Track news, policy changes, and vendor updates

Changelog OpenMetadata 2.0.8 is a maintenance release that expands connector coverage and improves lineage accuracy. It strengthens Data Quality and governance workflows. It also strengthens Model Context Protocol (MCP) authentication and access controls. The release improves search, Resource Description Framework (RDF) storage operations, and upgrades. It also improves user interface (UI) reliability. Security updates cover the server, ingestion, and Airflow. They also cover MCP and frontend dependencies. 🔌 Connectors and Ingestion This release includes the following connector and ingestion improvements: Deleting an ingestion pipeline could fail and leave its directed acyclic graph in Airflow #36267: Pipeline deletion now uses Airflow's supported cleanup path. It removes graph versions and cleans runs and task instances in dependency order. Glue Test Connection could fail on databases excluded by the pipeline filters #36574: Connection tests now probe only the databases and tables that ingestion is configured to read. Hex links were broken, and projects lacked detailed models and lineage #36420, #36388, #36856: Hex ingestion now builds working source links and creates data models with SQL-cell column lineage. It also ingests components, run history, and application views. Hex source tables linked to dashboards instead of their data models #37166: Table and column lineage now terminates at the Hex data model, including models created from query-history-only tables. Power BI retained stale dashboards and data models after a confirmed empty listing #36921: A complete, successful empty listing can now soft-delete entities that no longer exist while failed or incomplete listings remain protected. SSIS packages stored in Azure Blob Storage couldn't be configured #36917: SSIS connections now accept an Azure Data Lake Storage container in addition to Amazon S3 and local paths. BigQuery date-sharded tables appeared as separate physical assets #37002: An optional grouping mo

From Oct. 3 to Nov. 5, 2026, Amazon’s MadPot, a globally distributed network of decoy threat sensors, captured requests from AI agents that included operating memory from offensive campaigns that threat actors ran on ARTEX, an open-source agentic AI penetration testing framework. The ARTEX agents sent these requests to MadPot sensors configured to present as […]

As enterprises move AI agents from experimental prototypes into production, data and platform teams are discovering that deploying an agent is only half the battle — you also need to see how it reasons, which data it touches, and what it costs to run. Today, we are announcing expanded AI agent observability and evaluation capabilities across Google Data Cloud, giving you unified visibility into your first-party (1P) data agents in BigQuery and Looker as well as custom third-party (3P) agents built with agent development frameworks such as Agent Development Kit (ADK) or LangGraph. Putting conversational and autonomous agents into production requires solving three operational challenges: tracing multi-step reasoning while protecting sensitive data, controlling token consumption and query spend, and evaluating answer accuracy before prompt or schema changes reach users. Let’s explore how our new agent observability and evaluation capabilities help you do this. What's new: Agent observability across Data Cloud Running agents at scale means you need to see how they perform, what they cost, and what actions they take. We're rolling out a unified telemetry framework across Google's Agentic Data Cloud that works the same way for every agent: Capture telemetry from the agent, land it in BigQuery tables you own, and analyze it wherever your teams already work. Unified agent observability framework capturing logging telemetry across 1P and 3P agents into BigQuery to surface performance and usage insights across developer and admin tools. 1. Capture telemetry from every first-party and custom agent First-party data agents: Out-of-the-box Conversational Analytics agents emit telemetry automatically with no setup required. Queries routed to BigQuery data agents in Gemini Enterprise through agent-to-agent (A2A) workflows (now GA) send Cloud Monitoring metrics, Cloud Trace spans, and BigQuery conversation logs straight into your agent's project. Custom agents: Agents built with ADK

Bridging the gap between analytical systems (OLAP) and transactional databases (OLTP) is notoriously complex. Synchronizing data between the two systems, often called Reverse ETL (Extract, Transform, Load), traditionally requires heavy middleware, staging buckets, and complex orchestration, especially at enterprise scale. We’re announcing the general availability (GA) of BigQuery exports to AlloyDB, allowing data engineering teams to use a single, declarative SQL statement to export data from BigQuery to AlloyDB. This approach completely removes the need for complex ETL middleware. To demonstrate the benefits of this feature, we are sharing how Copel, a Brazilian electric power provider, eliminated a massive orchestration bottleneck and scaled their smart meter telemetry by migrating to high-performance, built-in Reverse ETL. The challenge of scaling smart meter telemetry At Copel, managing telemetry data from millions of smart electricity meters is a mission-critical operation. This data is streamed continuously and consolidated in an analytical Gold layer in BigQuery. However, to power Copel's "Systems of Action" — low-latency customer support dashboards, mobile applications, and operational billing systems — this rich dataset must be constantly synced back into its high-performance transactional database, AlloyDB for PostgreSQL. Until recently, Copel orchestrated this Reverse ETL architecture with a complex DAG in Google’s Managed Service for Apache Airflow. However, there were multiple brittle, intermediate stages in the process of enriching and exporting hundreds of millions of records from BigQuery to AlloyDB: Figure 1: Copel’s legacy multi-stage architecture requiring Cloud Storage file dumps and sequential gCloud scripts. BigQuery ran enrichment and extraction queries, dumping SQL results into dozens of fragmented CSV files inside a Google Cloud Storage bucket. Apache Airflow executed SQL to create temp tables in AlloyDB to prevent write locking or data corr

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources. aside_block Nov 9 - Nov 13 More frontier model choice on Google Cloud: Anthropic’s Claude Fable 5.2 and Meta’s Muse model series. Developers now have even more flexibility to build, deploy, and scale with the right model for every workload on Google Cloud. Anthropic’s Claude Fable 5.2 is now generally available in the Model Garden, bringing frontier intelligence to complex coding, scientific research, and multi-agent orchestration. Plus, Meta’s newest models—Muse Spark 1.3, Muse Wonder, and the efficient Muse Ember—are now available in public preview, giving teams seamless access to both proprietary and open-weight capabilities with enterprise-grade security. Explore the models in the model garden today. Next-Generation SecOps: SentinelOne and Google CloudGoogle Cloud and SentinelOne joined forces to solve the threat scaling challenge with a fundamentally re-architected integration. As a Google Unified Security (GUS) Recommended endpoint detection and response (EDR) partner, SentinelOne pairs Google Security Operations through an optimized direct integration and streamlined data schemas, delivering essential endpoint telemetry visibility to SecOps detection and agentic SOC pipelines while significantly reducing the total cost of ownership of the integration. Read more Layered LLM guardrails on Apigee: Static rules, Model Armor, and NeMo Guardrails No single LLM guardrail catches every threat. A new technical guide and demo showcase how to use Apigee as an AI gateway to stack three complementary layers—deterministic static rules, semantic Google Cloud Model Armor filters, and flexible business logic via NVIDIA NeMo Guardrails

You can use Genie Code to import Power BI and Tableau files as AI/BI dashboards and metric views. Starting on or after December 15, 2026, this will be available by default for workspaces with the compliance security profile enabled. Until then, a workspace admin can turn it on from Previews in the workspace settings. Importing requires partner-powered AI features for the account and workspace. See Import BI files using Genie Code.

Healthcare is adopting AI quickly, and it's moving beyond chat. Clinical teams are starting to use AI agents that draft documentation, summarize charts, navigate web apps, and complete multi-step tasks on a clinician's behalf. Many of these agents work in the same place—the browser. As electronic health records (EHRs) and clinical apps move to the web, the browser is where clinicians, applications, patient data, and AI services meet. When clinicians or agents act inside a session, security teams need clear control over what they can access.Healthcare systems frequently adopt a multi-vendor AI strategy, a flexibility that should be fully supported. Whether clinicians are using Gemini™ or an array of other web-based AI tools and extensions, Chrome Enterprise Premium secures data across the board to:Guard sensitive data: Advanced DLP controls restrict unauthorized copying and pasting, form submission, printing, and screen capture of sensitive data—preventing patient data from leaking into unapproved AI prompts or browser extensions.Maintain full operational visibility: Granular reporting and visibility into browser activity, extension usage, and data transfers help security teams uncover shadow AI and maintain internal oversight.Govern extensions and web AIs consistently: Security policies and extension controls apply directly at the browser layer, so IT teams can manage permissions, block risky extensions, and enforce guardrails across every AI service without needing a separate harness for each tool.Since we introduced new healthcare integrations in May, Chrome Enterprise has continued to invest in the healthcare ecosystem. Today we're sharing the next set of milestones that bring Chrome Enterprise to more clinical endpoints.Chrome Enterprise is now compatible with IGEL OS™As healthcare organizations accelerate the adoption of web-based clinical applications and AI-powered workflows, securing both the browser and the endpoint has become increasingly important. To add

Data governance with Microsoft Purview Summarize this article for me In this article Important Microsoft Purview Unifie... You can no longer create new accounts, and existing capabilities will remain in maintenance mode through November 12, 2027. Microsoft Purview data governance solutions, Unified Catalog and Data Map, deliver a modern data.

