Shortcut Transformations today silently drops corrupt rows during file-to-Delta ingestion - users see a count of rejected rows in telemetry but have no way to inspect what failed, which file it came from, or when it was rejected. It's estimated that 35% of all jobs encounter corrupt rows that are silently discarded. This feature persists corrupt rows alongside valid data in the destination Delta table with source file path and ingestion timestamp, enabling direct inspection via SQL/Power BI, downstream remediation in silver-layer pipelines, and compliance audit trails. Opt-in via a simple feature flag with zero regression for existing shortcut transformations whereas it would be supported for all new shortcut transformations by default. The feature is applicable to CSV, JSON, and Excel files
","feature_name":"Data Quality: Persist and Inspect Corrupt Rows in Shortcut Transformations","last_modified":"2026-06-16","product_id":"338c69fe-dcd6-ee11-9079-000d3a310f67","product_name":"OneLake","release_date":"Q2 2026","release_item_id":"b99d1d6c-103f-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-medallion-implementation-with-materialized-lake-views-in-fabric/5172766","distance":0.5731,"feature_description":"FMLV will introduce column-level data quality constraints within the MLV framework -- inspired by the widely adopted Deequ library -- enabling customers to define and enforce quality rules directly on their Materialized Lake Views, addressing strong demand for built-in column-level quality controls.","feature_description_html":"FMLV will introduce column-level data quality constraints within the MLV framework -- inspired by the widely adopted Deequ library -- enabling customers to define and enforce quality rules directly on their Materialized Lake Views, addressing strong demand for built-in column-level quality controls.
","feature_name":"Support Column-Level quality checks in Materialized Lake views","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"9c1813a3-c43e-f111-88b5-6045bd00f798","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"How Stibo Systems\u2019 MDM powers trusted data for analytics and AI in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/how-stibo-systems%E2%80%99-mdm-powers-trusted-data-for-analytics-and-ai-in-microsoft-fab/5172076","distance":0.5954,"feature_description":"Stibo's **DaaS workload **exposes its multi-domain Master Data Management (MDM) capabilities as a service within Microsoft Fabric, enabling customers to consume trusted master data directly inside their analytics and AI workflows. This includes product, customer, supplier, and other core enterprise domains, delivered as governed, curated datasets rather than raw exports.Product Features1. Fabric-Native Data Access via OneLake - Customers can materialize governed master data in OneLake and use the data for both BI and AI use-cases2.Configurable Data Export & Synchronization - Ability to select specific master data, denormalize data, managaging update frequecy enabling self-service data provisioning which preserving the governance model in the MDM system3.AI enriched master data - Automated product description generation, intelligent data quality recommendations and corrections. 4. Unified Analytics via Power BI - Leveraging the full Power BI co-pilot capabilities.","feature_description_html":"Stibo's **DaaS workload **exposes its multi-domain Master Data Management (MDM) capabilities as a service within Microsoft Fabric, enabling customers to consume trusted master data directly inside their analytics and AI workflows. This includes product, customer, supplier, and other core enterprise domains, delivered as governed, curated datasets rather than raw exports.Product Features1. Fabric-Native Data Access via OneLake - Customers can materialize governed master data in OneLake and use the data for both BI and AI use-cases2.Configurable Data Export & Synchronization - Ability to select specific master data, denormalize data, managaging update frequecy enabling self-service data provisioning which preserving the governance model in the MDM system3.AI enriched master data - Automated product description generation, intelligent data quality recommendations and corrections. 4. Unified Analytics via Power BI - Leveraging the full Power BI co-pilot capabilities.
","feature_name":"Stibo Master Data Management","last_modified":"2026-06-16","product_id":"94e84e43-aa69-f011-bec2-00224804b6c3","product_name":"Fabric Ecosystem","release_date":"Q1 2026","release_item_id":"4660613a-9d00-f111-8406-6045bd0a886d","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.607,"feature_description":"The Data Source schema description feature allows users to add rich descriptions to tables, columns, and other schema elements to enhance how the Data Agent understands and utilizes the data. These annotations provide valuable context that can improve routing decisions, schema selection, and query generation. By supplementing raw schema with user-defined descriptions--such as definitions, usage notes, or expected value formats--the agent can interpret questions more accurately and choose the most relevant data sources and fields when responding to user queries.","feature_description_html":"The Data Source schema description feature allows users to add rich descriptions to tables, columns, and other schema elements to enhance how the Data Agent understands and utilizes the data. These annotations provide valuable context that can improve routing decisions, schema selection, and query generation. By supplementing raw schema with user-defined descriptions--such as definitions, usage notes, or expected value formats--the agent can interpret questions more accurately and choose the most relevant data sources and fields when responding to user queries.
","feature_name":"Data Agent Schema Descriptions for SQL","last_modified":"2026-09-10","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q3 2026","release_item_id":"429662c3-a361-f011-bec1-000d3a35e553","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6096,"feature_description":"Instead of spending countless hours on error-prone, manual data testing requiring specialized skills, 2TEST automates data and BI testing with a user-friendly interface for your entire team.","feature_description_html":"Instead of spending countless hours on error-prone, manual data testing requiring specialized skills, 2TEST automates data and BI testing with a user-friendly interface for your entire team.
