Fabric RTI 101: Replay and Reprocessing

In a real-time data system, it’s not enough to process events once and move on. There are many cases where you need to replay or reprocess event data that has already passed through the system.

Replay and reprocessing allow you to go back in time — to re-run data through your pipelines as if it were arriving again in real time.

This capability is especially useful for debugging issues, conducting compliance audits, or retraining machine learning models with historical data.

For example, imagine that you’ve discovered a logic error in one of your transformations. Once you’ve fixed it, you can replay the stored event stream through the corrected logic to produce accurate results. Similarly, if a regulatory audit requires proof of how a system behaved at a specific time, replaying the original event data provides verifiable evidence.

In Microsoft Fabric, the recommended approach is to persist event streams into OneLake. By saving raw events in a durable storage layer, you can reprocess them later without relying on the original event source. This persistence also provides the foundation for combining real-time data with historical context.

Delta Lake plays a key role here. Its time travel feature allows you to query previous versions of tables, making it possible to analyze or reprocess data as it existed at any given point in time. That’s particularly valuable for iterative development and for maintaining reproducibility in analytics.

Replay and reprocessing effectively bridge the gap between streaming and batch analytics. They give you the flexibility to correct, retrain, or reanalyze data using the same infrastructure — ensuring that real-time systems remain not just fast, but also auditable, reliable, and adaptable over time.

Learn more about Fabric RTI

If you really want to learn about RTI right now, we have an online on-demand course that you can enrol in, right now. You’ll find it at Mastering Microsoft Fabric Real-Time Intelligence

2026-08-15