{"data":[{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Quickly identify when and where throttling is occurring across your Eventhouse system. This feature highlights recent throttling events, helping you diagnose performance bottlenecks and take corrective action before they impact users.","feature_name":"show Throttling events of eventhouse at Eventhouse WS monitoring","last_modified":"2026-05-31","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"2d60a7e8-b555-f011-877a-00224804ca88","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Easily load Fabric OneLake data into Excel \u2014 OneLake catalog and Get Data are integrated into Excel for Windows (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/easily-load-fabric-onelake-data-into-excel-onelake-catalog-and-modern-get-data-are-integrated-into-excel-for-windows-preview","feature_description":"The OneLake catalog integration in Excel as part of modern Get Data unlocks seamless connectivity to Fabric data, empowering every business user to discover and analyze organizational content directly from Excel. This powerful capability simplifies data access and accelerates insights, making Excel a true gateway to Fabric's rich data ecosystem.","feature_name":"Fabric OneLake catalog in Excel Get Data","last_modified":"2026-05-27","product_id":"796a0af7-2dc7-ee11-9079-000d3a3419a8","product_name":"Administration, Governance and Security","release_date":"Q3 2026","release_item_id":"d2e07536-32b3-f011-bbd3-00224808fcf0","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric Real-Time Analytics Integrates with Newly Announced Database Watcher for Azure SQL","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-database-watcher-for-azure-sql-and-its-integration-with-fabric-real-time-analytics","feature_description":"Integration on Fabric SQL DB in Workspace Monitoring.","feature_name":"Fabric SQL DB in Workspace Monitoring","last_modified":"2026-05-27","product_id":"347da228-ea54-ef11-a317-0022480a694f","product_name":"SQL database","release_date":"Q3 2026","release_item_id":"2e315574-8c31-f011-8c4d-00224804b6c3","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric IQ: The Semantic Foundation for Enterprise AI","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-fabric-iq-the-semantic-foundation-for-enterprise-ai","feature_description":"Synthetic AI engineers for Industrial Operations that help users automate routine tasks so human engineers can bring their ingenuity to creative and complex challenges.Its industrial semantics, ontologies, physics aware models and purpose built agents supply the understanding that turns raw data into  industrial grade operational intelligence using RTI on Fabric as the backbone. Eventstreams capture and transform data at massive scale, eventhouse delivers fast and flexible analytics and OneLake ensures that context is unified and discoverable.","feature_name":"Intuigence AI","last_modified":"2026-05-27","product_id":"94e84e43-aa69-f011-bec2-00224804b6c3","product_name":"Fabric Ecosystem","release_date":"Q2 2026","release_item_id":"6a3dbbe1-9d00-f111-8406-6045bd0a886d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Enhancing Data Transformation Flexibility with Multiple-Schema Inferencing in Eventstream (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/enhancing-data-transformation-flexibility-with-multiple-schema-inferencing-in-eventstream-preview","feature_description":"The Enhanced Eventstream UX for non-schematized events GA feature supports differentianting shapes from various sources and eventstream itself, enabling you to design different data transformation paths by picking up one of the shapes with rich flexibility. This allows for seamless data integration and processing, catering to complex and multiple data shapes environment","feature_name":"Enhanced Eventstream UX for non-schematized events GA","last_modified":"2026-05-22","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"b1a70168-24a6-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric Data Warehouse will introduce T-SQL functions for pattern matching, text extraction, and transformation using regular expressions. These capabilities will make it easier to validate, search, and manipulate text data directly within your queries.New functions include:* REGEXP_LIKE - Returns a Boolean indicating if the text matches the regex pattern.* REGEXP_REPLACE - Replaces occurrences of a regex pattern with a specified string.* REGEXP_SUBSTR - Extracts parts of a string based on a regex pattern, including Nth occurrence.* REGEXP_INSTR - Returns the position (start or end) of a matched substring.* REGEXP_COUNT - Counts how many times a regex pattern occurs in a string.