","feature_name":"2TEST Data Testing","last_modified":"2026-06-16","product_id":"94e84e43-aa69-f011-bec2-00224804b6c3","product_name":"Fabric Ecosystem","release_date":"Q4 2025","release_item_id":"f12482a4-e7bf-f011-bbd3-6045bd05dd14","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"How Stibo Systems\u2019 MDM powers trusted data for analytics and AI in Microsoft Fabric (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/how-stibo-systems%E2%80%99-mdm-powers-trusted-data-for-analytics-and-ai-in-microsoft-fab/5172076","distance":0.6166,"feature_description":"Previously, we introduced endorsement for Fabric items. Certified and promoted endorsements encouraged the use of standardized and trustworthy data. Now, we're taking it a step further with the introduction of Master Data. This new endorsement stage empowers IT and data teams to define and establish the organization's single source of truth. By defining master data, your organization can benefit from creating a repository of all critical organizational data, making it available to users with a variety of skills to discover and build upon.","feature_description_html":"Previously, we introduced endorsement for Fabric items. Certified and promoted endorsements encouraged the use of standardized and trustworthy data. Now, we're taking it a step further with the introduction of Master Data. This new endorsement stage empowers IT and data teams to define and establish the organization's single source of truth. By defining master data, your organization can benefit from creating a repository of all critical organizational data, making it available to users with a variety of skills to discover and build upon.
","feature_name":"Fabric items - Master data","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q2 2025","release_item_id":"2b104a7d-b408-ef11-9f89-000d3a36eea4","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Fabric IQ: The Semantic Foundation for Enterprise AI","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-iq-the-semantic-foundation-for-enterprise-ai/5172473","distance":0.6259,"feature_description":"We are introducing Ontology Health in Microsoft Fabric to help modelers quickly assess whether an ontology is ready for real-world use. With this capability, ontology quality is continuously evaluated across three core areas--foundation, validation, and refinement--ensuring models are structurally sound, properly configured, and operationally ready. With this capability, you can: * Identify structural issues and inconsistencies through foundation checks to ensure the ontology is sound. * Verify required configurations using validation checks to confirm completeness and correctness. * Improve clarity, coverage, and usability with refinement checks for downstream consumption. * Quickly prioritize fixes with results categorized as Critical, Warning, or Information. * Navigate directly to impacted elements with guided actions, enabling faster issue resolution and increased trust in queries, graph experiences, and AI-powered outcomes.","feature_description_html":"We are introducing Ontology Health in Microsoft Fabric to help modelers quickly assess whether an ontology is ready for real-world use. With this capability, ontology quality is continuously evaluated across three core areas--foundation, validation, and refinement--ensuring models are structurally sound, properly configured, and operationally ready. With this capability, you can:
\nUser Data Functions will provide a powerful mechanism for implementing and re-using custom, specialized business logic into Fabric data science and data engineering workflows, increasing efficiency and flexibility.
","feature_name":"User Data Functions in Fabric","last_modified":"2026-06-16","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q3 2025","release_item_id":"3a31cb8c-8503-ef11-a1fd-000d3a34b75c","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6302,"feature_description":"Unified GetData Full Page Experience is a unified, full-screen workflow for completing a data-ingestion job from entry points such as Real-Time Hub, Eventhouse, and Eventstream. It guides users through source discovery, connector configuration, preview, optional schema handling and transformation, destination setup, and creation or update of the required ingestion pipeline--with autosave and resume support.","feature_description_html":"Unified GetData Full Page Experience is a unified, full-screen workflow for completing a data-ingestion job from entry points such as Real-Time Hub, Eventhouse, and Eventstream. It guides users through source discovery, connector configuration, preview, optional schema handling and transformation, destination setup, and creation or update of the required ingestion pipeline--with autosave and resume support.
","feature_name":"Unified GetData Full Page Experience","last_modified":"2026-09-24","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q4 2026","release_item_id":"b3544bc9-afb7-f111-aaab-7ced8d70b853","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"External data sharing enhancements out now","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/external-data-sharing-enhancements-out-now/5172898","distance":0.6346,"feature_description":"Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, now in public preview is built on top of OneLake shortcuts,and enables seamless, in-place sharing of data across tenant boundaries. This can be used by retailers sharing data with suppliers, consumers sharing diagnostic data with manufacturers, healthcare providers sharing data to create better diagnostics, corporations sharing data with their consultants, or for any other business scenario in which data needs to be shared with users outside of the data provider's tenant.","feature_description_html":"Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, now in public preview is built on top of OneLake shortcuts,and enables seamless, in-place sharing of data across tenant boundaries. This can be used by retailers sharing data with suppliers, consumers sharing diagnostic data with manufacturers, healthcare providers sharing data to create better diagnostics, corporations sharing data with their consultants, or for any other business scenario in which data needs to be shared with users outside of the data provider's tenant.
","feature_name":"External data sharing public preview","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q2 2024","release_item_id":"bac9d235-f707-ef11-9f89-000d3a33ff0b","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Fabric IQ: The Semantic Foundation for Enterprise AI","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/fabric-iq-the-semantic-foundation-for-enterprise-ai/5172473","distance":0.6362,"feature_description":"Fabric IQ Ontology empowers the Data Agent to query and reason over your business data using natural language, grounded in a well-defined semantic model. Users can ask complex analytical questions and receive trustworthy, explainable answers with citations that trace back to the underlying ontology and data sources -- giving teams confidence that insights are accurate, verifiable, and rooted in the true structure of their enterprise data.","feature_description_html":"Fabric IQ Ontology empowers the Data Agent to query and reason over your business data using natural language, grounded in a well-defined semantic model. Users can ask complex analytical questions and receive trustworthy, explainable answers with citations that trace back to the underlying ontology and data sources -- giving teams confidence that insights are accurate, verifiable, and rooted in the true structure of their enterprise data.