Data governance roles and permissions in Microsoft Purview Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being retired. Microsoft is consolidating its data governance capabilities in Microsoft Fabric. ...icle covers.

Microsoft Purview Data Map overview Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being retired. Microsoft is consolidating its data governance capabilities in Microsoft Fabric. Microsoft Purview Data Map is the.

Data sources that connect to Data Map Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being retired. This article lists the supported data sources, file types, and scanning concepts in Microsoft Pu... Data source.

Learn about data governance billing Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being retired. Microsoft is consolidating its data governance capabilities in Microsoft Fabric. Microsoft Purview data governance.

Learn about data governance in the Microsoft Purview portal Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being... The new governance experience The Microsoft Purview portal, https://purview.microsoft.com, is a.

Create a Microsoft Purview account Summarize this article for me In this article Important Microsoft Purview Unified Catalog and classic Microsoft Purview data governance capabilities (formerly Azure Purview) are being retired. You can use the free versio... This quickstart describes the steps to create a Microsoft Purview (formerly Azure Purview).

Explore how you can build good data governance practices across your data estate using Microsoft Purview Unified Catalog.

Even as sophisticated, new zero-day exploits continue to emerge, most cybersecurity incidents still start from the same common sources: an attacker locating and exploiting a fundamental, easily preventable cloud misconfiguration. Organization Policy Service can help you secure resource configurations and set up security guardrails across your Google Cloud resource hierarchy. Using a catalog of over 100 predefined policies for many common cloud governance needs, as well as Custom Organization Policy, you can more easily set up boundaries with granular control over resources. To help platform and security teams author, audit, and scale guardrails with greater speed and simplicity, we are introducing the general availability of our Organization Policy Model Context Protocol (MCP) server and natural-language constraint authoring powered by Gemini. Managing Organization Policy with agents Rather than forcing administrators to manually parse complex hierarchy chains, the Organization Policy MCP server offers tools that allow autonomous agents and pipelines to interact directly with Organization Policy. Using the Organization Policy MCP server, agents can assist administrators with posture audits, drift detection, and exemption management. Key capabilities include: Constraint discovery: Tools like 'ListConstraints' and 'GetCustomConstraint' allow agents to understand the specific guardrails that apply to a project, folder, or organization. Policy evaluation: The 'GetEffectivePolicy' tool calculates the effective policy after considering hierarchy inheritance and tag-based conditions. You can use this to verify if a configuration change will be compliant. Manage policy: 'CreatePolicy' and 'CreateCustomConstraint' allow platform teams to manage guardrails via conversational workflows. For example, you can combine your preferred coding agent with the Organization Policy MCP tools to create a custom dashboard to visualize the constraints applied to an organization and identify

November 9 - November 13 Bringing conversational analytics to Gemini with Data Cloud connectors: Data Cloud connectors in Gemini are now in preview, enabling knowledge workers to query live enterprise data using natural language directly within tools like Google Docs, Gmail, and Google Slides. The capability connects across BigQuery, Looker, Spanner, AlloyDB for PostgreSQL, and Cloud SQL without requiring data movement, grounded by Knowledge Catalog semantic models under user-identity IAM governance. Read the announcement blog. Direct Apache Iceberg access in AlloyDB enables unified lakehouse querying: AlloyDB for PostgreSQL now supports direct querying of Apache Iceberg tables stored in Cloud Storage without requiring ETL pipelines or data duplication. Transactional applications can join operational data with analytical lakehouses in real time using standard PostgreSQL queries, accelerating cross-system analysis while maintaining complete relational integrity. Together with AlloyDB sync and lakehouse federation, you now have three integration approaches to choose from, depending on your use case. Read the launch blog. AlloyDB Express delivers instant scale-to-zero PostgreSQL for developer prototyping: AlloyDB Express is now available, providing a lightweight, fast-provisioning PostgreSQL environment tailored for developers, testing, and AI coding agents like Antigravity. It scales to zero when idle to minimize costs while maintaining full PostgreSQL compatibility, allowing engineering teams to transition prototype workloads to production AlloyDB instances when ready. AlloyDB Express offers a free tier, with easy-to-understand pricing for higher usage. Read the announcement blog. Carwow unifies its data foundation with BigQuery, Knowledge Catalog, and Looker to scale AI innovation: Europe's largest online car-changing marketplace migrated to BigQuery, Knowledge Catalog, and Looker to establish a governed single source of truth and enable trusted self-service analyti

Modern security operations centers (SOCs) face an ongoing pipeline dilemma. While ingesting comprehensive endpoint telemetry is non-negotiable for defense, conventional delivery paths often drive up transfer overhead and alert latency. As attackers operate at machine speed, any unnecessary friction in how data moves from endpoints to the SOC can leave teams without the insight they need to respond to attacks in real time. Addressing this requires optimizing the data pipeline at the integration layer. Rather than routing telemetry through complex intermediary cloud buckets that require ongoing maintenance and introduce costly processing delays, engineering teams need a streamlined delivery architecture. By eliminating architectural bottlenecks and optimizing data transit, security operations and IT leaders can significantly compress ingest latency, lower operational overhead, and give analysts near-instant visibility where it matters most. Google Cloud and SentinelOne collaborated to solve this scaling challenge with a fundamentally re-architected integration. As a Google Unified Security Recommended endpoint detection and response (EDR) partner, SentinelOne pairs with Google Security Operations through direct integration and streamlined data schemas, delivering essential endpoint telemetry visibility to Google Security Operations detection and agentic SOC pipelines while significantly reducing the total cost of ownership of the integration. What’s at stake? For enterprise customers, every minute of telemetry latency can increase the blast radius of an active intrusion. When detection pipelines take half an hour to surface adversary activity, incident response transitions from active mitigation to retrospective damage control. For the broader security community and IT leadership, inefficient ingestion pipelines can impact budget and compute resources. Retaining bulky, unoptimized telemetry in intermediate staging environments diverts resources away from strategic sec

Gartner has named Google a Leader, positioned furthest in vision, in its first-ever Magic Quadrant for Data Lakehouse Platforms. In our opinion, this recognition validates a fundamental shift our customers are experiencing every day: In the agentic era, the enterprise lakehouse is no longer a passive data repository, but a foundation where agents reason and act across all your data. The lakehouse originally emerged to bring data warehouse reliability to open, low-cost storage, but the benchmark defining the category is now shifting. Leading enterprises are moving rapidly from static reporting to autonomous AI agents that monitor operations, flag anomalies, and execute business workflows in real time. To support that shift, organizations need a platform where thousands of agents can read, reason, and act continuously across enterprise data without having to move that data first. Traditional, fragmented data stacks burdened by brittle pipelines, duplicated storage, and disjointed governance simply were not designed for the scale, performance, and latency required for agentic-scale workloads. We engineered our borderless Lakehouse at every layer of the stack to eliminate this complexity and hidden cost. Specifically designed for agents to reason across petabyte-scale data, the borderless Lakehouse unifies your data estate across clouds, data platforms, and SaaS applications so you can ground agents in governed business context and bring Gemini directly to your data. This allows your teams to move from managing infrastructure to orchestrating business outcomes. Activate your global data footprint in place For most organizations, the biggest barrier to scaling AI is that enterprise data lives everywhere, spanning multiple clouds, operational databases, and SaaS platforms. Moving all of that data into a single physical location is slow, expensive, and introduces governance risk. Our borderless Lakehouse acts as the bridge across your distributed environment to activate yo

Artifacts are slide decks and other custom visual pages that Genie One builds from your data and saves to your workspace. Ask Genie for an artifact in chat, edit it yourself or ask for changes, then present it and share it with others. Anyone who views an artifact can use Ask Genie to ask questions about the data behind a chart or number. An artifact stores a snapshot of its data, so anyone who can view it sees that data, even without access to the source tables. This feature is in Beta. A workspace admin turns on the Artifacts preview from the Previews page. See Artifacts in Genie One and Slides in Genie One.

