Help Shape the Future of Log Analytics: Your Feedback Matters
We’re launching a quick survey to gather your feedback on Azure Monitor Log Analytics. Your input directly impacts our product roadmap and helps us prioritize the features and improvements that matter most to you. The survey takes just a few minutes, and your responses will remain confidential. Take the Survey New to Log Analytics? Start here: Get Started with Azure Monitor Log Analytics Overview of Log Analytics in Azure Monitor - Azure Monitor | Microsoft Learn For questions or additional feedback, feel free to reach out to [email protected]. Thank you for being part of this journey!
Public Preview: Metrics usage insights for Azure Monitor Workspace
As organizations expand their services and applications, reliability and high availability are a top priority to ensure they provide a high level of quality to their customers. As the complexity of these services and applications grows, organizations continue to collect more telemetry to ensure higher observability. However, many are facing a common challenge: increasing costs driven by the ever-growing volume of telemetry data. Over time, as products grow and evolve, not all telemetry remains valuable. In fact, over instrumentation can create unnecessary noise, generating data that contributes to higher costs without delivering actionable insights. In a time where every team is being asked to do more with less, identifying which telemetry streams truly matter has become essential. To address this need we are announcing the Public Preview of ‘metrics usage insights’, a feature currently designed for Azure Managed Prometheus users which will analyze all metrics ingested in Azure Managed Workspace (AMW), surfacing actionable insights to optimize your observability setup. Metrics usage insights is built to empower teams with the visibility and tools the organizations need to manage observability costs effectively. It empowers customers to pinpoint metrics that align with their business objectives, uncover areas of unnecessary spend by identifying unused metrics, and sustain a streamlined, cost-effective monitoring approach. Metrics usage insights sends usage data to a Log Analytics Workspace (LAW) for analysis. This is a free offering, and there is no charge associated for the data sent to the Log Analytics workspace, storage or queries. Customers will be guided to enable the feature as part of the standard out of the box experience during new AMW resource creation. For existing AMWs this can be configured using diagnostic settings. Key Features 1.Understanding Limits and Quotas for Effective Resource Management Monitoring limits and quotas is crucial for system performance and resource optimization. Tracking usage aids in efficient scaling and cost avoidance. Metrics usage insights provides tools to monitor thresholds, resolve throttling, and ensure cost-effective operations without the need for creating support incidents. 2.Workspace Exploration This experience lets customers explore their AMW data and gain insights. It provides a detailed analysis of data points and samples ingested for billing, both at metric and workspace levels. Customers can evaluate individual metrics by examining their quantity, ingestion volume, and financial impact. 3.Identifying and Removing Unused Metrics The metrics usage insights feature helps identify underutilized metrics that are being ingested, but not used through dashboards, monitors, and API calls. Users facing high storage and ingestion costs can use this feature to delete unused metrics to optimize high-cost metrics, and reclaim capacity. Enable metrics usage insights To enable metrics usage insights, you create a diagnostic setting, which instructs the AMW to send data supporting the insights queries and workbooks to a Log Analytics Workspace (LAW). You'll be prompted to enable it automatically when you create a new Azure Monitor workspace. You can enable it later for an existing Azure Monitor workspace. Read MoreAzure Monitor Application Insights Auto-Instrumentation for Java and Node Microservices on AKS
Key Takeaways (TLDR) Monitor Java and Node applications with zero code changes Fast onboarding: just 2 steps Supports distributed tracing, logs, and metrics Correlates application-level telemetry in Application Insights with infrastructure-level telemetry in Container Insights Available today in public preview Introduction Monitoring your applications is now easier than ever with the public preview release of Auto-Instrumentation for Azure Kubernetes Service (AKS). You can now easily monitor your Java and Node deployments without changing your code by leveraging auto-instrumentation that is integrated into the AKS cluster. This feature is ideal for developers or operators who are... Looking to add monitoring in the easiest way possible, without modifying code and avoiding ongoing SDK update maintenance. Starting out on their monitoring journey and looking to benefit from carefully chosen default configurations with the ability to tweak them over time. Working with someone else’s code and looking to instrument at scale. Or considering monitoring for the first time at the time of deployment. Before the introduction of this feature, users needed to manually instrument code, install language-specific SDKs, and manage updates on their own—a process that involved significant effort and numerous opportunities for errors. Now, all you need to do is follow a simple two-step