","feature_name":"[Ontology for Data Agent] - Ontology integration with Data Agent","last_modified":"2026-06-17","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q2 2026","release_item_id":"ccbc18bb-0b03-f111-8406-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"From repetition to reuse: accelerate data prep with My queries in Dataflow Gen2","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/From-repetition-to-reuse-accelerate-data-prep-with-My-queries-in/ba-p/5176763","distance":0.6383,"feature_description":"We will allow Dataflow Gen2 Creators to create a 'snapshot' of their data and work 'locally' with that data in their environment without querying the data source again in the authoring / design experience, significantly reducing the latency after each query evaluation step.","feature_description_html":"We will allow Dataflow Gen2 Creators to create a 'snapshot' of their data and work 'locally' with that data in their environment without querying the data source again in the authoring / design experience, significantly reducing the latency after each query evaluation step.
","feature_name":"Dataflows - Design-time query sampling","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2025","release_item_id":"c5823840-3f21-f011-9989-000d3a34671f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Introducing support for Workspace Identity Authentication in Fabric Connectors","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-support-for-workspace-identity-authentication-in-fabric-connectors/5172633","distance":0.6386,"feature_description":"This enables Managed identity to be configured at a workspace level. You can use the Fabric managed identities to connect to your data source securely.","feature_description_html":"This enables Managed identity to be configured at a workspace level. You can use the Fabric managed identities to connect to your data source securely.
","feature_name":"Data source identity management (Managed Identity)","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2024","release_item_id":"c96ec639-5807-ef11-9f89-000d3a34b75c","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Warehouse Snapshots in Microsoft Fabric (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/warehouse-snapshots-in-microsoft-fabric-generally-available/5172500","distance":0.6396,"feature_description":"This capability allows users to access a consistent version of data from a specific point in time, even if they attempt to access it during an ongoing ETL process. Fabric DW is committed to empowering businesses with reliable and consistent data, enabling them to make informed decisions with confidence.","feature_description_html":"This capability allows users to access a consistent version of data from a specific point in time, even if they attempt to access it during an ongoing ETL process. Fabric DW is committed to empowering businesses with reliable and consistent data, enabling them to make informed decisions with confidence.
","feature_name":"Warehouse Snapshots (Public Preview)","last_modified":"2026-06-16","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2025","release_item_id":"82a45049-0c22-f011-9989-00224804b6c3","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Microsoft Purview Data Loss Prevention policies for Fabric have been extended to SQL, KQL and Mirrored Databases (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/microsoft-purview-data-loss-prevention-policies-for-fabric-have-been-extended-to/5172819","distance":0.6409,"feature_description":"Security admins can create in Microsoft Purview Data Loss Prevention portal policies to detect the upload of sensitive data (such as social security number) to Fabric Lakehouse, Kustoand SQL databases, Mirrored databases, and Semantic models. If such an upload is detected, the policies will trigger automatic audit activity and can be configured to show a custom policy tip to data owners and it can also trigger an alert for security admins. DLP policies can help automate the compliance processes to meet enterprise-scale compliance and regulatory requirements in an effective way.Note: Microsoft Purview data loss prevention policies for Fabric require Microsoft Purview license.","feature_description_html":"Security admins can create in Microsoft Purview Data Loss Prevention portal policies to detect the upload of sensitive data (such as social security number) to Fabric Lakehouse, Kustoand SQL databases, Mirrored databases, and Semantic models. If such an upload is detected, the policies will trigger automatic audit activity and can be configured to show a custom policy tip to data owners and it can also trigger an alert for security admins. DLP policies can help automate the compliance processes to meet enterprise-scale compliance and regulatory requirements in an effective way.Note: Microsoft Purview data loss prevention policies for Fabric require Microsoft Purview license.
","feature_name":"Microsoft Purview data loss prevention policies for structured data in OneLake","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q3 2025","release_item_id":"7086191a-4e20-f011-998a-0022480939f0","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Simplifying file access in OPENROWSET using data sources and relative paths (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/simplifying-file-access-in-openrowset-using-data-sources-and-relative-paths-prev/5172546","distance":0.6457,"feature_description":"The EXTERNAL DATA SOURCE feature allows you to define a root URI that can be referenced by name in OPENROWSET and BULK INSERT operations. By using the DATA SOURCE option, you can reference files using relative paths, eliminating the need to specify long absolute URIs.","feature_description_html":"The EXTERNAL DATA SOURCE feature allows you to define a root URI that can be referenced by name in OPENROWSET and BULK INSERT operations. By using the DATA SOURCE option, you can reference files using relative paths, eliminating the need to specify long absolute URIs.
","feature_name":"External data sources","last_modified":"2026-06-16","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"d153a715-e29d-f011-b41c-000d3a30273e","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Visualize and Explore Data with Real-Time Dashboards","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/visualize-and-explore-data-with-real-time-dashboards/5173172","distance":0.6462,"feature_description":"General availability of no-code data exploration experiences for Real-Time Intelligence, including the ability to explore real-time dashboards tile data and query results.","feature_description_html":"General availability of no-code data exploration experiences for Real-Time Intelligence, including the ability to explore real-time dashboards tile data and query results.
","feature_name":"Data Exploration General Availability","last_modified":"2026-06-16","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q4 2024","release_item_id":"2689b555-4a31-ef11-8409-000d3a31cfdc","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6472,"feature_description":"Quantexa Unify reads data directly from OneLake, automates cleansing, parsing and normalization of data sources, then performs sophisticated data matching to produce a connected view of the data in OneLake. Quantexa Unify provides preconfigured matching templates, allowing users to define the level of match quality required.","feature_description_html":"Quantexa Unify reads data directly from OneLake, automates cleansing, parsing and normalization of data sources, then performs sophisticated data matching to produce a connected view of the data in OneLake. Quantexa Unify provides preconfigured matching templates, allowing users to define the level of match quality required.
