You’re running agents in the GitHub Copilot app - maybe three at once, each on its own branch, each working a task while you steer. It’s a good way to work, right up until an agent needs to reach out...Update Type: Announcement, Services: , Categories:
Connecting streaming workloads to Azure Event Hubs should not require teams to manage shared credentials. Organizations building real-time data solutions need a secure and manageable way to connect streaming workloads to event sources. With workspace identity support for Azure Event Hubs sources in Eventstream, now in preview, customers can use the identity associated with their Fabric workspace to authenticate. Workspace identity support for Azure Event Hubs sources gives Eventstream an identity-based alternative to shared access keys. Eventstream authenticates using the identity associated with the Fabric workspace, while administrators manage access through Azure role-based access control (Azure RBAC). This removes the need to store a connection string containing a shared access key in the Eventstream configuration and aligns the connection with how teams manage access to other Azure resources. Why identity-based authentication matters Azure Event Hubs has traditionally supported shared access key authentication, which requires customers to provide credentials as part of the connection configuration. While this approach is widely used, it also means teams need to manage and maintain those credentials as part of their Eventstream deployments. Many organizations are increasingly adopting identity-based authentication across Azure services to reduce reliance on shared secrets and centralize access management through Microsoft Entra ID and Azure RBAC. Workspace identity brings this model to Azure Event Hubs sources in Eventstream. Instead of storing shared access keys in the Eventstream configuration, customers can grant access directly to the Fabric workspace identity and use it to authenticate to Azure Event Hubs. This simplifies connection management and provides a more streamlined way to configure Azure Event Hubs sources in Eventstream. How workspace identity helps Workspace identity is an automatically managed service associated with a Fabric workspace. It give
Operational dashboards often need to answer a direct question: Is this metric healthy, approaching a limit, or already in a critical range? A number alone shows the current value, but it does not always provide the context needed to interpret that value quickly. The new KPI visualization is now generally available in Microsoft Fabric Real-Time Dashboards. It combines a query-based numeric value with a scale, status ranges, formatting options, and reference indicators so you can communicate metric health in a single responsive visual. Choose the right KPI display You can display a KPI in four modes: Number, Bar, Gauge, or Donut. Each mode uses the same underlying value and configuration while supporting a different dashboard layout. Number emphasizes the formatted value and works well in compact layouts. Bar shows progress across a horizontal scale and fits wide or narrow tiles. Gauge presents the value on an arc for operational monitoring. Donut uses a circular layout that works well in square dashboard tiles. You can select the numeric source column returned by your Kusto Query Language (KQL) query. This makes the visual useful for metrics such as CPU usage, latency, error rate, availability, queue size, ingestion delay, orders per minute, or other operational and business measures. Bike-sharing dashboard showing KPI visuals in Number, Bar, Donut, and Gauge modes for available bikes, dock utilization, station occupancy, and bikes in use. Define healthy, warning, and critical ranges The KPI visualization includes a configurable minimum and maximum scale. By default, the scale runs from 0 to 100, but you can adjust it to match the metric you are monitoring. Conditional-formatting ranges add status context to the current value. The default configuration includes OK below 33, Warning from 33, and Danger from 67. You can change the range boundaries, colors, and tag text to match the terminology and operating limits used by your team. You can update a boundary by draggUp
Summarized advertised gateway prefixes for route advertisement is now generally available. You can specify aggregated (summarized) prefixes for an Azure gateway to advertise to your on-premises networks, rather than having every individual virtual network
AI agents are increasingly becoming part of how developers work with data. But generating SQL is only one step in a typical warehousing workflow. Users still need to understand the right objects, run and validate queries, troubleshoot failures, and investigate performance when something goes wrong. Today, agents often either lack the access or the product-specific context needed to help across that full workflow. Model Context Protocol, or MCP, gives agents a standard way to connect to external systems and invoke tools. Agent Skills complement MCP by giving agents reusable, task-specific instructions that guide how they approach and complete work. For Fabric Data Warehouse, we are bringing both together. The Fabric Data Warehouse MCP Server (remote), now in preview, gives agents a governed way to execute SQL queries against the warehouse, while Skills for Fabric give them the Fabric-specific knowledge to use that access effectively. The result is a more connected workflow, where an agent can help users move from writing SQL, to validating it against live data, to troubleshooting and monitoring the warehouse without treating each step as a separate experience. Figure: Fabric Warehouse MCP Server & Skills for Fabric architectureFabric Data Warehouse MCP Server (remote) For an agent to help beyond drafting SQL, it needs a way to work with the actual warehouse. The Fabric Data Warehouse MCP server (remote), now in preview, gives MCP-compatible agents and clients a managed way to connect to Fabric Warehouses and SQL analytics endpoints. Agents can connect in two ways: Global endpoint gives the agent flexibility to work across Fabric Warehouses by providing the workspace and item context as part of the interaction. https://api.fabric.microsoft.com/v1/mcp/dataPlane/sqlEndpoint Item-scoped endpoint binds the connection directly to a specific Fabric Warehouse. https://api.fabric.microsoft.com/v1/mcp/dataPlane/woUpdate Type: Announcement, Services: Microsoft Fabric, Categorie