The Genie One Chrome extension replaces the Chrome new tab page with Genie One from your Databricks workspace. While the extension is turned on, new tabs open the Genie One home page, so you can ask a question, search across your connected tools, or open a dashboard without first going to your workspace. The extension uses your existing Databricks sign-in and permissions, and it works with the one workspace you choose. This feature is in Beta. See Use Genie One as your Chrome new tab page.

Lakebase updated its supported Postgres versions to 18.7, 17.12, and 16.16, less than 30 minutes after the PostgreSQL community released them. These minor releases include the latest upstream Postgres bug fixes and security patches. Lakebase applies a new minor version the next time your compute restarts. See Recent minor version rollouts.

Expanded FOR clause support: DENY policies can now apply to model services, model provider services, MCP services, agent services, and skills. Expanded ON clause support: DENY policies can now be attached directly on tables, volumes, functions, models, secrets, model services, model provider services, MCP services, agent services, and skills. See Supported securable types and privileges.

Pipeline flows are now securable objects in Unity Catalog. You can grant READ FLOW, REFRESH FLOW, WRITE FLOW, APPLY TAG, and MANAGE on individual flows, and CREATE FLOW on schemas. To participate in the Beta, contact your Databricks account team. See Flows in Unity Catalog.

Google Gemini 4 Hydrogen is now available on Unity Gateway. You can access this model using Foundation Model APIs. Customers are responsible for ensuring their compliance with applicable terms.

Google Gemini 4 Hydrogen is now available on Unity Gateway. You can access this model using Foundation Model APIs. Customers are responsible for ensuring their compliance with applicable terms.

November 12, 2026 QTC SAP HANA RFC Connector New QTC SAP HANA RFC Connector for SAP table replication from S/4HANA and ECC on HANA systems. Option for SAP HANA customers impacted by SAP blocking all ODP-RFC access in December 2026 or wanting better performance than OData. Qlik ABAP Add-on installed on SAP system allows for table registration and snapshot/change log processing for CDC capability. QTC SAP HANA RFC Connector calls ABAP Add-on directly avoiding direct database access to process Full Load and CDC requests. SAP HANA RFC Some functionality may be rolled out in phases and not immediately available in all regions.

Banks are racing to appoint Chief Data and AI Officers — but the word order matters. Many banks are putting data governance ahead of AI ambition, and the research backs them up: MIT found 95% of generative AI pilots fail to show ROI, while a 2026 financial-distress study found that data preparation alone can lift model accuracy by over 15 points. In banking, most of that data is unstructured — credit memos, underwriting files, call transcripts — and ungoverned under BCBS 239, where only 18% of banks report full compliance. This piece breaks down five reasons the “data-first” title isn’t semantics: it’s a signal that governance, lineage, and AI-ready data are the real foundation behind durable AI performance in financial services.

The /api/v1/direct-access-agents endpoints are deprecated. Migrate to the /api/connectivity/direct-access-agents endpoints.

Starting on Amazon Web Services (AWS) is now more straightforward and more secure by default than it’s ever been. Over the past several months, we rolled out a new AWS experience that changes how you start building. It brings together a set of sensible, secure defaults, and taken one at a time, it’s easy to […]

Over the past few weeks, I’ve introduced you to people who fell into security from the most unexpected places, who work in domains you’ve probably never heard of, and who were honest about what the job is like after the excitement fades and the real work begins. This is the final post in a four-part […]

Learn what the Fabric IQ MCP server is, connect GitHub Copilot CLI, test the connection, and understand its Power BI data-exploration tools.

The teams responsible for security today don’t have a problem with the number of alerts they receive; they have a problem turning those alerts into something truly actionable. When an analyst concludes an investigation with “this was caused by our vulnerability scanner” or “this account is configured to share Amazon Machine Images (AMIs) publicly on […]

Amazon Web Services (AWS) is pleased to announce renewal of the AWS ProcessUnity Global Risk Exchange assessment. This validated assessment report helps customers perform effective cloud supplier due diligence on AWS and enhances their third-party risk management process. What is the AWS ProcessUnity Global Risk Exchange assessment Many customers use third-party cyber risk management (TPCRM) […]

AWS re:Invent returns to Las Vegas for cloud and AI’s biggest week from November 30–December 4, 2026. This year we have over 2,200 sessions, 70 percent of which are interactive, led by experts from Amazon Web Services (AWS), our customers, and our partners. With that much to choose from, we’ve pulled together the sessions built […]

Learn how workspace monitoring in Microsoft Fabric collects logs and metrics for analyzing the usage, health, and performance of items in a workspace.

Autonomy is an outcome of AI maturity. Build the trusted data, governance, and context that let AI and agents act with control.

An introduction to integrating Git version control with the Fabric Application lifecycle management (ALM) tool

November 10, 2026 New Direct Access gateway version (1.8.2) Direct Access gateway 1.8.2 introduces both new and enhanced features, and resolves several issues. For upgrade instructions and a list of resolved issues, see Upgrading the Direct Access gateway installation. This version introduces new and enhanced functionality as well as several bug fixes. .NET 10 support Direct Access gateway now runs on .NET 10. Previous versions ran on .NET 8, which reaches end of support on November 10, 2026. Scheduling benchmark tests (introduced on November 11, 2026) You can now schedule Direct Access gateway benchmark tests to run hourly, daily, weekly, or monthly, and view and manage the schedules from the Schedules sub-tab. For more information, see Viewing and running benchmark tests. More ODBC connectors supported in containerized deployments The Direct Access gateway container image now includes ODBC drivers for Amazon Redshift, Snowflake, Databricks, Google BigQuery, and Amazon Athena. For setup instructions, see Setting up Qlik Data Gateway - Direct Access in a container. Chunks recovery enabled by default The default value of the RELOAD_CACHE_MEMORY_MB property (Memory for cached data chunks) has changed from 0 to 100, so chunks recovery is now enabled by default. When you upgrade, installations that still use the previous default value of 0 are migrated to 100. For configuration details, see Memory for cached data chunks. Reload WebSocket recovery When chunk recovery is enabled, the gateway can recover a table header if a reload WebSocket connection drops before Qlik Cloud receives it. This helps to prevent reload failures such as: Value cannot be null. (Parameter 'data') Protocol message end-group tag did not match expected tag. Logging options renamed from METRICS to TRACE The AGENT_LOG_OPTIONS values METRICS_LOADREQUEST and METRICS_RELAYCOMMAND have been renamed to TRACE_LOADREQUEST and TRACE_RELAYCOMMAND. The METRICS_* names are deprecated but still accepted. If you

Amazon Bedrock AgentCore Memory helps AI agents maintain context across sessions through persistent short-term and long-term memory. Because memory content is automatically retrieved and injected into future conversations, the integrity of what gets written to memory directly shapes future agent behavior, which makes the write path a valuable control point to strengthen. AgentCore already provides […]

The built-in R and Python visuals in Power BI will retire in May 2027. What changes This retirement applies only to R and Python visuals in interactive Power BI reports. Python scripts used in Power Query to populate tables are not affected. Power BI service: At retirement, all R and Python visuals will stop working in published reports, including those published before retirement. R-powered custom visuals: Custom visuals that use R will also no longer work at retirement. Older versions of Power BI Desktop: These visuals will continue to work locally using R and Python installed on your machine. Future versions of Power BI Desktop: After retirement, R and Python visuals will be removed, so you won't be able to add them to reports. What you need to do No migration within your report will recreate your exact R or Python visual. You must replace it with a different visualization before retirement. Start reviewing your reports for R and Python visuals and communicate within your organization that reports using them will be impacted. Preserve their scripts and dependencies. If you use Power BI project (PBIP) format and Git-integrated workspaces, search your report definitions in source control to find these visuals across multiple reports. Look for the visualType values pythonVisual for Python visuals and scriptVisual for R visuals. Keep the visualization in your report. Rebuild it with a built-in Power BI visual or an existing custom visual. AI coding tools can also help you create a visual with the Power BI custom visuals software development kit (SDK). The Power BI roadmap includes planned HTML and Vega core visuals for more flexible report visualizations. Check the roadmap for planned availability. Recreate the analysis or visualization outside the report. Explore these alternatives: Fabric notebooks: Recreate the analysis and visualization in a notebook. Scripts may need changes for supported languages, libraries, and data access. Fabric Apps: Explore this option fo