process to instrument your applications and automatically send correlated OpenTelemetry-based application-level logs, metrics, and distributed tracing to your Application Insights resource. With AKS Auto-Instrumentation, you will be able to assess the performance of your application and identify the cause of any incidents more efficiently using the robust application performance monitoring capabilities of Azure Monitor Application Insights. This streamlined approach not only saves time but also ensures that your monitoring setup is both reliable and scalable. Feature Enablement and Onboarding To onboard to this feature, you will need to follow a two-step process: Prepare your cluster by installing the application monitoring webhook. Choose between namespace-wide onboarding or per-deployment onboarding by creating K8’s custom resources. Namespace-wide onboarding is the easiest method. It allows you to instrument all Java or Node deployments in your namespace and direct telemetry to a single Application Insights resource. Per-deployment onboarding allows more control by targeting specific deployments and directing telemetry to different Application Insights resources. Once the custom resource is created, you will need to deploy or redeploy your application, and telemetry will start flowing to Application Insights. For step-by-step instructions and to learn more about onboarding visit our official documentation on MS Learn. The Application Insights experience Once telemetry begins flowing, you can take advantage of Application Insights features such as Application Map, Failures/Performance Views, Availability, and more to help you efficiently diagnose and troubleshoot application issues. Let’s look at an example: I have an auto-instrumented distributed application running in the demoapp namespace of my AKS cluster. It consists of: One Java microservice Two Node.js microservices MongoDB and Redis as its data layer Scenario: End users have been complaining about some latency in the application. As the DRI, I can start my troubleshooting journey by going to the Application Map to get a topological view of my distributed application. I open Application Map and notice MicroserviceA has a red border - 50% of calls are erroring. The Container Insights card shows healthy pods - no failed pods or high CPU/memory usage. I can eliminate infrastructure issues as the cause of the slowness. In the Performance card, I spot that the rescuepet operation has an average duration of 10 seconds. That's pretty long. I drill in to get a distributed trace of the operation and find the root cause: an OutOfMemoryError. In this scenario, the issue has been identified as an out-of-memory error at the application layer. However, when the root cause is not in the code but in the infrastructure I get a full set of resource properties with every distributed trace so I can easily identify the infra resources running each span of my trace. I can click the investigate pods button to transition to Azure Monitor Container Insights and investigate my pods further. This correlation between application-level and infrastructure-level telemetry makes it much easier to determine whether the issue is caused by the application or the infrastructure. Pricing There is no additional cost to use AKS auto-instrumentation to send data to Azure Monitor. You will be only charged as per the current pricing. What’s Next Language Support This integration supports Java and Node workloads by leveraging the Azure Monitor OpenTelemetry distro. We have distros for .NET and Python as well and we are working to integrate these distros into this solution. At that point, this integration will support .NET, Python, Java and Node.js. For customers that want to instrument workloads in other languages such as Go, Ruby, PHP, etc. we plan to leverage open-source instrumentations available in the Open Telemetry community. In this scenario, customers will instrument their code using open source OpenTelemetry instrumentations, and we will provide mechanisms that will make it easy to channel the telemetry to Application Insights. Application Insights will expose an endpoint that accepts OpenTelemetry Language Protocol (OTLP) signals and configure the instrumented workload to channel the telemetry to this endpoint. Operating Systems and K8’s Controllers Right now, you can only instrument kubernetes deployments running on Linux node pools, but we plan to expand support to introduce support for Linux ARM64 node pools as well as support for StatefulSet, Job, Cronjob, and Replicaset controller types. Portal Experiences We are also working on Azure portal experiences to make onboarding easier. When our portal experiences for onboarding are released, users will be able to install the Application Insights extension for AKS using the portal and use a portal user interface to instrument their workloads instead of having to create custom resources. Beyond onboarding, we are working to build Application Insights consumption experiences within the AKS namespace and workloads blade. You will be able to see application-level telemetry right there in the AKS portal without having to navigate away from your cluster to Application Insights. FAQs: What are the advantages of AKS Auto-Instrumentation? No code changes required No access to source code required No configuration changes required Eliminates instrumentation maintenance What languages are supported by AKS Auto-Instrumentation? Currently, AKS Auto-Instrumentation supports Java and Node.js applications. Python and .NET support is coming soon. Moreover, we will be adding support for all OTel supported languages like Go soon via native OTLP ingestion. Does AKS Auto-Instrumentation support custom metrics? For Node.js applications, custom metrics require manual instrumentation with the Azure Monitor OpenTelemetry Distro. Java applications allow custom metrics with auto-instrumentation. Click here for more FAQs. This article was co-authored by Rishab Jolly and Abinet Abate