","feature_name":"Quantexa Unify entity resolution for OneLake","last_modified":"2026-06-16","product_id":"94e84e43-aa69-f011-bec2-00224804b6c3","product_name":"Fabric Ecosystem","release_date":"Q3 2025","release_item_id":"b642566a-b37e-f011-b4cc-00224804b6c3","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Data source routing in Microsoft Fabric data agents (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Data-source-routing-in-Microsoft-Fabric-data-agents-Generally/ba-p/5358696","distance":0.6473,"feature_description":"The Routing Tool is a fallback mechanism used by the Data Agent to intelligently determine which tool or data source should handle a user's natural language query. When schema descriptions are available--either user-supplied or imported--they enhance the routing process by providing semantic context about each table or schema element. This context helps the agent make more accurate decisions about which tool (e.g., SQL, KQL) or dataset is best suited to answer the question. The routing tool activates when a query is ambiguous, ensuring queries are directed efficiently and appropriately. The routing tool results can be viewed by the creator for easier debugging.","feature_description_html":"The Routing Tool is a fallback mechanism used by the Data Agent to intelligently determine which tool or data source should handle a user's natural language query. When schema descriptions are available--either user-supplied or imported--they enhance the routing process by providing semantic context about each table or schema element. This context helps the agent make more accurate decisions about which tool (e.g., SQL, KQL) or dataset is best suited to answer the question. The routing tool activates when a query is ambiguous, ensuring queries are directed efficiently and appropriately. The routing tool results can be viewed by the creator for easier debugging.
","feature_name":"Improve Data Agent Data Source Selection Results","last_modified":"2026-09-10","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q4 2025","release_item_id":"8bc8911e-a461-f011-bec1-000d3a35e553","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Data Agent Now Supports Eventhouse Functions, Materialized Views, and Shortcuts (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Data-Agent-Now-Supports-Eventhouse-Functions-Materialized-Views/ba-p/5181801","distance":0.6477,"feature_description":"Fabric Data Agent's support for KQL UDFs enables richer, more optimized reasoning over Eventhouse and KQL-backed data sources, improving both accuracy and performance of generated KQL queries. By leveraging governed, reusable functions, the agent can translate natural-language questions into more efficient queries while honoring Fabric's security and schema semantics. This expands the breadth of scenarios the agent can handle, making KQL-based analytics faster and more consistent.","feature_description_html":"Fabric Data Agent's support for KQL UDFs enables richer, more optimized reasoning over Eventhouse and KQL-backed data sources, improving both accuracy and performance of generated KQL queries. By leveraging governed, reusable functions, the agent can translate natural-language questions into more efficient queries while honoring Fabric's security and schema semantics. This expands the breadth of scenarios the agent can handle, making KQL-based analytics faster and more consistent.
","feature_name":"[PuPr] Data Agent to Support Kusto UDF","last_modified":"2026-06-24","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q1 2026","release_item_id":"ea64bc96-e805-f111-8406-6045bd0a886d","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Get Data in Real-Time Analytics: A New and Improved Experience","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/get-data-in-real-time-analytics-a-new-and-improved-experience/5172134","distance":0.6488,"feature_description":"Users experience a simplified and improved Get Data experience to get their data into a KQL database.","feature_description_html":"Users experience a simplified and improved Get Data experience to get their data into a KQL database.
","feature_name":"Improved Get Data experience","last_modified":"2026-06-16","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2024","release_item_id":"b475de8d-84d7-ee11-9079-000d3a310f67","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6511,"feature_description":"TMDL View offers a "semantic modeling as code" experience with [Tabular Model Definition Language](/analysis-services/tmdl/tmdl-overview) (TMDL) within Power BI Desktop. It enables users to script, modify, and apply changes to semantic model objects in a modern code editor, significantly enhancing user efficiency. Future updates will integrate Copilot for handling time-intensive tasks such as model translations, documentation, bulk operations, among others.","feature_description_html":"TMDL View offers a \"semantic modeling as code\" experience with Tabular Model Definition Language (TMDL) within Power BI Desktop. It enables users to script, modify, and apply changes to semantic model objects in a modern code editor, significantly enhancing user efficiency. Future updates will integrate Copilot for handling time-intensive tasks such as model translations, documentation, bulk operations, among others.
","feature_name":"TMDL View","last_modified":"2026-06-16","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q1 2025","release_item_id":"a0e8b3bd-4e92-ef11-ac21-6045bd062aa2","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Connecting to OneLake","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/connecting-to-onelake%C2%A0/5172301","distance":0.6511,"feature_description":"Telmai is a data quality analysis and monitoring platform that helps data teams proactively understand, detect, and resolve data quality issues without writing code, SQL, or rules. Telmai now supports connecting to Microsoft OneLake to assest and monitor datasets for quality assurance.","feature_description_html":"Telmai is a data quality analysis and monitoring platform that helps data teams proactively understand, detect, and resolve data quality issues without writing code, SQL, or rules. Telmai now supports connecting to Microsoft OneLake to assest and monitor datasets for quality assurance.
","feature_name":"Telmai OneLake connector","last_modified":"2026-06-16","product_id":"94e84e43-aa69-f011-bec2-00224804b6c3","product_name":"Fabric Ecosystem","release_date":"Q1 2026","release_item_id":"93425130-8100-f111-8406-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6528,"feature_description":"The Data Grid feature for data warehouses and SQL analytics endpoints includes:* Data Handling: Supports large datasets with virtualization for performance.* Column & Row Features: Reorder, hide/show, group, pin, define dynamic columns, sort, filter, and select multiple rows.* Filtering & Sorting: Multi-column sorting and custom filters for data types (string, integer, date).* Search: Search across columns.* Grid-Level Options: Copy data with/without headers, select multiple rows, and export data in various formats.","feature_description_html":"The Data Grid feature for data warehouses and SQL analytics endpoints includes:
\nTMDL View offers a \"semantic modeling as code\" experience with Tabular Model Definition Language (TMDL) within Power BI Desktop. It enables users to script, modify, and apply changes to semantic model objects in a modern code editor, significantly enhancing user efficiency. Future updates will integrate Copilot for handling time-intensive tasks such as model translations, documentation, bulk operations, among others.