Starting in August 2026, customers can engage directly with Azure Copilot agents to move more quickly from questions to action.With this update:Choose the right agent for your goal. Customers can select the specific agent best suited to their needs and ge
Part two of a series on medallion architecture with Fabric Data Warehouse One job per layer — with enough implementation detail to make it real In Part 1 of this series, you picked your pattern. Now, let’s fill in the layers. The single most useful mental model here: each layer has exactly one job. Most medallion messes come from a layer doing another’s work — cleaning data in Bronze or letting business logic creep into Gold. In this post, we’ll look at hose Bronze, Silver, and Gold layers are implemented in Fabric Data Warehouse (DW), the T-SQL patterns commonly used in each layer, and the practices that help keep your architecture maintainable as it grows. We’ll also cover which practices are worth calling out before we move deeper into best practices in Part 3 of this series. Here’s what “one job” means in Fabric DW. Bronze — land it, don’t touch it Bronze has one job: ingest all source data in its original raw form, with no business logic or cleansing applied — “write everything down first” — so you preserve a source-of-truth copy you can always refer back to. In Fabric DW, that usually means staging tables that closely mirror the source structure, placed in a dedicated Bronze schema, such as Bronze.SalesOrdersRaw, to clearly mark them as raw. If the source is a relational extract, the Bronze table often follows the source columns. If the source is semi-structured, keep parsing to the minimum needed to land and trace the data. For loading, Fabric DW supports Data Factory Pipelines, Dataflows, COPY INTO, T-SQL ingestion, OPENROWSET, and Spark-based patterns. A common and efficient path is the T-SQL COPY INTO command to bulk-load files from OneLake or external storage into a DW table. Implementation sketch: CREATE SCHEMA Bronze; CREATE TABLE Bronze.CustomerRaw (CustomerID INT NULL, Name VARCHAR(100) NULL, Email VARCHAR(100) NULL, CreatedDate DATETIME2 NULL, RawFileName VARCHAR(255) NULL ); COPY INTO Bronze.CustomerRaUpdate Type: Announcement, Services: Microsoft F
Use ACS to enforce Scope, Anchored Decisions, Flow Integrity, and Escalation while the agent runs, then prove the controls without model variance. Two sign-in failures, two correct outcomes A hel...Update Type: Announcement, Services: Microsoft Foundry, Categories:
A pattern to steal, not a product to buy. Your CFO leans over and asks a reasonable question: "How much are we spending on Copilot versus the agents our own team built?" You open the cost report, c...Update Type: Announcement, Services: , Categories:
What is the Azure SRE Agent Azure SRE Agent is an AI-powered service designed to reduce operational toil. Teams can use it to: Investigate incidents and identify probable causes. Automate hea...Update Type: Announcement, Services: SRE Agent, Categories:
We are proud to announce that what-if for Azure Deployment Stacks is now generally available, in all regions. To be precise about what this is, since the names are similar: what-if for standard tem...Update Type: GA, Services: , Categories:
Today, we are thrilled to announce the General Availability (GA) of Managed Instance on Azure App Service. Following the tremendous response to our Public Preview announcement at Ignite 2025, we've ...Update Type: GA, Services: App Service, Categories:
Apple developers can now build and test their applications natively on Apple Silicon in Azure Pipelines. New arm64 macOS agents are available in public preview through the pay-as-you-go GitHub-hosted Agents pool. Starting with Apple Silicon, Azure Pipelines is bringing over some of the agent sizes already available in GitHub Actions. The preview includes the following […] The post Apple Silicon and Xcode 27 images available in pay-as-you-go (preview) appeared first on Azure DevOps Blog.Update Type: Announcement, Services: Azure DevOps, Categories:
A Successful Demo Is Not a Production Platform Getting GitHub Actions runners working on Azure Kubernetes Service (AKS) with Actions Runner Controller (ARC) is relatively straightforward. Within a ...Update Type: Announcement, Services: Kubernetes Service, Categories:
The browser is becoming a new execution environment Over the last decade, enterprises moved compute from desktops to the cloud. Applications that once depended on individual machines now run on sca...Update Type: Announcement, Services: , Categories:
Cloud-native platforms are becoming the foundation for AI transformation. Discover how Microsoft's Azure application platform helps organizations modernize, innovate, and operate AI-powered applications at scale. The post Microsoft named a Leader in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms appeared first on Microsoft Azure Blog.