Operator/CRD support with Azure Monitor managed service for Prometheus is now Generally Available
We are excited to announce that custom resource definitions (CRD) support with Azure Monitor managed service for Prometheus is now generally available. Azure Monitor managed service for Prometheus is a component of Azure Monitor Metrics, allowing you to collect and analyze metrics at scale using a Prometheus-compatible monitoring solution, based on the Prometheus project from the Cloud Native Computing Foundation. This fully managed service enables using the Prometheus query language (PromQL) to analyze and alert on the performance of monitored infrastructure and workloads. What's new? With this new update, customers can customize scraping targets using Custom Resources (Pod Monitors and Service Monitors), similar to the OSS Prometheus Operator. Enabling Managed Prometheus add-on in an AKS cluster will deploy the Pod and Service Monitor custom resource definitions to allow you to create your own custom resources. If you are already using Prometheus Service and Pod monitors to collect metrics from your workloads, you can simply change the apiVersion in the Service/Pod monitor definitions to use them with Azure Managed Prometheus. Earlier, customers who did not have access to kube-system namespace were not able to customize metrics collection. With this update, customers can create custom resources to enable custom configuration of scrape jobs in any namespace. This is especially useful in a multitenancy scenario where customers are running workloads in different namespaces. Here is how a leading Public Sector Banking and Financial Services and Insurance (BFSI) company in India has used Service and Pod monitors custom resources to enable monitoring of GPU metrics with Azure Managed Prometheus, DCGM Exporter, and Azure Managed Grafana. “Azure Monitor managed service for Prometheus provides a production-grade solution for monitoring without the hassle of installation and maintenance. By leveraging these managed services, we can focus on extracting insights from your metrics and logs rather than managing the underlying infrastructure. The integration of essential GPU metrics—such as Framebuffer Memory Usage, GPU Utilization, Tensor Core Utilization, and SM Clock Frequencies—into Azure Managed Prometheus and Grafana enhances the visualization of actionable insights. This integration facilitates a comprehensive understanding of GPU consumption patterns, enabling more informed decisions regarding optimization and resource allocation.” -A leading public sector BFSI company in India Get started today! To use CRD support with Azure Managed Prometheus, enable Managed Prometheus add-on on your AKS cluster. This will automatically deploy the custom resource definitions (CRD) for service and pod monitors. To add Prometheus exporters to collect metrics from third-party workloads or other applications, and to see a list of workloads which have curated configurations and instructions, see Integrate common workloads with Azure Managed Prometheus - Azure Monitor | Microsoft Learn. For more details refer to this article, or our documentation. We would love to hear from you - Please share your feedback and suggestions in Azure Monitor · Community.Azure Monitor Private Link Scope (AMPLS) Scale Limits Increased by 10x!
What is Azure Monitor Private Link Scope (AMPLS)? Azure Monitor Private Link Scope (AMPLS) is a feature that allows you to securely connect Azure Monitor resources to your virtual network using private endpoints. This ensures that your monitoring data is accessed only through authorized private networks, preventing data exfiltration and keeping all traffic inside the Azure backbone network. AMPLS – Scale Limits Increased by 10x in Public Cloud - Public Preview In a groundbreaking development, we are excited to share that the scale limits for Azure Monitor Private Link Scope (AMPLS) have been significantly increased by tenfold (10x) in Public Cloud regions as part of the Public Preview! This substantial enhancement empowers our customers to manage their resources more efficiently and securely with private links using AMPLS, ensuring that workload logs are routed via the Microsoft backbone network. Addressing Customer Challenges Top Azure Strategic 500 customers, including leading Telecom service providers, Banking & Financial services customers, have reported that the previous limits of AMPLS were insufficient to meet their growing demands. The need for private links has surged 3-5 times beyond capacity, impacting network isolation and integration of critical workloads. Real-World Impact Our solution now enables customers to scale their Azure Monitor resources significantly, ensuring seamless network configurations and enhanced performance. Scenario 1: A Leading Telecom Service Provider known for its micro-segmentation architecture, have faced challenges with large-scale monitoring and reporting due to limitations on