Announcing the availability of Azure Databricks connector in Azure AI Foundry
At Microsoft, Databricks Data Intelligence Platform is available as a fully managed, native, first party Data and AI solution called Azure Databricks. This makes Azure the optimal cloud for running Databricks workloads. Because of our unique partnership, we can bring you seamless integrations leveraging the power of the entire Microsoft ecosystem to do more with your data. Azure AI Foundry is an integrated platform for Developers and IT Administrators to design, customize, and manage AI applications and agents. Today we are excited to announce the public preview of the Azure Databricks connector in Azure AI Foundry. With this launch you can build enterprise-grade AI agents that reason over real-time Azure Databricks data while being governed by Unity Catalog. These agents will also be enriched by the responsible AI capabilities of Azure AI Foundry. Here are a few ways this can benefit you and your organization: Native Integration: Connect to Azure Databricks AI/BI Genie from Azure AI Foundry Contextual Answers: Genie agents provide answers grounded in your unique data Supports Various LLMs: Secure, authenticated data access Streamlined Process: Real-time data insights within GenAI apps Seamless Integration: Simplifies AI agent management with data governance Multi-Agent workflows: Leverages Azure AI agents and Genie Spaces for faster insights Enhanced Collaboration: Boosts productivity between business and technical users To further democratize the use of data to those in your organization who aren't directly interacting with Azure Databricks, you can also take it one step further with Microsoft Teams and AI/BI Genie. AI/BI Genie enables you to get deep insights from your data using your natural language without needing to access Azure Databricks. Here you see an example of what an agent built in AI Foundry using data from Azure Databricks available in Microsoft Teams looks like We'd love to hear your feedback as you use the Azure Databricks connector in AI Foundry. Try it out today – to help you get started, we’ve put together some samples here. Read more on the Databricks blog, too.Announcing general availability of Cross-Cloud Data Governance with Azure Databricks
We are excited to announce the general availability of accessing AWS S3 data in Azure Databricks Unity Catalog. This release simplifies cross-cloud data governance by allowing teams to configure and query AWS S3 data directly from Azure Databricks without migrating or duplicating datasets. Key benefits include unified governance, frictionless data access, and enhanced security and compliance.Announcing the availability of Azure Databricks connector in Azure AI Foundry
At Microsoft, Databricks Data Intelligence Platform is available as a fully managed, native, first party Data and AI solution called Azure Databricks. This makes Azure the optimal cloud for running Databricks workloads. Because of our unique partnership, we can bring you seamless integrations leveraging the power of the entire Microsoft ecosystem to do more with your data. Azure AI Foundry is an integrated platform for Developers and IT Administrators to design, customize, and manage AI applications and agents. Today we are excited to announce the public preview of the Azure Databricks connector in Azure AI Foundry. With this launch you can build enterprise-grade AI agents that reason over real-time Azure Databricks data while being governed by Unity Catalog. These agents will also be enriched by the responsible AI capabilities of Azure AI Foundry. Here are a few ways this seamless integration can benefit you and your organization: Native Integration: Connect to Azure Databricks AI/BI Genie from Azure AI Foundry Contextual Answers: Genie agents provide answers grounded in your unique data Supports Various LLMs: Secure, authenticated data access Streamlined Process: Real-time data insights within GenAI apps Seamless Integration: Simplifies AI agent management with data governance Multi-Agent workflows: Leverages Azure AI agents and Genie Spaces for faster insights Enhanced Collaboration: Boosts productivity between business and technical users To further democratize the use of data for those in your organization aren't directly interacting with Azure Databricks, you can also take it one step further with Microsoft Teams and AI/BI Genie. AI/BI Genie enables you to get deep insights from your data using your natural language without needing to access Azure Databricks. Here you see an example of what an agent built in AI Foundry using data from Azure Databricks available in Microsoft Teams looks like We'd love to hear your feedback as you use the Azure Databricks connector in AI Foundry. Try it out today – to help you get started, we’ve put together some samples here.
Error code 11408: The operation has timed out. Id. de actividad
Hello, I am starting with Azure Synapse, and when I want to ingest data with a copy, when I configure the connection to the data source (In this case, it is HTTP with a URL), I get this error, and I don't know why. I have configured the storage account with the IPs that have permissions, and I have also configured the IPs that have access in my Synapse resource. Additionally, I have enabled the managed virtual network with Data exfiltration protection enabled. I believe this should be related to that, but I don't know what extra configuration I need to do to allow this type of connections and others. I haven't found information regarding this error code, I would greatly appreciate any help.Power BI & Azure Databricks: Smarter Refreshes, Less Hassle
We are excited to extend the deep integration between Azure Databricks and Microsoft Power BI with the Public Preview of the Power BI task type in Azure Databricks Workflows. This new capability allows users to update and refresh Power BI semantic models directly from their Azure Databricks workflows, ensuring real-time data updates for reports and dashboards. By leveraging orchestration and triggers within Azure Databricks Workflows, organizations can improve efficiency, reduce refresh costs, and enhance data accuracy for Power BI users. Power BI tasks seamlessly integrate with Unity Catalog in Azure Databricks, enabling automated updates to tables, views, materialized views, and streaming tables across multiple schemas and catalogs. With support for Import, DirectQuery, and Dual Storage modes, Power BI tasks provide flexibility in managing performance and security. This direct integration eliminates manual processes, ensuring Power BI models stay synchronized with underlying data without requiring context switching between platforms. Built into Azure Databricks Lakeflow, Power BI tasks benefit from enterprise-grade orchestration and monitoring, including task dependencies, scheduling, retries, and notifications. This streamlines workflows and improves governance by utilizing Microsoft Entra ID authentication and Unity Catalog suite of security and governance offerings. We invite you to explore the new Power BI tasks today and experience seamless data integration—get started by visiting the [ADB Power BI task documentation].