You can now use tokens from AWS Identity and Access Management (IAM) outbound federation to assume roles in other Amazon Web Services (AWS) partitions. When your workloads run across AWS partitions such as Commercial, China, GovCloud (US), and European Sovereign Cloud, a workload in one partition might need to access resources or communicate with AWS […]

November 9, 2026 High availability and load balancing for Direct Access Gateways We will now allow to bring Direct Access Gateways with identical configurations into pools. Using a pool when configuring a data connection will load balance and failover across the Direct Access Gateways in the pool. For reloads, the least-loaded available gateway is selected based on CPU, memory, and concurrent reloads. If the selected gateway cannot accept the reload, the next available gateway is tried automatically. No gateway software upgrade is required. Configuring load balancing with gateway pools Some functionality may be rolled out in phases and not immediately available in all regions.

AI agents are only as trustworthy as the context they work with. Today, we are excited to announce that the Collibra Model Context Protocol (MCP) server is available on Snowflake Marketplace and integrated with Snowflake’s public preview AI Gateway MCP & Tools app, making it easier than ever for Snowflake customers to bring governed business definitions, lineage, classifications, quality measures, semantics and data contracts directly into Snowflake CoWork and Snowflake CoCo. This launch…

What's new in Microsoft Fabric. Summarize this article for me In this article This article is continuously updated with preview/general availability status of Microsoft Fabric. To stay up to date with the latest news and features, see the Microsoft Fabric Updates Blog... Learning Paths for Fabric Get started with Microsoft Fabric End-to-end.

Study Guide for Exam DP-700: Implementing Data Engineering Solutions Using Microsoft Fabric Summarize this article for me In this article Tip A new Practice Assessment experience is available on AI Skills Navigator that more closely aligns with the exam experience. Purpose of this document This study guide should help you understand what to expect.

Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric Summarize this article for me In this article Tip A new Practice Assessment experience is available on AI Skills Navigator that more closely aligns with the exam experience. To try it, SIGN IN to AI Skills Navigator at Microsoft Ce... Purpose of this document This.

Workspace outbound access protection (OAP) helps you control where data in a Microsoft Fabric workspace can flow. The initial preview for Power BI reports, launched in June, required a report in an OAP-protected workspace to use a semantic model in that same workspace. Customer feedback highlighted that this was too restrictive. With this improvement, a report can now query a semantic model in another workspace provided a workspace administrator explicitly allowed that destination through an OAP exception policy. Apply exception policies to allow report-to-model connections OAP continues to block outbound report-to-model connections by default. When a report opens, Power BI evaluates the report workspace policy before it renders the report in the browser. If the destination workspace is covered by an applicable exception, the report can connect to its semantic model. Otherwise, Power BI blocks the connection, and the report does not open. The report honors the policy that a workspace admin configured for the semantic model destination. You don't configure an exception on each report, and you don't need to move the report and semantic model into the same workspace. The OAP exception policy supports a common Power BI architecture: a central team manages shared semantic models in one workspace, while business teams build reports in separate workspaces. You can preserve that separation of ownership without removing the outbound boundary from the report workspace. See the improvement in action The following example shows a report stored in an OAP-protected workspace, and its semantic model is stored in another workspace. First, the report does not have an applicable exception policy. Power BI blocks the cross-workspace connection and displays the following error because the report can't access its semantic model. Figure: OAP continues to block outbound report-to-model connections by default. Next, a workspace admin creates an exception policy for the semantic model works

SAP Business Data Cloud (BDC) is reshaping how businesses manage and use data for analytics and AI. In this article, we explore the key concepts behind BDC, including its architecture and Data Products, and share lessons from rewiring existing SAP Datasphere tenants on SAP BTP to BDC. Whether you’re new to BDC or supporting customers through the transition, this guide offers a practical starting point.

November 5, 2026 Bookmarks in Direct Query applications You can now create, update, and apply bookmarks in Direct Query applications. Save your selections and return to them later, in the same way as in in-memory applications. Bookmark limitations Accessing cloud databases directly with Direct Query Some functionality may be rolled out in phases and not immediately available in all regions.

SAP S/4HANA Cloud Private Editionでは、システムが稼働しているOSへ定期的にOSパッチを適用し、OSレベルのセキュリティを維持することを推奨しております。OSパッチ適用時にご利用いただくService Request（以下、SR）テンプレートは、次の2種類でございます。なお、現時点では下記のSRテンプレートはPacemakerをご利用のシステムに対応しておりません。該当するシステムにつきましては、Assisted Service Requestをご利用いただきますようお願いいたします。Apply latest Security Patch to OS (Linux, Windows)Apply Other Security Patch to OS (Linux)本記事では、未適用のOSパッチを確認する方法と、SRによるOSパッチ適用の依頼手順(Pacemaker構成のシステムを除く)についてご説明いたします。 未適用のOSパッチの確認 SAP for Meにログイン後、”Services & Support” -> ”Private Cloud Workspace” を開き、"Risk Management"の"Systems with Missing Patches"をクリックいたします。Operating Systemタブでは、未適用のOSパッチが存在するSIDが以下のように表示されます。 また、該当SIDの未適用OSパッチ一覧を以下のように確認できます。ここで重要となるのが"Requires Downtime"列の値でございます。Yes: お客様にてSRを起票いただき、該当OSパッチを適用する必要がございます。No（patched automatically）: ダウンタイムなしでオンライン適用が可能なOSパッチは月次で自動適用されるため、お客様側での対応は不要でございます。該当OSパッチのリリースから約3ヶ月以内に適用することが、現時点でのRISEとしての推奨でございます。OSパッチの適用はダウンタイムを伴うSR依頼となりますので、あらかじめOSパッチの適用方針をご検討いただき、定期的に適用いただくことをおすすめいたします。 OSパッチ適用の依頼 RISE上のシステムは基本的に開発／検証／本番の構成となっており、Landscape全体でOSパッチレベルの一貫性を維持することを推奨いたします。以下では、S/4HANAシステムの開発／検証／本番環境を例にご説明いたします。適用の流れは次のとおりでございます。開発環境のAPサーバとSAP HANAサーバに最新のOSパッチを適用いたします。検証／本番環境は、開発環境を参照システムとしてOSパッチを適用いたします。これは、Landscape全体でOSパッチレベルを一致させるためでございます。 1. 最新のOSパッチ適用（開発環境） OSに最新パッチを適用する際のSRテンプレートは、以下のとおりでございます。SRテンプレート: Apply latest Security Patch to OS (Linux, Windows)該当SRテンプレートの免責事項に記載されておりますとおり、SRの作成時にはHANAデータベースのSIDを選択いただけますようお願いいたします。HANAデータベース上で稼働するシステムの場合、本サービスリクエストの作成時にHANAデータベースシステムのSIDを選択してください。この選択により、関連するすべてのアプリケーションサーバーおよびデータベースサーバーへのパッチ適用が確実に行われます。この場合、アプリケーションシステムのSIDを使用して追加のサービスリクエストを作成する必要はありません。以下は、開発環境のHANAデータベースのSIDを選択してからSRを作成する例でございます。主なパラメータは次のとおりでございます。Creating with Advance Scheduling: NO（SAP for Meにて未適用のOSセキュリティパッチが存在するため）Set system message (for ABAP systems): ABAPシステムにおいて、メンテナンス期間中に表示するシステムメッセージを設定するかどうかを指定する項目でございます。なお、Creating with Advance Schedulingパラメータは、SAP for Me上で未適用OSパッチの有無をご確認いただいたうえで選択いただけますようお願いいたします。本作業に必要なダウンタイムは、SRサービス説明に以下のとおり記載されておりますとおり、”スケジュール実行"ステップにてご確認いただけますようお願いいたします。記載されている「推定標準所要時間」

There's a reason SAP will be ahead of the curve when AI agents become the norm in enterprise software — and it starts with a decision made back in November 2024.That's when SAP began the journey of delivering Data Products across its Business Data Cloud (BDC). At the time, it may have looked like a data engineering initiative. Looking back, it was the foundation for something far larger: Ontology.