AMPLS. With the new solution, the customer can now scale up to 3,000 Log Analytics and 10,000 Application Insights workspaces with a single AMPLS resource, allowing them to configure over 13,000 Azure Monitor resources effortlessly. Scenario 2: A Leading Banking & Financial Services Customer have faced the scale challenges in delivering personalized insights due to complex workflows. By utilizing Azure Monitor with network isolation configurations, the customer can now scale their Azure Monitor resources to ensure secure telemetry flow and compliance. They have enabled thousands of Azure Monitor resources configured with AMPLS. Key Benefits to the Customer We believe that the solution our team has developed will significantly improve our customers' experience, allowing them to manage their resources more efficiently and effectively with private links using AMPLS. An AMPLS object can now connect up to 3,000 Log Analytics workspaces and 10,000 Application Insights components. (10x Increase) The Log Analytics workspace limit has been increased from 300 to 3,000 (10x increase). The Application Insights limit has increased from 1,000 to 10,000 (10x increase). An Azure Monitor resources can now connect up to 100 AMPLSs (20x increase). Data Collection Endpoint (DCE) Log Analytics Workspace (LA WS) Application Insights components (AI) An AMPLS object can connect to 10 private endpoints at most. Redesign of AMPLS – User experience to load 13K+ resources with Pagination Call to Action Explore the new capabilities of Azure Monitor Private Link Scope (AMPLS) and see how it can transform your network isolation and resource management. Visit our Azure Monitor Private Link Scope (AMPLS) documentation page for more details and start leveraging these enhancements today! For detailed information on configuring Azure Monitor private link scope and azure monitor resources, please refer to the following link: Configure Azure Monitor Private Link Scope (AMPLS) Configure Private Link for Azure MonitorAzure Monitor Network Security Perimeter - Features available in 56 Public Cloud Regions
What is Network Security Perimeter? The Network Security Perimeter is a feature designed to enhance the security of Azure PaaS resources by creating a logical network isolation boundary. This allows Azure PaaS resources to communicate within an explicit trusted boundary, ensuring that external access is limited based on network controls defined across all Private Link Resources within the perimeter. Azure Monitor - Network Security Perimeter - Public Cloud Regions - Update We are pleased to announce the expansion of Network Security Perimeter features in Azure Monitor services from 6 to 56 Azure regions. This significant milestone enables us to reach a broader audience and serve a larger customer base. It underscores our continuous growth and dedication to meeting the security needs of our global customers. The Network Security Perimeter feature, now available in these additional regions, is designed to enhance the security and monitoring capabilities of our customers' networks. By utilizing our solution, customers can achieve a more secure and isolated network environment, which is crucial in today's dynamic threat landscape. Currently, NSP is in Public Preview with Azure Global customers, and we have expanded Azure Monitor region support for NSP from 6 regions to 56 regions. The region rollout has enabled our customers to meet their network isolation and monitoring requirements for implementing the Secure Future Initiative (SFI) security waves. Key Benefits to Azure Customers The Network Security Perimeter (NSP) provides several key benefits for securing and managing Azure PaaS resources: Enhances security by allowing communication within a trusted boundary and limiting external access based on network controls. Provides centralized management, enabling administrators to define network boundaries and configure access controls through a uniform API in Azure Core Network. Offers granular access control with NSP rules based on IP addresses or subscriptions. Includes logging and monitoring capabilities for visibility into traffic patterns, aiding in auditing, compliance, and threat identification. Integrates seamlessly with other Azure services and supports complex network setups by associating multiple Private Link Resources with a single perimeter. These characteristics highlight NSP as an excellent instrument for enhancing network security and ensuring data integrity based on the network isolation configuration. For detailed information on configuring Azure Monitor with a Network Security Perimeter, please refer to the following link: Configure Azure Monitor with Network Security Perimeter (Preview) Reference documentation links: Network Security Perimeter - Concepts Transition to a network security perimeter in Azure Have a Question / Any Feedback? Reach us at [email protected]Ingestion of Managed Prometheus metrics from a private AKS cluster using private link