* REGEXP_MATCHES - Returns a table of captured substrings matching the regex pattern.* REGEXP_SPLIT_TO_TABLE - Splits a string into rows using a regex delimiter.","feature_name":"Regular expressions","last_modified":"2026-05-21","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"aceeae17-dfb8-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Fabric Data Warehouse will introduce string similarity and comparison functions based on Levenshtein and Jaro-Winkler algorithms. These functions make it easier to find strings that are similar, even when they have small changes or spelling errors.New functions that will be added are:* EDIT_DISTANCE - Returns the number of edits (insertions, deletions, substitutions) needed to transform one string into another.* EDIT_DISTANCE_SIMILARITY - Calculates a similarity score (0-1) based on Levenshtein distance, where 1 means identical strings.* JARO_WINKLER_DISTANCE - Measures the distance between two strings using the Jaro-Winkler algorithm, considering transpositions and common prefixes.* JARO_WINKLER_SIMILARITY - Returns a similarity score (0-1) using Jaro-Winkler, optimized for short strings and minor typos.","feature_name":"Fuzzy string matching","last_modified":"2026-05-21","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"9b0e4fae-ecb8-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing Shortcut Transformations: from files to Delta tables. Always in sync, no pipelines required.","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-shortcut-transformations-from-files-to-delta-tables-always-in-sync-no-pipelines-required","feature_description":"With the upcoming enhancements to Shortcut Transformations, you'll be able to fully customize how you bring in data from CSV, Parquet, JSON, and Excel files. You can set file encoding, choose custom quote or escape characters for your data in case of CSV files. For CSV, Parquet and JSON files, you'll have options to decide refresh mode (Append Only or Mirror) and schema mode (Dynamic or Fixed).  All these controls will be available directly in the user interface, giving you more flexibility, reliability, and efficiency in managing your data ingestion and transformation workflows.","feature_name":"Shortcut Transformations - Customize ingestion with user configurations","last_modified":"2026-05-20","product_id":"338c69fe-dcd6-ee11-9079-000d3a310f67","product_name":"OneLake","release_date":"Q2 2026","release_item_id":"bc53aecb-f0ba-f011-bbd3-000d3a3740cc","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Medallion Implementation with Materialized Lake Views in Fabric","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-materialized-lake-views-at-build-2025","feature_description":"Multi Lakehouse support in Lineage enables users to visualize and manage dependencies of Materialized Lake Views (MLVs) across multiple workspaces and lakehouses, providing a unified view that helps prevent data silos and improves transparency. It is designed to support scalable lineage tracking, advanced search, and focused navigation, making it easier for data teams to trace upstream and downstream dependencies.","feature_name":"Fabric Materialized Lake Views - Multi Workspace/Lakehouse execution support","last_modified":"2026-05-19","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"f152e5fd-1fbf-f011-bbd3-000d3a3740cc","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Updates to default data destination behavior in Dataflow Gen2","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/updates-to-default-data-destination-behavior-dataflow-gen-2","feature_description":"A commonly requested new capability for Output Destinations is the ability to merge, or upsert, data into previously loaded rows in the destination table. We aim to provide this support for Fabric Lakehouse destination.","feature_name":"Dataflows - Merge/Upsert Support for Output Destinations","last_modified":"2026-05-16","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"67d0f235-4521-f011-9989-6045bd030c4d","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Introducing SQL Audit Logs for Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/introducing-sql-audit-logs-for-fabric-datawarehouse","feature_description":"This feature will introduce enhancements to SQL Audit Logs, including support for audit event filtering and query size enforcement.These capabilities help customers reduce audit noise and improve audit completeness by controlling which events are captured and preventing oversized queries from exceeding audit limits.","feature_name":"SQL Audit Logs Improvments","last_modified":"2026-05-14","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"e75dc0ae-3722-f011-9989-000d3a302e4a","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Fabric