In May, I introduced the open-source Azure Architecture Diagram Builder as a way to move from a natural-language prompt to an Azure architecture diagram, cost estimate, Well-Architected assessment, a...Update Type: Announcement, Services: Azure Arc, Categories: Features
Today, we’re announcing that AWS Certificate Manager (ACM) will discontinue support for email-validated public certificates by September 30, 2027. If you use email validation for your ACM public certificates, you need to migrate to DNS validation before that date. This change aligns with the Certification Authority/Browser (CA/B) Forum’s industry-wide deprecation of email-based domain validation and […]
As firewall rule sets grow in complexity, security teams face a common challenge: manual log analysis is used to determine which rules are actively matching traffic and which are consuming capacity without being triggered. This lack of visibility creates operational and compliance gaps. Organizations with governance policies that require removal of dormant rules after a […]
AWS partners can now configure category-based notifications and multi-channel delivery for AWS Marketplace notifications through AWS User Notifications. Previously, partners received their AWS Marketplace notifications through their AWS account’s root email address or custom email aliases, with no way to select notification categories or route each category to the teams responsible for managing it. With this launch, partners can choose which contacts receive each notification category and how those notifications are delivered. Four notification categories are available. Product listings notifications cover open product tasks, recurring scan findings for AMI and container products, and Vendor Insights security profile snapshots for SaaS products. Offers and agreements notifications cover private offers, reseller activity, professional services requests, agreement starts and cancellations, cancellation requests, and agreement creation failures. Payments and disbursements notifications cover payment requests, billing adjustments, invoice submission outcomes, payment failures, and disbursement issues. Account management notifications cover business and bank account verification actions, approvals, expirations, and rejections. By default, notifications are delivered by email to the AWS account’s root email address. Partners can add recipients through additional email addresses and distribution lists. Partners can also receive notifications through the AWS Console Mobile Application or Amazon Q Developer in chat applications such as Slack and Microsoft Teams. After enabling managed notifications, partners can select the contacts and delivery channels that receive each category. AWS Marketplace category-based notifications are available in all AWS Commercial Regions where AWS Marketplace is available. To learn more, see Managing email notifications for AWS Marketplace events in the AWS Marketplace documentation. To enable managed notifications, visit the AWS User Notificat
Amazon CloudWatch pipelines now includes three new processors that parse and enrich log data as it's ingested: an Amazon RDS log parser, an XML parser and a GeoIP enrichment processor. CloudWatch pipelines is a fully managed service that ingests, transforms, and routes telemetry to CloudWatch without managing infrastructure. Log sources often produce data that isn't immediately queryable without reprocessing the data. RDS Aurora logs arrive in their native engine format, application logs carry embedded XML, and IP addresses lack location context. The new processors address each case. The Amazon RDS processor parses Aurora audit and error logs into structured fields, the XML parser converts a field containing an XML string into JSON, and the GeoIP processor enriches any IP address field with geographic context such as city, country, and coordinates. For example, you can parse an Aurora audit log into structured fields for compliance reporting. In a separate pipeline, you can extract the XML payload from a Windows Event Log into JSON and resolve its source IP to a city and country for security analysis. You can use these processors independently or combine them in one pipeline. These processors are available at no additional cost in all AWS Regions where CloudWatch pipelines is generally available. CloudWatch logs ingestion and storage rates apply. You can add these processors to your pipelines using the AWS Management Console, AWS CLI, or AWS SDKs. To get started, see the Amazon CloudWatch pipelines documentation.