This article describes the end-to-end instructions on how to configure Managed Prometheus for data ingestion from your private Azure Kubernetes Service (AKS) cluster to an Azure Monitor Workspace. Azure Private Link enables you to access Azure platform as a service (PaaS) resources to your virtual network by using private endpoints. An Azure Monitor Private Link Scope (AMPLS) connects a private endpoint to a set of Azure Monitor resources to define the boundaries of your monitoring network. Using private endpoints for Managed Prometheus and your Azure Monitor workspace you can allow clients on a virtual network (VNet) to securely ingest Prometheus metrics over a Private Link. Conceptual overview A private endpoint is a special network interface for an Azure service in your Virtual Network (VNet). When you create a private endpoint for your Azure Monitor workspace, it provides secure connectivity between clients on your VNet and your workspace. For more details, see Private Endpoint. An Azure Private Link enables you to securely link Azure platform as a service (PaaS) resource to your virtual network by using private endpoints. Azure Monitor uses a single private link connection called Azure Monitor Private Link Scope or AMPLS, which enables each client in the virtual network to connect with all Azure Monitor resources like Log Analytics Workspace, Azure Monitor Workspace etc. (instead of creating multiple private links). For more details, see Azure Monitor Private Link Scope (AMPLS) To set up ingestion of Managed Prometheus metrics from virtual network using private endpoints into Azure Monitor Workspace, follow these high-level steps: Create an Azure Monitor Private Link Scope (AMPLS) and connect it with the Data Collection Endpoint of the Azure Monitor Workspace. Connect the AMPLS to a private endpoint that is set up for the virtual network of your private AKS cluster. Prerequisites A private AKS cluster with Managed Prometheus enabled. As part of Managed Prometheus enablement, you will also have an Azure Monitor Workspace that is set up. For more information, see Enable Managed Prometheus in AKS. 1. Create an AMPLS for Azure Monitor Workspace Metrics collected with Azure Managed Prometheus is ingested and stored in Azure Monitor workspace, so you must make the workspace accessible over a private link. For this, create an Azure Monitor Private Link Scope or AMPLS. In the Azure portal, search for "Azure Monitor Private Link Scopes", and then click "Create". Enter the resource group and name, select Private Only for Ingestion Access Mode. Click on "Review + Create" to create the AMPLS. For more details on setup of AMPLS, see Configure private link for Azure Monitor. 2. Connect the AMPLS to the Data Collection Endpoint of Azure Monitor Workspace Private links for data ingestion for Managed Prometheus are configured on the Data Collection Endpoints (DCE) of the Azure Monitor workspace that stores the data. To identify the DCEs associated with your Azure Monitor workspace, select Data Collection Endpoints from your Azure Monitor workspace in the Azure portal. In the Azure portal, search for the Azure Monitor Workspace that you created as part of enabling Managed Prometheus for your private AKS cluster. Note the Data Collection Endpoint name. Now, in the Azure portal, search for the AMPLS that you created in the previous step. Go to the AMPLS overview page, click on Azure Monitor Resources, click Add, and then connect the DCE of the Azure Monitor Workspace that you noted in the previous step. 2a. Configure DCEs Note: If your AKS cluster isn't in the same region as your Azure Monitor Workspace, then you need to configure the Data Collection Rule for the Azure Monitor Workspace. Follow the steps below only if your AKS cluster is not in the same region as your Azure Monitor Workspace. If your cluster is in the same region, skip this step and move to step 3. Create a Data Collection Endpoint in the same region as the AKS cluster. Go to your Azure Monitor Workspace, and click on the Data collection rule (DCR) on the Overview page. This DCR has the same name as your Azure Monitor Workspace. From the DCR overview page, click on Resources -> + Add, and then select the AKS cluster. Once the AKS cluster is added (you might need to refresh the page), click on the AKS cluster, and then Edit Data Collection of Endpoint. On the blade that opens, select the Data Collection Endpoint that you created in step 1 of this section. This DCE should be in the same region as the AKS cluster. 3. Connect AMPLS to private endpoint of AKS cluster A private endpoint is a special network interface for an Azure service in your Virtual Network (VNet). We will now create a private endpoint in the VNet of your private AKS cluster and connect it to the AMPLS for secure ingestion of metrics. In the Azure portal, search for the AMPLS that you created in the previous steps. Go to the AMPLS overview page, click on Configure -> Private Endpoint connections, and then select + Private Endpoint. Select the resource group and enter a name of the private endpoint, then click Next. In the Resource section, select Microsoft.Monitor/accounts as the Resource type, the Azure Monitor Workspace as the Resource, and then select prometheusMetrics. Click Next. In the Virtual Network section, select the virtual network of your AKS cluster. You can find this in the portal under AKS overview -> Settings -> Networking -> Virtual network integration. 4. Verify if metrics are ingested into Azure Monitor Workspace Verify if Prometheus metrics from your private AKS cluster are ingested into Azure Monitor Workspace: In the Azure portal, search for the Azure Monitor Workspace, and go to Monitoring -> Metrics. In the Metrics Explorer, query for metrics and verify that you are able to query. Next steps Use private endpoints for Managed Prometheus and Azure Monitor workspace for details on how to configure private link to query data from your Azure Monitor workspace using workbooks.