Data Pipelines \u2013 Advanced Scheduling Techniques (Part 2: Run a Pipeline on a Specific Day)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fabric-data-pipelines-advanced-scheduling-techniques-part-2-run-a-pipeline-on-a-specific-day","feature_description":"Pipelines in Fabric Data Factory emphasize general re-use patterns and metadata driven methodologies. Now with support for pipeline parameters in schedules you can create multiple schedules per pipeline with different parameter values enabling incredibly powerful generic pipeline workflow patterns.","feature_name":"Pipelines - Support pipeline parameters in schedules","last_modified":"2026-05-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"422e1a3d-056f-f011-bec2-00224804b6c3","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Run Spark Job Definitions in Pipelines with Service Principal or Workspace Identity","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/run-spark-job-definitions-in-pipelines-with-service-principal-or-workspace-identity","feature_description":"When automating your Fabric Data Factory pipelines, you will greatly benefit from having the flexibility to set the identity (i.e. user, SPN, workspace identity) of the pipeine at the time of execution. With this feature, you can now set it easily from the scheduler API and scheduler UI.","feature_name":"Pipelines - Set the "Run As" identity of the pipeline from the pipeline schedule","last_modified":"2026-05-13","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"99108f89-076f-f011-bec2-00224804b6c3","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"BULK INSERT in Fabric Data Warehouse","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/bulk-insert-statement-in-fabric-datawarehouse","feature_description":"Fabric Data Warehouse will support the bcp utility and the TDS Bulk Load API, enabling high-performance data ingestion from a variety of client tools such as bcp, SSIS, and Azure Data Factory. This integration simplifies bulk data loading into Fabric DW and supports scalable, efficient workflows. Centralized support for these APIs ensures consistency across ingestion pipelines and improves interoperability with existing tools.<br/>An example of a bcp command that loads file content into a DW table:<br/>```bcp dbo.artists in gold_artist.txt -d TextDW -c -S myworkspace.datawarehouse.fabric.microsoft.com -G -U theuser@microsoft.com ```","feature_name":"BCP","last_modified":"2026-05-13","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"d58f4693-ca80-ef11-ac21-6045bd062aa2","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Inline Scalar user-defined functions (UDFs) in Microsoft Fabric Warehouse (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/inline-scalar-user-defined-functions-udfs-in-microsoft-fabric-warehouse-preview","feature_description":"Computation-based scalar UDFs now support WHILE loops, multiple RETURN statements and deeply nested IF-THEN-ELSE blocks in the function body. They can also be used in queries that include Common Table Expressions (CTEs), and within GROUP BY, HAVING, and ORDER BY clauses.","feature_name":"Scalar UDFs - Expanded T-SQL constructs and query shapes for computation-based UDFs","last_modified":"2026-05-13","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q2 2026","release_item_id":"c418b243-333f-f111-88b5-6045bd0a8ec1","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Announcing GitHub integration for source control (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-github-integration-for-source-control-preview","feature_description":"Ability to connect Fabric workspace to repositories which reside on GitHub Enterprise Cloud with data residency","feature_name":"Git Integration provider - GitHub Enterprise Cloud with data residency support","last_modified":"2026-05-13","product_id":"c6da6b3b-ded6-ee11-9079-000d3a310f67","product_name":"Fabric Developer Experiences","release_date":"Q2 2026","release_item_id":"76396892-86db-f011-8544-000d3a3b0571","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Currently the largest message size Eventstream can support is 1MB. This new feature will push this limit to bigger size to meet broader requirements.","feature_name":"Eventstream supports large message size","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"fc3ce8fd-6e2f-f011-8c4d-000d3a34671f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Acquiring Real-Time Data from New Sources with Enhanced Eventstream","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/acquiring-real-time-data-from-new-sources-with-enhanced-eventstream","feature_description":"The Reference Data Join feature in Eventstream enables you to enrich streaming events by joining them with