From Doubt to Victory: How I Passed Microsoft SC-200
Hey everyone! I wanted to share my journey of how I went from doubting my chances to successfully passing the Microsoft SC-200 exam. At first, the idea of taking the SC-200 seemed overwhelming. With so many topics to cover, especially with the integration of Microsoft security technologies, I wasn’t sure if I could pull it off. But after months of studying and staying consistent, I finally passed! 🎉 Here’s what worked for me: Study Plan: I created a structured study schedule and stuck to it. I broke down each section of the exam objectives and allocated time for each part. Authentic Exam Questions: I used it-examstest for practice exams. Their realistic test format helped me get a good grasp of the exam pattern. Plus, the explanations for the answers were super helpful in understanding the concepts. Practice Exams: I did multiple mock tests. Honestly, they helped me more than I expected! They boosted my confidence, and I could pinpoint areas where I needed to improve. SC-200 Study Materials: I relied on a combination of online courses, books, and video resources. Watching the study videos and taking notes helped me retain the information better. Don’t Cram: I didn’t leave things to the last minute. It took me about 2-3 months of consistent study to get comfortable with the material. I made sure to take breaks and not burn myself out. Passing this exam felt amazing! If you're in the same boat and feeling uncertain, just stick with it! It’s a challenging exam, but with the right tools and preparation, you can do it. Keep pushing forward, and good luck to everyone! 💪 Would be happy to answer any questions if anyone has them!How integreate Azure IoT Hub with Azure Synapse in RealTime
Hello, I'm researching how to connect Azure IoT Hub with Azure Synapse, I've already used IoT Hub a bit but I don't have any knowledge of Synapse, it is also required that the data be in RT, so if someone has already done something similar or knows where I can find answers I would appreciate it. Have a good day.
Fabric Data Agents: Unlocking the Power of Agents as a Steppingstone for a Modern Data Platform
What Are Fabric Data Agents? Fabric Data Agents are intelligent, AI-powered assistants embedded within Microsoft Fabric, a unified data platform that integrates data ingestion, processing, transformation, and analytics. These agents act as intermediaries between users and data, enabling seamless interaction through natural language queries in the form of Q&A applications. Whether it's retrieving insights, analyzing trends, or generating visualizations, Fabric Data Agents simplify complex data tasks, making advanced analytics accessible to everyone—from data scientists to business analysts to executive teams. How Do They Work? At the center of Fabric Data Agents is OneLake, a unified and governed data lake that joins data from various sources, including on-premises systems, cloud platforms, and third-party databases. OneLake ensures that all data is stored in a common, open format, simplifying data management and enabling agents to access a comprehensive view of the organization's data. Through Fabric’s Data Ingestion capabilities, such as Fabric Data Factory, OneLake Shortcuts, and Fabric Database Mirroring, Fabric Data Agents are designed to connect with over 200 data sources, ensuring seamless integration across an organization's data estate. This connectivity allows them to pull data from diverse systems and provide a unified analytics experience. Here's how Fabric Data Agents work: Natural Language Processing: Using advanced NLP techniques, Fabric Data Agents enable users to interact with data through conversational queries. For example, users can ask questions like, "What are the top-performing investment portfolios this quarter?" and receive precise answers, grounded on enterprise data. AI-powered Insights: The agents process queries, reason over data, and deliver actionable insights, using Azure OpenAI models, all while maintaining data security and compliance. Customization: Fabric data agents are highly customizable. Users can provide custom instructions and examples to tailor their behavior to specific scenarios. Fabric Data Agents allow users to provide example SQL queries, which can be used to influence the agent’s behavior. They also can integrate with Azure AI Agent Service or Microsoft Copilot Studio, where organizations can tailor agents to specific use cases, such as risk assessment or fraud detection. Security and Compliance: Fabric Data Agents are built with enterprise-grade security features, including inheriting Identity Passthrough/On-Behalf-Of (OBO) authentication. This ensures that business users only access data they are authorized to view, keeping strict compliance with regulations like GDPR and CCPA across geographies and user roles. Integration with Azure: Fabric Data Agents are deeply integrated with Azure services, such as Azure AI Agent Service and Azure OpenAI Service. Practically, organizations can publish Fabric Data Agents to custom Copilots using these services and use the APIs in various custom AI applications. This integration ensures scalability, high availability, and performance and exceptional customer experience. Why Should Financial Services Companies Use Fabric Data Agents? The financial services industry faces unique challenges, including stringent regulatory requirements, the need for real-time decision-making, and empowering users to interact with an AI application in a Q&A fashion over enterprise data. Fabric Data Agents address these challenges head-on through: Enhanced Efficiency: Automate repetitive tasks, freeing up valuable time for