","feature_name":"TMDL View","last_modified":"2026-06-16","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q3 2025","release_item_id":"f7581238-3f22-f011-9989-6045bd030c4d","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Introducing Power BI Embedded with Direct Lake Mode","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-power-bi-embedded-with-direct-lake-mode/5172944","distance":0.6573,"feature_description":"Power BI is standardizing on open-data formats by adopting Delta Lake and Parquet to help you avoid vendor lock-in and reduce data duplication. This minimizes data silos and fragmentation, offering a single source of truth across the enterprise. Direct Lake accelerates time to data-driven decisions by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming data refreshes for large volumes of data in OneLake.Create Direct Lake semantic models from Power BI Desktop from any OneLake source, not only Lakehouse or Warehouse. This feature allows users to build and maintain Power BI semantic models that query data directly from the lake, without depending on the SQL endpoint, enabling faster performance, simpler security management.","feature_description_html":"Power BI is standardizing on open-data formats by adopting Delta Lake and Parquet to help you avoid vendor lock-in and reduce data duplication. This minimizes data silos and fragmentation, offering a single source of truth across the enterprise. Direct Lake accelerates time to data-driven decisions by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming data refreshes for large volumes of data in OneLake.Create Direct Lake semantic models from Power BI Desktop from any OneLake source, not only Lakehouse or Warehouse. This feature allows users to build and maintain Power BI semantic models that query data directly from the lake, without depending on the SQL endpoint, enabling faster performance, simpler security management.
","feature_name":"Creation of Direct Lake semantic models in Power BI Desktop","last_modified":"2026-06-16","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q2 2025","release_item_id":"7a2592b6-4f92-ef11-ac21-6045bd062aa2","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Introducing Power BI Embedded with Direct Lake Mode","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-power-bi-embedded-with-direct-lake-mode/5172944","distance":0.6573,"feature_description":"Power BI is standardizing on open-data formats by adopting Delta Lake and Parquet to help you avoid vendor lock-in and reduce data duplication. This minimizes data silos and fragmentation, offering a single source of truth across the enterprise. Direct Lake accelerates time to data-driven decisions by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming data refreshes for large volumes of data in OneLake.Create Direct Lake semantic models from Power BI Desktop from any OneLake source, not only Lakehouse or Warehouse. This feature allows users to build and maintain Power BI semantic models that query data directly from the lake, without depending on the SQL endpoint, enabling faster performance, simpler security management.","feature_description_html":"Power BI is standardizing on open-data formats by adopting Delta Lake and Parquet to help you avoid vendor lock-in and reduce data duplication. This minimizes data silos and fragmentation, offering a single source of truth across the enterprise. Direct Lake accelerates time to data-driven decisions by unlocking incredible performance directly against OneLake, without the need to manage costly, time-consuming data refreshes for large volumes of data in OneLake.Create Direct Lake semantic models from Power BI Desktop from any OneLake source, not only Lakehouse or Warehouse. This feature allows users to build and maintain Power BI semantic models that query data directly from the lake, without depending on the SQL endpoint, enabling faster performance, simpler security management.
","feature_name":"Creation of Direct Lake semantic models in Power BI Desktop","last_modified":"2026-06-16","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q3 2025","release_item_id":"3384cd31-6a83-f011-b4cc-000d3a30273e","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6595,"feature_description":"The Data Grid feature for data warehouses and SQL analytics endpoints includes:* Data Handling: Supports large datasets with virtualization for performance.* Performance Optimization: Virtual scrolling, row virtualization, and lazy loading.* Search: Search across columns.* Column & Row Features: Resize and sort.* Grid-Level Options: Copy data with/without headers","feature_description_html":"The Data Grid feature for data warehouses and SQL analytics endpoints includes:
\nWe plan to make My Queries in Dataflow Gen2 generally available, enabling users to save commonly used Power Query transformations to a personal library and reuse them across multiple dataflows.Moving My Queries to GA means it is ready for production usage, with the reliability, consistency, and support required for standardizing data preparation at scale.Key benefits and scenarios:
\nWith the CDC mechanism, customers can automatically capture inserts, updates, and deletions from SAP via Datasphere and replicate them to the destination--without requiring a watermark column.
","feature_name":"Copy job - CDC based replication from SAP via Datasphere","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q4 2025","release_item_id":"1670c585-c19a-f011-b4cc-000d3a5b0efa","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Expanded Data Agent Support for Large Data Sources","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/expanded-data-agent-support-for-large-data-sources/5172654","distance":0.6596,"feature_description":"Production Datawarehouses, Lakehouses, Semantic Models, and SQL DBs contain hundreds to tens of thousands of Tables, Views, Functions, and Measures. This feature highlights the requirements to support production scale database systems and semantic models across data agent scenarios within the Data Agent UX seamlessly: - Enabling the UX to allow users to load large schemas into Data Agent and select Objects easily and efficiently.- Enabling the UX to search for elements allowing users to easily identify key entities required for data agent input - Enabling pagination in the UX to seamlessly browse the schema in the Object explorer.","feature_description_html":"Production Datawarehouses, Lakehouses, Semantic Models, and SQL DBs contain hundreds to tens of thousands of Tables, Views, Functions, and Measures. This feature highlights the requirements to support production scale database systems and semantic models across data agent scenarios within the Data Agent UX seamlessly:
\nAccelerate metadata queries and schema exploration for warehouses and sql analytics endpoints, improving object browsing, tooling responsiveness, and developer productivity across Fabric and SQL tools.