Introduction As supply chain planning grows more data-intensive, the ability to connect planning systems with a unified, enterprise-wide data foundation becomes a competitive advantage. SAP addresses this with the integration between SAP Integrated Business Planning (SAP IBP) and SAP Datasphere of SAP Business Data Cloud (SAP BDC). SAP Datasphere enables structured storage and harmonization of master and transactional data from SAP and non-SAP sources via an integrated data lake. This integration extends to SAP Integrated Business Planning (SAP IBP), allowing planning data to flow from IBP into Datasphere embedded Object Store for further processing and analytics.The integration pipeline works as follows: application jobs in SAP IBP extract data and push it to the Datasphere data lake, from where it is merged and structured in SAP Datasphere. Supported data types include key figures (time-series only), master data, detailed pegging data, forecast data ( including CBP ), gating factor data, and stock data. This blog walks through the setup and steps to integrate IBP key figures with SAP Datasphere embedded object store , create a custom data product based on IBP data and share with SAP Databricks using zero copy approach .One key prerequisite : The integration requires the SAP IBP system to run on SAP HANA CloudDisclaimer: This blog is intended for informational and knowledge-sharing purposes only. The configurations described are demonstrated in a SAP lab environment and may not reflect production-ready setups. Please refer to official SAP IBP and SAP Datasphere help documentation 1. Getting IBP and DSP system ready: SAP IBP - Datasphere connectionThe connection setup between a SAP IBP and SAP Datasphere tenant happens through a specific formation named “Integration with SAP Datasphere” as documented on the help portal: How to Connect SAP Integrated Business Planning to SAP Datasphere | SAP Help PortalYou need to execute the following steps for creating a new format

Reliable digital systems are increasingly essential to defense readiness.

November 4, 2026 A refreshed toolbar keeps search and selections visible while you edit The toolbar above sheets has a new look. When you edit a sheet, search and selections now stay visible next to the edit controls, so you no longer need to leave edit mode to check or change your selections. The Edit button also has a refreshed appearance. The toolbar adapts its button labels to the space available, so it works better in smaller windows. All existing actions, visibility rules, and permissions work as before. Working with sheets Some functionality may be rolled out in phases and not immediately available in all regions.

Security incidents are on the rise, and, as a result, security frameworks like the Cybersecurity Maturity Model Certification (CMMC) are gaining more general attention. There are five significant reasons for this focus: Supply chain attacks are now the costliest and slowest to contain Cyber insurers are underwriting around it Legal exposure now reaches beyond direct contractors Other federal agencies are eyeing the same model Early movers gain a competitive and M&A advantage I’ll…

Key date: 31 December 2026. SAP BusinessObjects BI Platform 4.3 reaches End of Mainstream Maintenance on 31 December 2026. SAP has a clear upgrade path and the resources to support your move.If you are running SAP BI Platform 4.3 or lower, now is the right time to plan your upgrade to SAP BusinessObjects BI 2025 or BI 2027. Where BI 4.3 support stands todayAs of October 2026, SAP is focusing its BI 4.3 engineering on what matters most. According to KBA 3763590, from October through December 2026, High and Very-High priority product issues are being prioritised for 4.3 SP05 patches. Medium and low priority fixes are being directed into future BI 202x releases — where customers on the latest platform will benefit from them first.On 31 December 2026, Mainstream Maintenance ends. From 1 January 2027, BI Platform 4.3 enters Customer-Specific Maintenance (CSM) — a structured, time-limited transition phase. SAP BusinessObjects BI Platform Maintenance Roadmap Where BI 4.3 support stands todayAs of October 2026, SAP is focusing its BI 4.3 engineering on what matters most. According to KBA 3763590, from October through December 2026, High and Very-High priority product issues are being prioritised for 4.3 SP05 patches. Medium and low priority fixes are being directed into future BI 202x releases — where customers on the latest platform will benefit from them first.On 31 December 2026, Mainstream Maintenance ends. From 1 January 2027, BI Platform 4.3 enters Customer-Specific Maintenance (CSM) — a structured, time-limited transition phase. What Customer-Specific Maintenance (CSM) means in practiceCSM is not the same as Mainstream Maintenance. It is important to understand the difference — because it directly affects what you get when something goes wrong. As discussed in the KBA 3486924, here is what CSM includes:Security coverage — first 12 months: SAP will deliver security fixes on BI 4.3 SP05 for Very High and High severity vulnerabilities (CVSS >= 7.0) during the first 12 m

Ask a data governance team which critical data elements are missing an owner, and listen for the pause. Gartner warns that 60% of organizations will fail to get value from their AI initiatives due to poor governance. Yet the policies are known, standards written down and the metadata needed to test them is already in the catalog. What’s missing is the check: something that continuously compares governance standards against reality without a steward having to verify each asset by hand. What’s…

The /api/v1/webhooks endpoints are deprecated. Migrate to the new /api/core/webhooks endpoints.

OneLake is included with every Microsoft Fabric tenant and is designed to be the single place for all your analytics data.

November 2, 2026 Allow MCP access for everyone in one step Connecting an AI assistant to Qlik Cloud is now simpler, and it starts with one setting. Set MCP to Allowed in default permissions and everyone in your tenant can use the Qlik MCP server. There's no custom role to build, assign, or keep up to date. Why it's worth doing: One setting instead of a role. Switch MCP on once and people can connect, so a missed role assignment no longer stops anyone connecting. Still flexible. To limit MCP to selected users or groups, leave it Not allowed in default permissions and allow it through a custom role instead. Allowing MCP for users doesn't replace approval of the MCP client. A tenant administrator still approves the first connection from a new client. Allowing user access to Qlik MCP server Some functionality may be rolled out in phases and not immediately available in all regions.

New dbt platform release notes for November 2026. Visit the page to see what's new, updated, and fixed.

SAP provides the solutions necessary to govern, control, and innovate now and into the future.

Fabric data engineering agent (Project Osmos) is an autonomous data engineering capability for Microsoft Fabric that plans, executes, and validates work against authorized lakehouse resources.

Enterprises can build AI agents act with greater autonomy and confidence.

With comprehensive AI tools and the SAP Business AI Platform, developers, customers, and partners can build the Autonomous Enterprise.

Your ERP is where the business actually happens: every invoice, order, and payment. Yet for most organizations, that data sits outside the catalog, understood by a handful of long-tenured experts and documented or governed nowhere. What’s new: SAP S/4HANA integration SAP S/4HANA holds the core business data (finance, sales, procurement, and operations) for a large share of the world’s enterprises. The new SAP S/4HANA integration brings that system’s metadata into Collibra Platform, so SAP sits…

Every Collibra AI feature has, until now, come from the same place: Collibra's own managed models. For an enterprise that already runs its own vetted, compliant AI infrastructure, that’s a second security review nobody asked for. Bring your own model (BYOM) removes the coupling: the AI feature stays, the model underneath it is now yours to choose and control. What’s new: Bring your own model (BYOM) BYOM lets a Collibra customer register their own AI model provider and have Collibra's AI…

AI agents are moving into production faster than organizations can govern them. And “operational trust” is more critical than ever. According to Deloitte's 2026 State of AI in the Enterprise survey of 3,235 business and IT leaders across 24 countries, 74% of organizations expect to use AI agents at least moderately by 2027, up from 23% today. Yet only 21% have a mature governance model in place for agentic AI; in other words, roughly 80% lack capabilities like clear agent decision…

Sigma shows how it manages its semantic layer as version-controlled code across thousands of dbt models.

KOHO explains why AI self-serve analytics needs a strong data culture and a governed dbt foundation first.