Amazon SageMaker AI now offers Generative AI Inference Recommendations in SageMaker AI Studio, giving customers a guided, low-code, no-code path to find the best inference configuration for their workload. This builds on the API-based launch in April 2026, extending the same benchmarking infrastructure to teams that prefer a visual workflow over programmatic access. Deploying generative AI models in production requires finding the right combination of instance type, serving container, and optimization strategy. Getting this right typically involves weeks of manual benchmarking, configuration tuning, and trial-and-error, with no easy way to know if the final setup is actually optimal. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. With the new experience, customers describe their workload and what matters most, whether that's latency, throughput, or cost, and SageMaker AI does the rest. It benchmarks multiple configurations on real GPU infrastructure using NVIDIA AIPerf, applies goal-aligned techniques like speculative decoding for throughput or kernel tuning for latency, and returns ranked, production-ready recommendations with measured performance data. Teams get to a validated configuration in hours instead of weeks, without needing to decide which techniques to apply or how to configure them. In SageMaker AI Studio under Jobs, Inference optimization, customers select a use-case profile (Interact, Generate, Summarize, or Custom), choose an opti
Today, AWS Direct Connect announced inbound prefix controls, a new capability that lets you allocate and manage inbound route-prefix allocations for your private and transit virtual interfaces (VIFs) based on your workload's needs. You can now allocate up to 1,000 prefixes each for IPv4 and IPv6 on your VIFs on dedicated and hosted connections. Previously, Direct Connect VIFs accepted a maximum of 100 route prefixes advertised from your on-premises network to AWS on a private or transit VIF. If you had a larger or growing network, you had to architect around this ceiling, for example, by summarizing routes or segmenting across multiple VIFs or connections. With inbound prefix controls, you can allocate up to 1,000 prefixes to a single VIF and advertise your routes directly. Inbound prefix controls introduce new prefix capacity pools at the dedicated connection level and at the Direct Connect gateway (DXGW) level. When you create or update a VIF, you allocate a specific number of prefixes to it, and that allocation draws from the dedicated connection's pool and the DXGW's pool when you attach it. This lets you right-size prefix capacity per workload—for example, a large allocation for a transit VIF carrying many routes and a smaller allocation for a private VIF on the same connection. Connection pool sizes scale with connection speed, and link aggregation group (LAG) pools scale with the number of member connections. You can configure prefix allocations using the AWS Direct Connect console or CLI/API. Inbound prefix controls are available at no additional cost in all commercial AWS Regions where AWS Direct Connect is available, AWS GovCloud Regions (US-East and US-West), as well as the Amazon Web Services China (Beijing) Region, operated by Sinnet, and the Amazon Web Services China (Ningxia) Region, operated by NWCD. To learn more, see Inbound prefix controls for AWS Direct Connect in the AWS Direct Connect User Guide.
Amazon EC2 now supports creating Amazon Machine Images (AMIs) with local snapshots from instances running on AWS Outposts. This capability makes it easier for customers to create AMIs while storing snapshots directly on the Outpost to meet data residency requirements. Customers can now create AMIs with snapshots stored either on the Outpost itself or in the parent AWS Region. When keeping data local, EC2 automatically determines the target Outpost from the instance's location — no need to manually specify an Outpost ARN. This also enables customers to integrate AMI creation into their existing backup and lifecycle workflows for instances on Outposts while ensuring data residency requirements are met. This feature is available in all AWS Regions in which AWS Outposts supports local snapshot storage. To learn more, visit the documentation.