","feature_name":"Faster Metadata & Schema Discovery for Large Warehouses","last_modified":"2026-08-26","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"e943c699-fca0-f111-b8db-000d3a3ad657","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Introducing the HTTP and MongoDB CDC Connectors for Eventstream \u2014 Inspired by You","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/introducing-the-http-and-mongodb-cdc-connectors-for-eventstream-%E2%80%94-inspired-by-yo/5172439","distance":0.6603,"feature_description":"Support the data insights (metrics) on connector sources","feature_description_html":"Support the data insights (metrics) on connector sources
","feature_name":"Data insights (metrics) on connector sources in Eventstream","last_modified":"2026-06-16","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q4 2024","release_item_id":"45bcb820-d691-ef11-ac21-002248098a98","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Analyze Your Business Events in Eventhouse and Real-Time Dashboards (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Analyze-Your-Business-Events-in-Eventhouse-and-Real-Time/ba-p/5190245","distance":0.6605,"feature_description":"**Business Events Data Preview Overview**Enable developers to inspect, validate, and troubleshoot Business Events when publishing them to downstream consumers. Data Preview provides real-time visibility into event payloads, schema mappings, and event metadata, reducing time-to-value and simplifying event-driven application development.**Key Capabilities*** Real-Time Event Inspection: Preview Business Event payloads as they are generated, allowing developers to validate event content before integrating downstream consumers.* Schema Visibility: View event schemas and payload structures side-by-side to ensure events conform to expected Business Event definitions.* Metadata Exploration: Inspect event properties, headers, correlation identifiers, timestamps, and other event metadata to better understand event context and flow.* Accelerated Troubleshooting: Quickly identify publishing issues, schema mismatches, missing attributes, and configuration errors without requiring external debugging tools.* Developer-Friendly Testing Experience: Validate Business Events during development and onboarding, enabling faster iteration and reducing time to first successful event-driven scenario.* Improved Confidence Before Consumption: Allow publishers to verify event quality and correctness before events are consumed by Eventstream, Activator, AI agents, or other downstream applications.* End-to-End Event Flow Validation: Enable developers to confirm that Business Events are generated as expected before investing effort in consumers, automation workflows, or integrations.","feature_description_html":"Business Events Data Preview OverviewEnable developers to inspect, validate, and troubleshoot Business Events when publishing them to downstream consumers. Data Preview provides real-time visibility into event payloads, schema mappings, and event metadata, reducing time-to-value and simplifying event-driven application development.
\nKey Capabilities
\nFabric Data Agent's support for External Tables allows data agent users to include data that lives outside Eventhouse while keeping a single, governed agent configuration and schema context.
\nThis enables simpler setup and reuse of existing KQL data definitions, reducing duplication and manual query logic within data agent..
","feature_name":"[PuPr] Data Agent to Support Kusto External Tables","last_modified":"2026-06-24","product_id":"951b64e0-a663-f111-a826-6045bd00f798","product_name":"Conversational Analytics","release_date":"Q1 2026","release_item_id":"3a1e1a9b-1b12-f111-8406-6045bd0a8ec1","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Announcing Data Clustering in Fabric Data Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-data-clustering-in-fabric-data-warehouse-preview/5172452","distance":0.6614,"feature_description":"Data Clustering enables faster read performance by allowing users to specify columns for co-locating data on ingestion and perform file skipping on read.","feature_description_html":"Data Clustering enables faster read performance by allowing users to specify columns for co-locating data on ingestion and perform file skipping on read.
","feature_name":"Data Clustering","last_modified":"2026-07-30","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2026","release_item_id":"5cd4b9a0-1322-f011-998a-0022480939f0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Announcing Data Clustering in Fabric Data Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-data-clustering-in-fabric-data-warehouse-preview/5172452","distance":0.6614,"feature_description":"Data Clustering enables faster read performance by allowing users to specify columns for co-locating data on ingestion and perform file skipping on read.","feature_description_html":"Data Clustering enables faster read performance by allowing users to specify columns for co-locating data on ingestion and perform file skipping on read.
","feature_name":"Data Clustering","last_modified":"2026-06-16","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q4 2025","release_item_id":"d8b8c72e-7f82-ef11-ac21-002248098a98","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"distance":0.6621,"feature_description":"This feature allows users to create property definitions without immediately selecting a data type or binding to a data source. Users can define high-level business concepts first and assign data types later when real data is bound, at which point the entity type updates automatically. This flexibility lets users model concepts early without needing to know data types or source details upfront.","feature_description_html":"This feature allows users to create property definitions without immediately selecting a data type or binding to a data source. Users can define high-level business concepts first and assign data types later when real data is bound, at which point the entity type updates automatically. This flexibility lets users model concepts early without needing to know data types or source details upfront.
","feature_name":"Modeling Untyped Properties","last_modified":"2026-06-17","product_id":"cef5a30d-562f-f011-8c4d-6045bd096d8f","product_name":"IQ","release_date":"Q2 2026","release_item_id":"dbfb5522-a703-f111-8406-6045bd00f798","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"External data sharing enhancements out now","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/external-data-sharing-enhancements-out-now/5172898","distance":0.6624,"feature_description":"We will introduce additional enhancements to Fabric external data sharing, including Public APIs, the ability to share multiple tables, and to share eventhouse tables.","feature_description_html":"We will introduce additional enhancements to Fabric external data sharing, including Public APIs, the ability to share multiple tables, and to share eventhouse tables.