static or slowly changing delta tables in one lake.User can reference any one lake delta tables using lakehouse, and join it with streaming data in eventstream to enahace and add additional context to their events during processing.","feature_name":"Eventstream reference data join with one lake delta tables","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"816ee551-33a5-f011-bbd3-000d3a30273e","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"New Eventstream sources: MQTT, Solace PubSub+, Azure Data Explorer, Weather & Azure Event Grid","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/new-eventstream-sources-mqtt-solace-pubsub-azure-data-explorer-weather-event-grid","feature_description":"This feature enables customers to develop or re-use their own Kafka connectors or open-source Kafka connectors on Fabric when there are no pre-built streaming connectors available.","feature_name":"Eventstream supports customers to upload own connector","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"60246c0e-762f-f011-8c4d-000d3a34671f","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplifying Data Ingestion with Copy job \u2013 Incremental Copy GA, Lakehouse Upserts, and New Connectors","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/simplifying-data-ingestion-with-copy-job-incremental-copy-ga-lakehouse-upserts-and-new-connectors","feature_description":"The integration of Real-Time Intelligence with CopyJob in Microsoft Fabric empowers organizations to stream incremental updates from traditionally 'batch' data sources directly into Eventstreams. Simultaneously, users can ingest data from streaming sources into any CopyJob destination that supports incremental updates. This unified experience makes it easy to build hybrid data platforms that combine batch and streaming assets. As a result, creating event-driven and AI-powered applications--like dashboards, alerts, and compliance solutions--no longer requires manual data movement or complex integrations.","feature_name":"Unify batch & streaming data platforms with CopyJob & Real-Time Intelligence","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q3 2026","release_item_id":"403b7b13-0ca4-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Mirroring for SQL Server in Microsoft Fabric (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/22820","feature_description":"Mirror DB integration with Real-Time Intelligence in Microsoft Fabric lets organizations track and process updates to their Mirrored data sources like Azure SQL, Mirroed Snowflake and others. By enabling change feeds on these mirrored datasets, users can quickly build event-driven and AI-powered applications--like dashboards and alerts--without complex setup or deep knowlege of Apache Spark. This streamlined approach delivers instant insights and automation, helping businesses respond to evolving data in real time.","feature_name":"Enable stream processing and real-time analytics on Mirror DB change feeds","last_modified":"2026-05-07","product_id":"58cb90aa-4203-ef11-a1fd-000d3a36eea4","product_name":"Real-Time Intelligence","release_date":"Q2 2026","release_item_id":"1dbe5728-0aa4-f011-bbd3-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Fabric User Data Functions (Generally Available)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/announcing-fabric-user-data-functions-now-in-general-availability","feature_description":"Fabric function support in T-SQL enables users to call external data functions directly from their SQL queries within the Data Warehouse. This allows for advanced processing, enrichment, and transformation while centralizing logic and promoting code reuse across different engines and workloads. By embedding Fabric functions into SQL, teams can streamline operations and maintain consistency across platforms.For example, if you defined a `my_fabric_function` user data function, you cna call it from T-SQL code using the folowing T-SQL syntax:<br/>```SELECT *, my_fabric_function(param1, param2...) FROM table```","feature_name":"Fabric Functions in DW","last_modified":"2026-05-06","product_id":"fa3a73cd-dcd6-ee11-9079-000d3a310f67","product_name":"Data Warehouse","release_date":"Q3 2026","release_item_id":"64884d05-7482-ef11-ac21-6045bd062aa2","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Simplify your data movement with Copy job: CDC with SQL estate (Generally Available)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Simplify-your-data-movement-with-Copy-job-CDC-with-SQL-estate/ba-p/5184211","feature_description":"Customers can use Copy job to automatically capture inserts, updates, and deletions from any supported CDC source store, and replicate them to the new destination stores including Oracle