New in Confluent Cloud: Making Data & Pipelines Accessible for AI-Ready Streaming | Learn More
Now that KSQL is available for production use as a part of the Confluent Platform, it has never been easier to run the open-source streaming SQL engine for Apache Kafka®. Which is not to say that everything is entirely obvious to the new user. A beginning or even intermediate streaming SQL user might still need a hand, and we’re here to give you one!
Maybe you’ve already been using KSQL, and you have fallen in love with its intuitive syntax for creating and enriching streams of real-time data. Maybe you run Confluent Platform, and you already love the handy KSQL user interface and Confluent Control Center’s stream monitoring capabilities to monitor the performance of your KSQL queries.

Or maybe not yet. Regardless, we can tell you that now is the time to level up your KSQL. Whether you are brand new to it or ready to take it to production, now you can dive deep on core KSQL concepts, streams and tables, enriching unbounded data and data aggregations, scalability and security configurations, and more. Stay tuned with us over the next few weeks as we release the Level Up Your KSQL video series that enables you to really understand KSQL.
There are more videos besides these. We also cover:
Interested in more? Learn more about what KSQL can do:
Batch CDPs can't capture user intent as it forms. By the time a nightly sync runs, the moment is gone. This guide covers the streaming architecture behind real-time personalization, from sub-100ms ad bidding to cross-channel orchestration, with recommendation patterns built on Kafka and Flink.
Separate batch and streaming pipelines for ML features cause training-serving skew. DoorDash measured a 35.7% feature mismatch in their dual setup. This guide covers a unified kappa architecture using Flink to compute features once for both training and serving, plus a 2026 tooling comparison.