","feature_name":"External data sharing enhancements","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q2 2025","release_item_id":"357fe638-f807-ef11-9f89-000d3a33ff0b","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Protecting Your Fabric Data Using Purview (Generally Available)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/protecting-your-fabric-data-using-purview-generally-available/5172586","distance":0.6628,"feature_description":"Fabric admins and data owners can gain valuable insights about sensitive data, certified and promoted items. They contain insights about sensitive data, certified and promoted items, and a gateway to advanced capabilities in Microsoft Purview portals.","feature_description_html":"Fabric admins and data owners can gain valuable insights about sensitive data, certified and promoted items. They contain insights about sensitive data, certified and promoted items, and a gateway to advanced capabilities in Microsoft Purview portals.
","feature_name":"Purview hub for administrators and data owners","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q1 2024","release_item_id":"11fdb55a-e9d6-ee11-9079-000d3a341a60","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Audit columns in Copy job in Fabric Data Factory\u2014every row is traceable for data lineage and compliance","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/audit-columns-in-copy-job-in-fabric-data-factory%E2%80%94every-row-is-traceable-for-data/5172218","distance":0.6652,"feature_description":"You can use Copy job to add audit-style system columns when moving data to your destination, improving data lineage and compliance. This feature provides row-level visibility into your destination data, showing when rows were moved and where they came from, enabling downstream data lineage tracking and compliance reporting across the entire stack.","feature_description_html":"You can use Copy job to add audit-style system columns when moving data to your destination, improving data lineage and compliance. This feature provides row-level visibility into your destination data, showing when rows were moved and where they came from, enabling downstream data lineage tracking and compliance reporting across the entire stack.
","feature_name":"Copy Job - Audit Column","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q1 2026","release_item_id":"ec4d989c-6921-f011-998a-0022480939f0","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Govern in OneLake Catalog for Fabric admins (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/govern-in-onelake-catalog-for-fabric-admins-preview/5172446","distance":0.6661,"feature_description":"The Goven tab in OneLake Catalog, is a central place for data owners to find insights on their data, get recommended actions and find all the solutions in Fabric available for themAs part of the public preview, Data owners will be able to see information about the data they created, such as insights on their item inventory, last refresh, protection state (with sensitivity label or not), curations state (description, tags, endorsement)Recommended actions will focus the user on steps he should take in order to optimize their items, if it needs to be better curated, well protected and refreshed, while in some cases the user can decide those items are no longer relevant and help with housekeeping.Additional valuable are includes all the solutions available for data owners in Fabric and important links that can help to promote user's knowledge on Fabric to better utilize the system.","feature_description_html":"The Goven tab in OneLake Catalog, is a central place for data owners to find insights on their data, get recommended actions and find all the solutions in Fabric available for themAs part of the public preview, Data owners will be able to see information about the data they created, such as insights on their item inventory, last refresh, protection state (with sensitivity label or not), curations state (description, tags, endorsement)Recommended actions will focus the user on steps he should take in order to optimize their items, if it needs to be better curated, well protected and refreshed, while in some cases the user can decide those items are no longer relevant and help with housekeeping.Additional valuable are includes all the solutions available for data owners in Fabric and important links that can help to promote user's knowledge on Fabric to better utilize the system.
","feature_name":"OneLake Catalog - Govern Tab for Data Owners","last_modified":"2026-06-16","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q1 2025","release_item_id":"ea1fc0f1-6d20-f011-998a-000d3a341dd9","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Data Factory Spotlight: Semantic model refresh activity","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/data-factory-spotlight-semantic-model-refresh-activity%C2%A0/5173217","distance":0.6665,"feature_description":"The flexibility and control over worfklows that data pipelines provides for refreshing your semantic models in Fabric has proven to be extremely popular. We are super excited to announce the general availability of the Fabric Data Factory Data Pipeline activity for Semantic Model Refresh.","feature_description_html":"The flexibility and control over worfklows that data pipelines provides for refreshing your semantic models in Fabric has proven to be extremely popular. We are super excited to announce the general availability of the Fabric Data Factory Data Pipeline activity for Semantic Model Refresh.
","feature_name":"Data Pipelines - Semantic Model Refresh Activity","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q2 2025","release_item_id":"4f59ff7b-0226-f011-8c4d-000d3a34671f","release_status":"Shipped","release_type":"General availability"},{"active":true,"blog_title":"Announcing Public Preview: Incremental Refresh in Dataflow Gen2","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/announcing-public-preview-incremental-refresh-in-dataflow-gen2/5173103","distance":0.6669,"feature_description":"Dataflow Gen2 CI/CD developers will benefit from richer Refresh History information including M Engine statistics (rows processed, duration, etc.)","feature_description_html":"Dataflow Gen2 CI/CD developers will benefit from richer Refresh History information including M Engine statistics (rows processed, duration, etc.)
