzureStorageBlog"},"author":{"__ref":"User:user:1620854"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":1703},"postTime":"2025-02-21T08:33:45.576-08:00","lastPublishTime":"2025-02-22T10:34:11.303-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Organizations today face a critical challenge: ensuring consistent and automated data governance across rapidly expanding data estates. Driven by the growth of AI and the increasing reliance on vast data volumes for model training, Chief Data Officers (CDOs) and Chief Information Security Officers (CISOs) must prevent unintentional exposure of sensitive data (PII, credit card information) while adhering to data and legal regulations. \n Many organizations rely on Azure Blob Storage and ADLS for storing vast amounts of data, offering scalable, secure, and highly available cloud storage solutions. While solutions like RBAC (role-based access control), ABAC (attribute-based access control), and ACLs (Access Control Lists) offer secure ways to manage data access, they can operate on metadata such as file paths, tags, or container names. These mechanisms are effective for implementing restrictive data governance by controlling who can access specific files or containers. However, there are scenarios were implementing automatic access controls based on the sensitivity of the content itself is necessary. For example, identifying and protecting sensitive information like credit card numbers within a blob requires more granular control. Ensuring that sensitive content is restricted to specific roles and applications across the organization is crucial, especially as enterprises focus on building new applications and infusing AI into current solutions. This is where integrated solutions like Microsoft Information Protection (MIP) come into play. \n Microsoft Information Protection (MIP) protection policies provide a solution by enabling organizations to scan and label data based on the content stored in the blob. This allows for applying access controls directly related to the data asset content across storage accounts. By eliminating the need for in-house scanning and labeling, MIP streamlines compliance and helps in applying consistent data governance using a centralized solution. \n \n The Solution: Microsoft Purview Information Protection (MIP) Protection Policies for Governance & Compliance \n Microsoft Purview Information Protection (MIP) provides an efficient and centralized approach to data protection by automatically restricting access to storage data assets based on sensitivity labels discovered through automated scanning and leveraging Protection policies (learn more). This feature builds upon Microsoft Purview's existing capability (learn more) to scan and label sensitive data assets, ensuring robust data protection. \n This not only enhances data governance but also ensures that data is managed in a way that protects sensitive information, reducing the risk of unauthorized access and maintaining the security and trust of customers. \n \n Enhancing Data Governance with MIP Protection policies: \n Contoso, a multinational corporation, handles large volumes of data stored in Azure Storage (Blob/ADLS). Different users, such as financial auditors, legal advisors, compliance officers, and data analysts, need access to different blobs in the Storage account. These blobs are updated daily with new content, and there can be sensitive data across these blobs. Given the diverse nature of the stored data, Contoso needed an access control method that could restrict access based on data asset sensitivity. For instance, data analysts access the blob named \"logs\" where log files are uploaded. If these files contain PII or financial data, which should only be accessed by financial officers, the access permissions need to be dynamically updated based on the changing sensitivity of the stored data. \n MIP protection policies can address this challenge efficiently by automatically limiting access to data based on sensitivity labels found through automated scanning. \n Key Benefits: \n \n Auto-labelling: Automatically apply sensitivity labels to Azure Storage based on detection of sensitive information types. \n \n \n Automated Protection: Automatically restrict access to data with specific sensitivity labels, ensuring consistent data protection. Storage Data Owners can selectively enable specific storage accounts for policy enforcement, providing flexibility and control. Like a protection policy that restricted access to data labeled as \"Highly Confidential\" to only specific groups or users. For instance, blobs labeled with \"logs\" were accessible only to data analysts. With MIP, the labels are updated based on content changes, and the protection policy can deny access if the content if any “Highly Confidential” data is identified. \n Enterprise-level Control: Information Protection policies are applied to blobs and resource sets, ensuring that only authorized Azure Entra ID users or M365 user groups can access sensitive data. Unauthorized users will be prevented from reading the blob or resource set. \n Centralized Policy Management: Create, manage, and enforce protection policies across Azure Storage from a single, unified interface in Microsoft Purview. Enterprise admins have granular control over which storage accounts enforce protection coverage based on the account’s sensitivity label. \n \n By using Microsoft Purview Information Protection (MIP) Protection Policies, Contoso was able to achieve secure and consistent data governance, and centralized policy management, effectively addressing their data security challenges \n \n Prerequisites \n Microsoft 365 E5 licenses and setup of pay as you go billing model. \n \n To understand pay as you go billing by assets protected, see the pay-as-you-go billing model. \n For information about the specific licenses required, see this information on sensitivity labels. \n Microsoft 365 E5 trial licenses can be attained for your tenant by navigating here from your environment. \n \n \n Getting Started \n The public preview of Protection Policies supports the following Azure Storage services: \n \n Azure Blob Storage \n Azure Data Lake Storage \n \n To enable Protection Policies for your Azure Storage accounts: \n \n Navigate to the Microsoft Purview portal> Information Protection card > Policies. \n Configure or use an existing sensitivity label in Microsoft Purview Information Protection that’s scoped to “Files & other data assets” \n Create an auto-labelling to apply a specific sensitivity label to scoped assets in Azure Storage based on Microsoft out-of-the-box sensitive info types detected. Run scans on assets for auto-labelling to apply. \n Create a protection policy and associate it with your desired sensitivity labels. \n Apply the policy to your Azure Blob Storage or ADLS Gen2 accounts. \n \n \n \n Limitations \n During the public preview, please note the following limitations: \n \n Currently a maximum of 10 storage accounts are supported in one protection policy, and they must be selected under Edit for them to be enabled. \n Changing pattern rules will re-apply labels on all storage accounts. During the public preview, there might be delays in label synchronization, which could prevent MIP policies from functioning effectively. \n If customer storage account enables CMK, the storage account MIP policy will not work. \n \n \n Next Steps \n With the Public Preview, MIP Protection policies is now available in all regions, and any storage account registered on the Microsoft Purview Data Map can create and apply protection policies to implement consistent data governance strategies across their data in Azure Storage. \n We encourage you to try out this feature and provide feedback. Your input is crucial in shaping this feature as we work towards general availability. 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