Fabric RTI 101: Integrating with Kafka Streams

Kafka Streams is a client-side stream processing library that operates directly on Kafka topics. Unlike a separate processing engine, it runs within your own application or service, allowing data transformation to occur close to where the events are produced.

This makes it ideal for preprocessing data before it reaches Fabric. You can use Kafka Streams to perform tasks such as data enrichment, filtering, or lightweight aggregation. For instance, before sending events to Fabric, you might:

• Enrich transactions with reference data such as geographic or device information
• Filter out irrelevant messages or duplicates
• Compute simple aggregates or derive new fields

By performing these operations at the Kafka level, you reduce the data volume and complexity entering Fabric, which can lower downstream latency and resource usage.

Fabric can then consume the enriched Kafka topics through Eventstream or other connectors, treating them as just another ingestion source. This approach provides a clear separation of responsibilities: Kafka handles the edge-level transformations, while Fabric focuses on higher-level analytics, real-time monitoring, and visualization.

Kafka Streams acts as a preprocessing layer - a way to clean, standardize, or enhance data in motion before it enters Fabric’s real-time intelligence pipeline.

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

2026-10-10