AWS Elemental MediaConnect Router now enables customers to configure latency settings, providing control over the internal connection latency between inputs and outputs within the router. Previously, the protocol latency inside the router was set automatically by the service and could not be adjusted. MediaConnect Router now offers two latency configuration options on router outputs: balanced mode and low-latency mode. The balanced mode maintains the existing behavior for general use cases, while the low-latency mode optimizes the internal connection recovery time for latency-sensitive workflows. The appropriate mode is configured per router output, allowing the same input to feed multiple outputs with different latency requirements. A new CloudWatch metric, RouteFabricRecoveryLatency, provides visibility into the configured recovery latency for each route. Customers can configure and view the latency setting using the MediaConnect API or AWS Management Console and AWS CLI. Configurable recovery latency is available in all regions where MediaConnect Router is currently deployed. To learn more about latency modes, visit the AWS Elemental MediaConnect documentation.
Each location will be a permanent community space to offer free workshops, networking events, pitch nights, content creation spaces, collaboration/co-working areas, and event hosting for developers, students, or tech professionals who want to walk through the doors.
As AI agents move from chatbots to taking action, their reliability depends on the consistency of the data layer beneath them. This post examines how replication lag poisons an agent's context and shows how to match Amazon Aurora, Amazon DynamoDB, and Amazon Keyspaces replication models to each task's consistency requirements.
Today, Amazon Location Service adds per-category and density-based Point of Interest (POI) filtering to its maps. Developers gain server-side control over the density of POIs and which categories of places appear on a map. Developers control this behavior through the GetStyleDescriptor API. Amazon Location Service is a fully managed service that helps developers add maps, places, routing, and tracking to applications using high-quality geospatial data. Previously, every map style rendered all POI categories, and developers who wanted to tailor POIs to their use case relied on a client-side workaround tied to internal style layer identifiers. With this launch, the developer provides new poi-categories and poi-density query parameters. The poi-categories parameter takes an allowlist of categories - such as Food & Drink, Transit, or Accommodations—and the poi-density parameter tunes how many POIs appear on the map. Amazon Location Service returns a style descriptor that renders only the requested categories at the chosen density. Filtering is configured server-side, so maps display correctly with no additional code required on the client side, on web, mobile, and headless server-side renderers alike. A property-listing site can surface only accommodations, a logistics fleet app can show only transit and fuel, and a tourist map can highlight sights and dining. Amazon Location Service is available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Spain), Europe (Stockholm), South America (São Paulo), and AWS GovCloud (US-West). To get started, see Map features in the Amazon Location Service Developer Guide.
AWS Billing and Cost Management (BCM) Dashboards now include Managed Dashboards. These are a collection of preconfigured and read-only dashboards located in your dashboard list. They deliver actionable cost insights with your account data pre-populated without setup. There are five curated dashboards. Cost Overview & Trends tracks your spending patterns across services, accounts, and regions over 12 months with forward-looking forecasts. Compute and Database dashboards show your spend pattern within each service category, pairing cost breakdowns with relevant commitment coverage and utilization metrics in a single view. Reservations and Savings Plans dashboards show how well your purchased commitments are performing across all eligible services, quantifying gaps and underutilization in monetary terms. All managed dashboards are read-only and maintained by AWS. You can duplicate any dashboard to create a fully editable custom copy, add individual widgets to your existing dashboards, and export via PDF or CSV. Whether you are starting your FinOps journey or looking for a standardized baseline across accounts, Managed Dashboards give you cost visibility without setup so you can focus on analysis and action rather than configuration. AWS Managed Dashboards are available in all commercial AWS Regions at no additional cost. To get started, navigate to Dashboards in the AWS Billing and Cost Management console or view the user guide.
BigQuery Deprecated Starting April 26, 2027, core graph processing for BigQuery Graph will be restricted to the BigQuery Enterprise and Enterprise Plus editions. Consequently, we are deprecating support for Standard edition and on-demand billing for core graph processing. Graph measures will remain available in the Enterprise and Enterprise Plus editions and for queries run using on-demand pricing. Measures are not available in Standard edition. Feature The run_bq_command tool exposes the bq command-line tool within the Cloud CLI remote MCP server. AI agents can now execute advanced BigQuery operations, such as job scheduling, job management, and reservation management, through a managed MCP endpoint. For more information, see Use the Cloud CLI remote MCP server. This feature is in Preview.