","feature_name":"Dataflows - Refresh History including M engine statistics","last_modified":"2026-06-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2025","release_item_id":"b9a3f371-3e21-f011-9989-000d3a34671f","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Building real-time, event-driven applications with Database CDC feeds and Fabric Eventstreams DeltaFlow (Preview)","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/building-real-time-event-driven-applications-with-database-cdc-feeds-and-fabric-/5172193","distance":0.6691,"feature_description":"DeltaFlow is an enhancement to Eventstreams that simplifies real-time analytics on Change Data Capture (CDC) feeds. By automatically transforming raw Debezium change events into structured tables that mirror the source schema, DeltaFlow enables developers and analysts to work with change data as if it were regular tables--eliminating the need to interpret complex CDC semantics. This streamlined approach accelerates the development of operational dashboards, event-driven applications, and real-time analytics solutions, allowing users to extract insights, build AI-powered workflows, and react instantly to changes in their data sources with minimal manual setup and maintenance.","feature_description_html":"DeltaFlow is an enhancement to Eventstreams that simplifies real-time analytics on Change Data Capture (CDC) feeds. By automatically transforming raw Debezium change events into structured tables that mirror the source schema, DeltaFlow enables developers and analysts to work with change data as if it were regular tables--eliminating the need to interpret complex CDC semantics. This streamlined approach accelerates the development of operational dashboards, event-driven applications, and real-time analytics solutions, allowing users to extract insights, build AI-powered workflows, and react instantly to changes in their data sources with minimal manual setup and maintenance.
","feature_name":"DeltaFlow: Seamless stream processing of Database CDC Feeds","last_modified":"2026-06-16","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2026","release_item_id":"08c6dc13-08a4-f011-bbd3-000d3a5b0efa","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"New Dataflow Gen2 data destinations and experience improvements","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/new-dataflow-gen2-data-destinations-and-experience-improvements/5172568","distance":0.6692,"feature_description":"Dataflows Gen2 will expand support for Dynamic Schema mode when writing to Fabric Data Warehouse destinations, making it easier to handle evolving data without manual rework.With Dynamic Schema, Dataflow Gen2 can automatically adapt to changes in your query schema--such as added or modified columns--when publishing data to a warehouse. This removes the need to pre-define and rigidly maintain table schemas as your data evolves.Why this matters:* Faster iteration: Update transformations without blocking on schema alignment.* Schema-flexible pipelines: Land evolving datasets into a warehouse with less friction.* Simpler migrations: Common Gen1 and self-service scenarios that rely on schema evolution become easier to move to Gen2.This capability is especially valuable for transformation-heavy pipelines and self-service data preparation, where schemas naturally change over time, and aligns Dataflow Gen2 more closely with the flexible, modern data engineering patterns supported across Microsoft Fabric.","feature_description_html":"Dataflows Gen2 will expand support for Dynamic Schema mode when writing to Fabric Data Warehouse destinations, making it easier to handle evolving data without manual rework.With Dynamic Schema, Dataflow Gen2 can automatically adapt to changes in your query schema--such as added or modified columns--when publishing data to a warehouse. This removes the need to pre-define and rigidly maintain table schemas as your data evolves.Why this matters:
\nThis capability is especially valuable for transformation-heavy pipelines and self-service data preparation, where schemas naturally change over time, and aligns Dataflow Gen2 more closely with the flexible, modern data engineering patterns supported across Microsoft Fabric.
","feature_name":"Dataflows - Dynamic Schema Support for Fabric Data Warehouse Destination","last_modified":"2026-10-07","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"8ed33294-5a38-f111-88b5-6045bd00fc61","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"Explore Data Transformation in Eventstream for KQL Database Integration","blog_url":"https://community.fabric.microsoft.com/blog/fbc_fabricupdatesblogs/explore-data-transformation-in-eventstream-for-kql-database-integration/5172104","distance":0.6695,"feature_description":"The addition of the KQL database destination with data transformation enables customers to transform, enrich, and reduce the data volume before it's routed to KQL databases.","feature_description_html":"The addition of the KQL database destination with data transformation enables customers to transform, enrich, and reduce the data volume before it's routed to KQL databases.
","feature_name":"Eventstream data preprocessing to KQL database destination","last_modified":"2026-06-16","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q1 2024","release_item_id":"f38c475a-84d7-ee11-9079-000d3a310f67","release_status":"Shipped","release_type":"Public preview"},{"active":true,"blog_title":"T-SQL language enhancements in Microsoft Fabric Data Warehouse (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/T-SQL-language-enhancements-in-Microsoft-Fabric-Data-Warehouse/ba-p/5375433","distance":0.6696,"feature_description":"New capabilities focused on making T-SQL more powerful for analytics, simpler to write, and more intuitive for data exploration.<br/>* **Advanced Analytics** Expanded statistical capabilities with functions such as `MEDIAN`, `QUANTILE`, `PERCENTILE`, `APPROX_MEDIAN`, and `APPROX_QUANTILE` for distribution analysis and large-scale analytical workloads.* **Simplified Query Syntax** New language constructs including `QUALIFY`, `GROUP BY ALL`, and `ORDER BY ALL` help reduce query complexity and improve readability of analytical queries.* **More Intuitive Querying** Developer-friendly syntax enhancements designed to make common query patterns easier to express, improving productivity and reducing the amount of code required to answer business questions.","feature_description_html":"New capabilities focused on making T-SQL more powerful for analytics, simpler to write, and more intuitive for data exploration.
\nMEDIAN, QUANTILE, PERCENTILE, APPROX_MEDIAN, and APPROX_QUANTILE for distribution analysis and large-scale analytical workloads.QUALIFY, GROUP BY ALL, and ORDER BY ALL help reduce query complexity and improve readability of analytical queries.Eventstream now makes change data capture (CDC) feeds analytics ready by automatically turning raw change events into structured tables. Developers and analysts can work with change data as if it were regular tables, without interpreting complex CDC semantics. This speeds the development of operational dashboards, event-driven applications, and real-time analytics.With this capability, you can:
\nWe plan to make Diagnostics Download in Dataflow Gen2 generally available, providing a production-ready way to capture and share detailed runtime diagnostics for dataflow executions.Moving this feature to GA means it is ready for production usage, with consistent behavior, reliability, and support suitable for business-critical data pipelines.Key benefits and scenarios:
\n