without requiring a watermark column.","feature_name":"Copy job - CDC based replication to Oracle","last_modified":"2026-05-06","product_id":"a821f83f-dbd6-ee11-9079-000d3a310f67","product_name":"Data Factory","release_date":"Q3 2026","release_item_id":"71ffb0dc-c99a-f011-b4cc-000d3a5b0efa","release_status":"Planned","release_type":"Public preview"},{"active":true,"blog_title":"Native Execution Engine available at no additional cost!","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/native-execution-engine-available-at-no-additional-cost","feature_description":"Enhance the Native Execution Engine in Microsoft Fabric Spark to support CSV ingestion natively, minimizing fallbacks to the Spark JVM and improving performance for data ingestion workflows.Unlock native engine speedups for foundational ingestion scenarios.Reduce cost and latency during ETL, especially for write-heavy delta loads for customers given majority of users  have CSV based file dependencies","feature_name":"CSV Support for Native Execution Engine","last_modified":"2026-05-06","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"bfd0bc17-7dba-f011-bbd2-0022480a2ecf","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Fine-grained ReadWrite access to data with OneLake security (Preview)","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/fine-grained-readwrite-access-to-lakehouse-data-with-onelake-security","feature_description":"Configure write permissions for folders in a lakehouse using OneLake security. With this feature, viewers can be given selective write access to certain folders in the lakehouse enabling teams to write data without having Contributor or higher roles in the workspace.","feature_name":"OneLake security - ReadWrite permission - GA","last_modified":"2026-05-06","product_id":"338c69fe-dcd6-ee11-9079-000d3a310f67","product_name":"OneLake","release_date":"Q2 2026","release_item_id":"5d44c899-f7d1-f011-bbd3-00224808fcf0","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Custom Live Pools for Fabric Data Engineering (Preview)","blog_url":"https://community.fabric.microsoft.com/t5/Fabric-Updates-Blog/Custom-Live-Pools-for-Fabric-Data-Engineering-Preview/ba-p/5187356","feature_description":"Customers can create custom compute pools for Spark with libraries and other items specific to their scenario and keep them warm like they can today with starter pools.","feature_name":"Custom Live Pools","last_modified":"2026-05-06","product_id":"a731518f-36ca-ee11-9079-000d3a341a60","product_name":"Data Engineering","release_date":"Q2 2026","release_item_id":"11fd2c23-e28c-ef11-ac21-00224804e9b4","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":null,"blog_url":null,"feature_description":"Control which tooltips show and how the tooltips display on your visual","feature_name":"Tooltip options for Power BI visuals","last_modified":"2026-05-06","product_id":"642a8375-05fc-ee11-a1ff-000d3a341a60","product_name":"Power BI","release_date":"Q2 2026","release_item_id":"aa43ab07-d11f-f111-8341-6045bd0a8ec1","release_status":"Planned","release_type":"General availability"},{"active":true,"blog_title":"Creator Improvements in the Data Agent","blog_url":"https://blog.fabric.microsoft.com/en-us/blog/creator-improvements-in-the-data-agent","feature_description":"The Creator Agent is a specialized AI assistant designed to help data agent creators configure, improve, and optimize their data agents by generating and refining Agent Instructions, Data Source Instructions, and Few-Shot Examples. It addresses common customer pain points such as confusion about where to put instructions, uncertainty about the effectiveness of few shots, and difficulty diagnosing why an agent produces poor results. The agent works in a collaborative, chat-based 'setup' mode, where it analyzes existing configurations, explores database schemas and query patterns, and recommends improvements that users can explicitly accept or reject. It is designed to detect ambiguity and contradictions across configurations and suggest clearer, more consistent alternatives. Initially focused on SQL data sources, the Creator Agent is intended to expand to additional data sources (e.g., KQL, semantic models) over time. 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It models how Eventhouse behaves when a minimum CU level is defined and allows users to schedule different Min-Capacity values per hour of the day and per day of the week. By visualizing baseline consumption, peak-time protection, and off-hours reduction, the planner makes it easy to choose the right minimum levels, avoiding throttling during busy periods while preventing unnecessary consumption during quiet times. 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