employees to focus on strategic initiatives. Improved Compliance: Use robust data governance features to ensure compliance with regulations like GDPR and CCPA. Data-Driven Decisions: Gain deeper insights into customer behavior, market trends, and operational performance. Scalability: Seamlessly scale analytics capabilities to meet the demands of a growing organization, without really investing in building custom AI applications which require deep expertise. Integration with Azure: Fabric Data Agents are natively designed to integrate across Microsoft’s ecosystem, providing a comprehensive end-to-end solution for a Modern Data Platform. How different are Fabric Data Agents from Copilot Studio Agents? Fabric Data Agents and Copilot Studio Agents serve distinct purposes within Microsoft's ecosystem: Fabric Data Agents are tailored for data science workflows. They integrate AI capabilities to interact with organizational data, providing analytics insights. They focus on data processing and analysis using the medallion architecture (bronze, silver, and gold layers) and support integration with the Lakehouse, Data Warehouse, KQL Databases and Semantic Models. Copilot Studio Agents, on the other hand, are customizable AI-powered assistants designed for specific tasks. Built within Copilot Studio, they can connect to various enterprise data sources like OneLake, AI Search, SharePoint, OneDrive, and Dynamics 365. These agents are versatile, enabling businesses to automate workflows, analyze data, and provide contextual responses by using APIs and built-in connectors. What are the technical requirements for using Fabric Data Agents? A paid F64 or higher Fabric capacity resource Fabric data agent tenant settingsis enabled. Copilot tenant switchis enabled. Cross-geo processing for AIis enabled. Cross-geo storing for AIis enabled. At least one of these: Fabric Data Warehouse, Fabric Lakehouse, one or more Power BI semantic models, or a KQL database with data. Power BI semantic models via XMLA endpoints tenant switchis enabled for Power BI semantic model data sources. Final Thoughts In a data-driven world, Fabric Data Agents are poised to redefine how financial services organizations operate and innovate. By simplifying complex data processes, enabling actionable insights, and fostering collaboration across teams, these intelligent agents empower organizations to unlock the true potential of their data. Paired with the robust capabilities of Microsoft Fabric and Azure, financial institutions can confidently navigate industry challenges, drive growth, and deliver superior customer experiences. Adopting Fabric Data Agents is not just an upgrade—it's a transformative step towards building a resilient and future-ready business. The time to embrace the data revolution is now. Learn how to create Fabric Data AgentsLlama 4 is now available in Azure Databricks
We are excited to announce the availability of Meta's Llama 4 in Azure Databricks. As you know, enterprises all over the world already use Llama models in Azure Databricks to power AI enterprise agents, workflows, and applications. Now with Llama 4 and Azure Databricks, you can get higher quality, faster inference, and lower cost than previous models. Llama 4 Maverick, the highest-quality and largest Llama model from today's announcement, is built for developers building the next generation of AI products that combine multilingual fluency, image understanding precision, and security. With Maverick on Azure Databricks, you can: Build domain specific AI agents with your data Run scalable inference with your data pipeline Fine-tune for accuracy and Govern AI usage with Mosaic AI Gateway Azure Databricks Intelligence Platform makes it easy for you to securely connect Llama 4 to your enterprise data using Unity Catalog governed tools to build agents with contextual awareness. Enterprise data needs enterprise scale, whether it is to summarize documents or analyze support tickets, but without the infrastructure overhead. With Azure Databricks workflows and Llama 4 at scale, you can use SQL/Python to run LLMs at scale without overhead. You can tune Llama 4 to your custom use case for accuracy and alignment such as assistant behavior or summarization. All this comes with built in security controls and compliant model usage via Azure Databricks Mosaic AI Gateway with PII detection, logging, and policy guardrails on Azure Databricks. Llama 4 is available now in Azure Databricks. More models will become available in phases. Llama 4 Scout is coming soon and you'll be able to pick the model that fits your workload best. Learn more about Llama 4 and supported models in Azure Databricks here and get started today.
Delivering Information with Azure Synapse and Data Vault 2.0
Data Vault has been designed to integrate data from multiple data sources, creatively destruct the data into its fundamental components, and store and organize it so that any target structure can be derived quickly. This article focused on generating information models, often dimensional models, using virtual entities. They are used in the data architecture to deliver information. After all, dimensional models are easier to consume by dashboarding solutions, and business users know how to use dimensions and facts to aggregate their measures. However, PIT and bridge tables are usually needed to maintain the desired performance level. They also simplify the implementation of dimension and fact entities and, for those reasons, are frequently found in Data Vault-based data platforms. This article completes the information delivery. The following articles will focus on the automation aspects of Data Vault modeling and implementation.
