Ace the CCDAK Apache Kafka Challenge 2026 – Ignite Your Data Streaming Success!

When you join two KTables, the result is another KTable that holds the merged value for each key. That means the output type is a KTable, not a stream, because you’re combining state from two tables into a new table-like view of the data. The operation is non-windowed by default, since it uses the latest known values for each key rather than aggregating over a time window.

Co-partitioning is required because both input tables must be partitioned on the same key so updates for a given key land on the same partition and can be joined without an extra repartition step. If they aren’t co-partitioned, Kafka Streams will repartition behind the scenes, which you want to avoid for performance reasons.

As for the join type, the join is based on the keys that identify records in both tables, though you can structure your data so that a foreign key in one side can serve as the join key after rekeying. In other words, it’s fundamentally a key-based join, with the practical option to use a foreign-key mapping if your data model requires it.