A plausible AI revenue query was $3.5 million wrong. AWS explains how business context helps prevent those errors.

DoorDash used a dbt-defined semantic layer to scale analytics from pilot to thousands of merchant users in weeks.

Changelog OpenMetadata 2.0.7 is a maintenance release that improves connector reliability and search consistency. It also strengthens governance workflows and access controls. The release fixes user interface (UI) issues across activity feeds, bulk editing, lineage, and intake forms. It also includes security updates for server, ingestion, and frontend dependencies. 🔌 Connectors and Ingestion This release includes the following connector and ingestion fixes: Google Cloud Pub/Sub test connections ignored service account impersonation #35831: Publisher, subscriber, and schema clients now use the configured impersonated identity instead of falling back to application default credentials. Microsoft SQL Server connection tests could time out on large schemas #35906: Table and view checks now use limited catalog probes instead of sorting the entire catalog. Unity Catalog lineage dropped upstream tables ingested under another service #35919: Cross-database lineage now searches configured services in order and reports unresolved upstream assets as warnings. Hex lineage appeared as manually created lineage #35946: Hex table-to-dashboard edges now record DashboardLineage as their source. dbt Cloud test results were missing when dbt test wasn't the final run step #35947: Ingestion now reads run_results.json from every run step and keeps the latest result for each test. Power BI reports were ingested without their pages #36062: Each report page is now ingested as a chart under its report without requiring an additional API request. Tableau ingestion could delete data models while the Metadata API was rebuilding #36067: Runs that reconcile data models now stop safely during a Tableau backfill instead of treating partial results as deleted data. Power BI BigQuery lineage failed when SQL contained comment markers inside strings #36232: Native-query extraction now preserves SQL literals, line breaks, and comments for accurate parsing. IOMETE and Teradata installations could resolve

Seit dem Update vom 26. Oktober können Feature-Policy-Regeln eigene Fehlertexte mitgeben. Wird eine Anweisung blockiert, kann der Text die Ursache und zulässige Alternativen erklären. Das erleichtert die Umsetzung nachvollziehbarer Governance-Regeln.

If you haven't already, check out FabCon and SQLCon 2026 in Barcelona: Building the data foundation for Microsoft Copilot and Agents for a complete look at all of our FabCon Europe announcements across Fabric and Microsoft Databases. Advancing an open, interoperable semantic foundation for AI-powered analytics Today, Microsoft is pleased to affirm our commitment to Apache Ossie (incubating), a community-led effort to establish a vendor-neutral standard for exchanging semantic metadata across analytics, AI, and BI platforms. This has been a close collaboration with Snowflake, who helped found Ossie. We have heard loud and clear that customers want to enable platform-agnostic agentic experiences. Shared semantic context must be defined once and reused across platforms, enabling agentic consumption at scale, maximizing the reach of AI-powered analytics, and empowering users to leverage the best agent for the job. Power BI semantic models are key enablers of AI-powered analysis, providing humans and AI agents with governed semantic context to answer data questions quickly and confidently. Power BI semantic models have more than 38 million monthly active users and are adopted by 94% of Fortune 500 companies. Microsoft was also recognized as a Leader and Outperformer in the GigaOm’s radar for semantic layers & metric stores in 2025. We’re now bringing this wealth of experience to Apache Ossie, and we are excited to work with Snowflake and other partners to help make the project successful. Why Apache Ossie matters Until now, semantic definitions were often recreated across platforms, introducing complexity, inconsistency, and management overhead. Apache Ossie addresses this with a common specification that tools can read and write. Its goal is straightforward but consequential: define a semantic layer once and make it portable across the data and AI ecosystem, while preserving semantic meaning and governance for agentic consumption. Collaboration with Snowflake Snowflake

Most organizations have moved past asking whether to use AI. The harder question now is whether they can prove the AI they run is doing what it should. That gap is why we are writing this together. Collibra announced the acquisition of Trail ML, the Munich-based AI governance company founded in 2023 by Anna Spitznagel, Nikolaus Pinger and Sven Hölzel. Trail's capabilities will become part of Collibra AI Command Center. Here is what that means, and why we believe it matters for anyone…

Changelog OpenMetadata 2.0.6 is a maintenance release that makes metadata ingestion more accurate, upgrades safer, and Data Quality results easier to trust. It improves search, lineage, governance workflows, sign-in and role synchronization, while fixing several navigation and product-tour issues in the user interface (UI). The release also strengthens multi-pod deployments and addresses security vulnerabilities in application dependencies and content handling. 🔌 Connectors and Ingestion This release includes the following connector and ingestion fixes: CockroachDB partition details could be incomplete or duplicated #34894: Tables with multiple partition columns now show the correct columns, without duplicates or values accidentally taken from a similarly named table in another database. Some dbt snapshot lineage links were missing #34930: Ingestion now keeps the upstream table it already found and uses that exact table when creating lineage, avoiding failed second lookups. Omni dashboards could be ingested without their owners or lineage #35259: Omni ingestion now connects dashboards to the correct owners and upstream data assets. Teradata query usage and lineage were missing #35258: The connector now reads Teradata's database query log (DBQL) to show which tables are used and how data moves between them. Auto-classification ignored sample budgets above 50 rows #35492: SQL and NoSQL samplers now honor configured budgets up to 1,000 rows while continuing to store at most 50 sample rows. dbt manifest catalogs didn't match their OpenMetadata databases #35599: A new catalogMapping option resolves tables and compiled-SQL lineage when engine catalogs and ingested database names differ. MySQL and MariaDB views disappeared when the ingestion user lacked SELECT #35619: View columns fall back to information_schema when DESCRIBE is denied, allowing metadata-only ingestion with SHOW VIEW and REFERENCES privileges. Athena views were duplicated or ingested when includeViews was

As the Power BI, Microsoft Fabric, data, and analytics community gathers in Barcelona for the European Microsoft Fabric + SQL Community Conference, this month is packed with news. From new Copilot and AI experiences to reporting, modeling, data connectivity, developer enhancements, and mobile updates, we're sharing the latest innovations designed to help you build, analyze, and act on your data more effectively. Whether you're joining us in person or following along from afar, this roundup is your guide to everything new across Power BI this month. Table of Contents General Deprecation of Old File Picker experience in Power BI Desktop Copilot and AI Power BI Agentic Experiences (Generally Available) Microsoft 365 Copilot is coming to Power BI (Preview) Power BI data answers in Microsoft Copilot Chat (Generally Available) Fabric IQ Plugin in Microsoft 365 Copilot Cowork (Generally Available) Build Fabric Apps more easily in GitHub Copilot App (Preview) Enhanced Report Consumption Copilot with Fabric IQ New control for Copilot access to semantic models Remote Power BI Authoring MCP Server (Preview) Reporting New Report Ribbon on the web (Preview) Live Visual Sharing in Outlook on the web (Preview) More control and context in Power BI Home (Generally Available) Set and reset column widths in table and matrix (Generally Available) Visual calculations for small multiples (Generally Available) Design ribbon for Power BI reports (Generally Available) Slider color options for Slicer visual (Generally Available) Translytical task flows show user date function (UDF) information in button action (Generally Available) Format pane headers stay visible as you scroll (Generally Available) Pick the axis initial scroll position (Generally Available) Azure Maps geocoding improvements (Generally Available) On-object interaction improvements (Preview) Support for Fabric Apps in Org apps (Coming Soon) Changes to Power BI subscription email delivery Increased Export to Excel row limits (