Public Preview: The New AKS Monitoring Experience
We're excited to announce the public preview of our enhanced Monitoring experience for Azure Kubernetes Service (AKS). This redesign of the existing Insights experience brings comprehensive monitoring capabilities into a single, streamlined view, addressing some of the most common challenges users face when managing their AKS clusters. Our new Monitoring experience provides both basic (free) and detailed insights (with enabled Prometheus metrics and logging), offering a unified, single-pane-of-glass experience. The basic experience is available for all AKS users with no configuration required at all. A significant benefit of this new experience is in diagnosing pod deployment failures. In the past, identifying pending or failed pods could be a cumbersome process. With the new KPI Card for Pod Status, you can now quickly pinpoint and address these issues before they escalate, ensuring smoother deployments and reduced downtime. Another key scenario where this enhanced view shines is investigating node resource issues. Understanding node readiness and capacity is crucial for efficient cluster management. The Node Readiness Status card, along with detailed CPU and memory usage metrics, provides clear insights into whether your nodes are fully prepared to host pods. This helps prevent resource bottlenecks and optimizes the overall performance of your cluster. Ensuring cluster health during a scaling operation has never been easier. The new Summary Card for Events helps you monitor Kubernetes warning events and pending pod states, making it simple to track and respond to spikes. This ensures your cluster scales smoothly and efficiently, without unexpected hitches that could disrupt your services. Additionally, troubleshooting latency and connectivity issues in AKS is now more straightforward. With enhanced insights into node saturation metrics, including VMSS OS Disk Bandwidth and IOPS consumption, you can quickly identify and resolve issues causing latency. Detailed ETCD monitoring and Load Balancer metrics, such as % SNAT Port Usage, provide critical data to maintain optimal cluster performance, keeping your applications running smoothly. The following comparison table highlights what data comes out of the box for free for ALL AKS users. When you upgrade, you get all the same data collected in the newer Prometheus format as well as access to more rich metrics and logs for your core troubleshooting scenarios. Basic tier metrics Additional metrics in upgraded experience Alert summary card Historical Kubernetes events (30 days) Events summary card Warning events by reason Pod status KPI card Namespace CPU and memory % Node status KPI card Container logs by volume Node CPU and memory % Top five controllers by logs volume VMSS OS disk bandwidth consumed % (max) Packets dropped I/O VMSS OS disk IOPS consumed % (max) Load balancer SNAT port usage We’re committed to providing you with the tools you need to manage and optimize your AKS clusters effectively. Explore the new Monitoring experience in the Azure portal today and experience the future of AKS monitoring!Log Analytics Simple Mode is Now Generally Available
Over the past few months, we gradually rolled out the new Log Analytics experience to our users. The feedback has been positive, and the telemetry shows that users are more successful at working with their data. Today, we’re excited to announce that the new Log Analytics experience, including Simple Mode and other improvements, is now fully available and enabled by default. How simple is it? Here are two quick examples: Investigate Workspace Usage: Double-click the Usage table to load the latest data. Add an Aggregate operation to sum the Quantity column by DataType. Add a Sort operation by Quantity, and instantly see the results organized. At the top-right, click the three dots and create a New Alert Rule. Troubleshoot Kubernetes Pods: Select the KubePodInventory table and click Run to view the latest data. Filter the PodStatus column to Pending. Add an Aggregate operator to count the failed pods by Name. Click Share and export the results to CSV. That’s it - just a few clicks, and you’ve gained meaningful insights! Seamless Transition for Advanced Users If you’re comfortable with Kusto Query Language (KQL), you can switch to KQL Mode, edit the auto-generated query, and dive deeper. Once done, you can switch back to Simple Mode to continue exploring with updated results. You can also set your preferred default mode through the Settings menu for a customized experience. Improved Usability The interface includes organized menus for key actions like Save, Share, and Export, and a collapsible pane for quick access to tables, saved queries, examples, and more. To dive deeper into Simple Mode and other recent updates, visit our official documentation. Your Feedback Matters We’re committed to continuously improving Log Analytics to meet our users’ needs. Your input is invaluable in shaping its capabilities and user experience. For questions or feedback, feel free to reach out to [email protected] or use the Give Feedback form directly in Logs.
