Wednesday, November 12, 2025

CES - Change Event Streaming in SQL Server 2025

As we know that Microsoft's data team has released a preview of SQL Server 2025, which will include a lot of great features, such as Database Mirroring to Microsoft Fabric, Flexible AI Model Management and many more. One of the most interesting feature "Change Event Streaming" is coming within SQL Server 2025 as the new feature release. 


SQL Server has already very interesting existing features like Change Data Capture (CDC) and Change Tracking (CT), as well as the introduction of new mechanisms for capturing and streaming data changes in real-time or near real-time. 

Change Event Streaming (CES) is a new native engine capability in SQL Server 2025 that allows you to stream database changes (inserts, updates, deletes, and possibly schema changes) as real-time events to external systems (e.g., Azure Event Hubs, Kafka) instead of relying purely on older batch/change capture approaches.

Current Limitations and Opportunities

SQL Server currently offers two primary mechanisms for tracking data changes: Change Data Capture (CDC) and Change Tracking (CT). While both are valuable, they have limitations:

  • Change Data Capture (CDC): Provides detailed historical changes, but can be complex to configure and manage. It also introduces overhead due to the asynchronous capture process and the need for cleanup jobs. CDC is also not available in all SQL Server editions.

  • Change Tracking (CT): Simpler to configure than CDC, but only provides information about which rows have changed, not the actual changes themselves. This requires additional queries to retrieve the changed data, potentially impacting performance.

SQL Server 2025 has the opportunity to address these limitations and provide a more robust and versatile change event streaming solution. Key areas for improvement include:

  • Real-time or Near Real-time Streaming: Reducing latency between data changes and their availability to downstream consumers.

  • Simplified Configuration and Management: Making it easier to set up and maintain change event streams.

  • Improved Performance and Scalability: Minimizing the impact on the source database and supporting high-volume change rates.

  • Enhanced Data Transformation and Enrichment: Providing mechanisms to transform and enrich change events before they are streamed.

  • Integration with Modern Data Architectures: Seamlessly integrating with cloud-based data lakes, message queues, and stream processing platforms.

  • Support for a wider range of data types: Expanding support for data types like JSON, XML, and spatial data.