This release is part of the broader ODBC-to-ADBC transition for Power Query connectors described in the Transition from ODBC to ADBC drivers in Power BI and Microsoft Fabric documentation. What’s new Existing connections in your semantic models, dataflows, and paginated reports continue to work unchanged — the migration path is currently opt-in per connection, per workspace, or per tenant. Highlights: Improved security - ADBC drivers incorporate security enhancements, such as memory safety and garbage collection. No changes to existing behavior - This transition moves away from the embedded ODBC drivers that ship with Power BI. It does not change behavior for the ODBC connector when you use a separately installed ODBC driver. Efficient large dataset support - ADBC provides a set of standard interfaces for interacting with Arrow data, which is especially efficient at fetching large datasets with minimal overhead and no serialization or copying. Supported authentication - Databricks ADBC supports OAuth (user), OAuth (M2M / Client Credentials), and Microsoft Entra ID. Connectors covered by this release ConnectorPrevious driverNew driver (GA)DatabricksSimba Spark ODBCDatabricks ADBCAzure DatabricksSimba Spark ODBCDatabricks ADBC How to try it You can test the ADBC driver now. We suggest you start soon to help ensure the eventual deprecation of ODBC and migration to ADBC runs smoothly. There are three ways to begin validating ADBC, ordered by how much control you want. 1. Opt in per connection using Implementation="2.0" The fastest way to compare ADBC and ODBC for a specific report or dataset. Add the Implementation="2.0" option to the source step in your Power Query expression: Source = Databricks.Catalogs("adb-1234567890.1.azuredatabricks.net", "/sql/1.0/warehouses/", [Implementation="2.0"]) Refresh the dataset and compare row counts, column types, and totals against your production baseline. If you want to force a side-by-side comparison against ODBC, use Implementati

The isTestRun and isArchived properties in the run ended event are deprecated. Use testRun and archived.

qlik-cli release with new commands, deprecated automation commands, and a first-connection 403 fix

The Direct Access Agents API now includes an endpoint for retrieving gateway configuration with environment and product version metadata.

The schedules[].interval property in the Workflows Automations API is deprecated. Use schedules[].recurrence to configure automation schedules.

Changelog OpenMetadata 2.0.5 is a maintenance release focused on connector and lineage reliability. It restores metadata affected by 2.0 ingestion regressions and protects user-managed metadata during bot ingestion. It also improves several user interface (UI) paths and updates Jackson dependencies for two security vulnerabilities. 🔌 Connectors and Ingestion This release includes the following connector and ingestion fixes: Redshift datashare databases on consumer clusters weren't ingested #34381: The connector now identifies non-local databases and reads their schemas and tables through Redshift's cross-database catalog views. It also reads their columns and descriptions. Oracle foreign-key constraints disappeared when identifier casing differed #34415: Referred table names now keep the database's original casing, so OpenMetadata can resolve and ingest the constraints. Mode report queries omitted columns, column lineage, and owners #34733: Mode ingestion now extracts query columns, builds column-level lineage, and assigns report creators as owners when their email matches an OpenMetadata user. 🔀 Lineage This release includes the following lineage fixes: Databricks dropped lineage through jobs when reads and writes appeared in separate records #34732: Databricks now preserves lineage when a job reads and writes tables in separate query-history records. Entity-relationship diagrams reversed foreign-key cardinality #34812: The diagrams now show the correct one-to-many relationship for foreign keys after reindexing. 🛡️ Data Governance and Quality This release includes the following governance and data quality fixes: Upgrading from OpenMetadata 2.0 could remove ingestion-managed metadata #34836: The 2.0.5 migration restores affected descriptions, display names, and retention periods. It also restores table constraints and tags. Ingestion bots now preserve existing descriptions when a source provides an empty value. Bot ingestion overwrote user-managed tags, owners, and

Die offizielle Release-Seite nennt den 8. Oktober 2026 für Version 2.0.5. Redaktionell geprüft am 15.10.2026. Vor Upgrade Änderungen, Kompatibilität und betroffene Connectoren anhand der Release Notes prüfen.

Changelog OpenMetadata 2.0.4 is a maintenance release focused on connector reliability across AWS Glue, Snowflake, SQL Server, and Datalake, correctness fixes in search scoping and Explore counts, data quality and profiler fixes, and a safer bot bulk-import path. 🔌 Connectors & Ingestion AWS Glue: table parameters weren't captured #34269: Glue table parameters are now ingested as custom properties. SQL Server: table synonyms weren't ingested #33415: SQL Server synonyms are now ingested as table aliases and allowed through the entity patch. Snowflake: the overflow retry skipped subclassed sum, mean, and standard-deviation metrics #34396: The overflow-retry fallback now keeps subclassed Sum, Mean, and StdDev metrics instead of dropping them. Datalake: MF4 file reads failed after a canmatrix update #34419: Pins canmatrix below 1.3 so asammdf imports again and MF4 reads work. Ingestion could break under SQLAlchemy 2.1 #34259: Caps SQLAlchemy below 2.1 to keep ingestion working. 📊 Data Quality A test case's status followed the last result written or deleted, not the newest one #34285: A late older result, or deleting the newest result, could overwrite the status and even auto-resolve a newer failure's incident. Status now always reflects the newest result. The Test Definition "Supported Services" picker listed the Dbt and QueryLog pseudo-types #34624: These have no connection schema, so a test definition restricted to one silently vanished from the Add Test Case picker. Both are now filtered out. Setting a 0% profile sample silently discarded the Profiler Settings config #34625: A 0% or 0-row sample saved nothing while still showing a success toast, and a stored 0 would force a full-table scan. A non-zero sample is now required. 🔀 Lineage Upstream data quality failures were missed once an instance had more than 100 test cases #34286: The upstream-failure check kept only the 100 busiest test cases before filtering, dropping failing upstream tables. It now uses one exact,

The Automation connections, Automation connectors, and Automations REST APIs are deprecated and will be removed on or after November 30, 2027. Migrate to the equivalent APIs in the Workflows namespace before deprecation ends.

New dbt platform release notes for October 2026. Visit the page to see what's new, updated, and fixed.

Das offizielle Oktober-Update nennt die allgemeine Verfügbarkeit der Überwachung sensibler Daten bei FTP- und SFTP-Übertragungen durch Endpoint DLP. Die gewünschten Aktivitäten und Einschränkungen müssen in den Richtlinien konfiguriert werden. Die Quelle nennt einen Monatsstand, keinen genauen Veröffentlichungstag.

Changelog OpenMetadata 2.0.3 is a maintenance release focused on connector reliability across Snowflake, BigQuery, MSSQL, Tableau, Looker, and DynamoDB, two data-integrity migrations, several security fixes including a certificate-verification bypass and a SQL-injection fix, and correctness fixes across search, lineage, and data quality. 🔌 Connectors & Ingestion Airflow: pipelines showed a stale run instead of the latest one #33153: Pipeline status now selects the latest run correctly. Unity Catalog: reading lineage and its SQL required two separate queries #33141: Native lineage and its SQL are now read together in one streamed query. Unity Catalog: path-based lineage to external tables didn't resolve #32565: Path-based lineage to external tables now resolves correctly. Mode: report discovery pagination could stop before reaching the last page #33189: Fixes the pagination termination condition, so reports past the first page are no longer missed. Looker: lineage and project filtering were incomplete #32531: Improves both lineage resolution and project filtering. REST: OpenAPI Path Item fields weren't handled #32958: Path Item-level fields are now parsed correctly. DynamoDB: no way to see a table's primary key #33207: Extracts the partition key and sort key as the primary key. PostgreSQL and Greenplum: materialized views weren't ingested #31549: Materialized views are now included. dbt: 1.12+ inline metric specs failed to ingest #33535: Falls back to type_params.expr for dbt 1.12+ inline metric specs. MSSQL: descriptions were read from the wrong database, encryption wasn't honored per driver, and query timeouts were unbounded #32574: All three are fixed. Snowflake: the semantic catalog cache collided across databases sharing a schema name #33926: The cache is now keyed by database, not schema alone. BigQuery: auto-detected partition windows could silently return empty samples #33221: Widens the auto-detected partition window to avoid silent empty samples. Tableau: f

Use OAuth Client ID Metadata Documents (CIMD) to simplify OAuth client configuration for Model Context Protocol (MCP) connections between AI platforms and Qlik Cloud tenants.

Changelog OpenMetadata 2.0.2 is a maintenance release focused on connector reliability across Oracle, Databricks, Snowflake, BigQuery, Clickhouse, KafkaConnect, Grafana, and StarRocks, MCP tool consolidation and hardening, a governance rollout that restricts direct team membership to Group teams, several XSS and access-control fixes, and a broad security dependency cleanup. 🔌 Connectors & Ingestion REST/OpenAPI: array properties with an inline object schema emitted a synthetic item: UNKNOWN child #32007: Expands the item's properties with their OpenMetadata types and descriptions instead. REST/OpenAPI: API collections and endpoints could ingest out of order #31852: Persists all API collections before ingesting endpoints, so endpoints no longer reference a collection that hasn't been committed yet. KafkaConnect: topic-namespace membership was emitted as lineage #32207: Topic-namespace membership is no longer misreported as a lineage edge. KafkaConnect: Confluent Cloud managed connectors resolved no topics, and telemetry credential failures logged the same line whatever the cause #32512, #32999: Topics are now resolved from Confluent's Telemetry API, and the warning distinguishes a 401 from a 403 with Confluent's own error detail. pygtrie 2.6 broke import airflow on Python 3.10 #32378: Caps pygtrie below 2.6 so ingestion imports Airflow correctly. Databricks: SQL-defined DLT pipelines had no lineage extracted #31654: Lineage is now extracted from SQL-defined DLT pipelines. Databricks: Unity Catalog access rows incorrectly linked a table to itself #32214: Legitimate access-not-derivation rows (a streaming or CDC write touching its own target table) no longer create a self-referencing lineage edge. Databricks: large workspaces failed to ingest all jobs #33204: Jobs are now paginated by token, so large workspaces ingest in full. Clickhouse: materialized views had no downstream lineage #32314: Downstream lineage is now created for materialized views. Oracle: query history

The semantic model settings pane is becoming the default way to configure semantic models in the Power BI service. The pane opens alongside your workspace, so you can review and change settings without leaving the current page. It provides the same settings as the full semantic model settings page while keeping the surrounding context visible. The classic settings page remains available during this transition, but it no longer opens first. This change builds on the settings pane preview and provides a more focused experience for managing semantic models.Why it mattersThe pane helps you stay in context while you manage a semantic model. It opens on the right side of the browser window and keeps your workspace visible. Settings are organized into expandable sections and tabs, including refresh, data access, performance, and OneDrive and SharePoint. You can use the search box at the top of the pane to find a setting across all sections and tabs. For example, enter "re" and select View refresh history to go directly to refresh history instead of opening sections one at a time. This organization is especially useful for semantic models with several connection, refresh, or performance settings. You can review a setting, make a change, and return to your work without navigating to a separate page.What's changingThe settings pane was an optional experience earlier in the preview. As this change rolls out, the pane opens by default when you open settings for a semantic model. The rollout is gradual, so the timing can vary by environment.The pane includes the settings available on the full settings page. Its main area groups related settings into expandable sections, and the tabs at the top organize settings by category. Use the search box when you know the setting you want or browse the sections to review related options together.If you prefer the full-page experience, select Open settings page at the bottom of the pane. The classic page opens in the same browser window. Thi

Changelog OpenMetadata 1.13.6 is a maintenance release focused on connector reliability across Kafka Connect, Tableau, Snowflake, BigQuery, Athena, Airflow, Looker, Grafana, Mode and Doris, correct change events and alerting for ingestion-driven updates, access-control fixes on the follower and glossary bulk-asset endpoints, and a repair of the Data Insights data-asset charts. It also adds SQL Server synonym ingestion. 🔌 Connectors & Ingestion SQL Server synonyms were not ingested #31829: Synonyms are now ingested as Table.aliases, indexed for search, resolved to their canonical table when building lineage, and shown in a read-only Aliases widget. The ingestion bot blanked a user-curated displayName #32955: A bot PUT carrying displayName=null cleared a name curated in the UI because the deny rule never fired; updateDisplayName() is now policy-aware. KafkaConnect: Confluent Cloud managed connectors resolved no topics #32512: Topics are now resolved from Confluent's Telemetry API, falling back to the configured names on any failure. KafkaConnect: telemetry credential failures logged the same line whatever the cause #32999: The warning now distinguishes a 401 (the key must change) from a 403 (a role is missing) and carries Confluent's own error detail. Tableau: source tables hidden from the ingestion account produced silently empty lineage #31441: A GetSourceTables test-connection step now reports withheld source tables before an ingestion is run. Tableau: auto-titled mirror columns were ingested as duplicates #32301: Mirror columns are now collapsed. Looker: data-model lineage failed on the first run #32367: Data models are read back before the sink's bulk buffer has flushed; the stage now commits every data model before anything looks it up. Mode: report query metadata wasn't ingested #32533: Mode report queries are now ingested as data model metadata. Grafana: panels nested inside collapsed rows were skipped #31611: Collapsed-row panels are flattened before chart an

In December, we announced the retirement of Power BI Q&A, our legacy natural-language querying experience, with retirement planned for December 2026. To give current Q&A user's additional time to assess their dependencies and transition to newer Copilot-powered solutions, we’re extending the retirement date to February 2027. This post recaps the affected experiences and provides updates on Copilot capacity availability, embedded scenarios, and sovereign clouds. Recap of the January announcement What is being deprecated? The retirement applies to both the end-user Q&A experiences, and the associated Q&A configuration tools. Retiring experience Recommended alternative Q&A in reports Copilot with Power BI reports Q&A on a dashboard Copilot standalone experience Q&A virtual analyst in mobile app Copilot in Power BI Mobile Q&A in Power BI embedded analytics Copilot for SaaS scenarios. For embedded PaaS scenarios, refer to the updates later in this post. Q&A Setup Prep Data for AI What happens at retirement? Beginning in February 2027, Q&A will no longer work in Power BI. The Q&A visual will be removed, and existing reports that contain Q&A visuals will display an error in place of the visual. Updates since December Copilot availability across capacities One of the most common questions following the December announcement was how customers relying primarily on Power BI Pro licenses could adopt Copilot capabilities after the retirement of Q&A. Copilot is now available across all Fabric capacities starting at F2. In addition, Fabric Copilot Capacity (FCC), which was initially limited to P1/F64 and above, can now be created on F2 and higher capacities. FCC is an optional way to centralize Copilot usage for assigned users, including Pro and PPU workspaces, on one capacity. Review the current Copilot requirements. This change makes Copilot available on smaller Fabric capacities, giving organizations that previously relied on Power BI Pro licenses another path to evaluate Copil

Many enterprise Power BI semantic models use Azure Databricks as a data source. When building these models, developers and architects face an early and consequential decision: which storage mode to use. Cost, security, and ease of development and tuning all factor in — but report performance is probably the most important of them, because reports that are slow to load are one of the most common causes of end-user dissatisfaction. In practice, that decision is often made on intuition rather than evidence. To help change that, we've published a new white paper, Modern Power BI Architecture Choices for Reporting on Azure Databricks, benchmarking four ways of serving the same Delta tables to a Power BI report: Direct Lake on OneLake — over Delta tables in a Fabric lakehouse or warehouse Direct Lake on mirrored Unity Catalog tables — shortcuts, no copy DirectQuery — on a Databricks SQL warehouse Composite Model on Databricks — DirectQuery combined with Import-mode aggregations Figure: The four Power BI storage modes benchmarked to evaluate their impact on report performance and scalability. What the results suggest: there's no universal winner — but there are clear patterns. Direct Lake on OneLake performed well across the widest range of situations in this benchmark. It's highly competitive at smaller and mid-size volumes, and for the typical Power BI workload — where reports are used repeatedly throughout the day — it delivers interactive performance without extra modeling effort. At the top end of the volume curve, the picture shifts. With billions of rows, a Composite Model with aggregations was the fastest and most consistent pattern, staying sub-100ms on queries the aggregation tables can resolve. The caveat is equally clear: that advantage doesn't extend to queries that fall through to the underlying DirectQuery source, so the payoff depends on how well your aggregations match real user behavior. These are just the headline findings — the detailed results vary con

New dbt platform release notes for September 2026. Visit the page to see what's new, updated, and fixed.

New dbt platform release notes: dbt Summit 2026 announcements. Visit the page to see what's new, updated, and fixed.

New dbt platform release notes: Pre-dbt Summit. Visit the page to see what's new, updated, and fixed.

New dbt platform release notes for August 2026. Visit the page to see what's new, updated, and fixed.

New dbt platform release notes for July 2026. Visit the page to see what's new, updated, and fixed.

New dbt platform release notes for June 2026. Visit the page to see what's new, updated, and fixed.