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You can also directly query ksqlDB's tables of state, eliminating the need to sink your data to another data store. Using ksqlDB, you can run any Kafka Connect connector by embedding it in ksqlDB's servers. ksqlDB helps to consolidate this complexity by slimming the architecture down to two things: storage (Kafka) and compute (ksqlDB). It's challenging to monitor, secure, and scale all of these systems as one. In addition to your database, you end up managing clusters for Kafka, connectors, the stream processor, and another data store. Running all of the above systems is a lot to manage. This can work, but is there a better way? Why ksqlDB? ¶ As the materialization updates, it's updated in Redis so that applications can query the materializations. The changelog is stored in Kafka and processed by a stream processor. One way you might do this is to capture the changelog of MySQL using the Debezium Kafka connector. This enables creating multiple distributed materializations that best suit each application's query patterns.
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This is why materialized views can offer highly performant reads.Ī standard way of building a materialized cache is to capture the changelog of a database and process it as a stream of events. In contrast with a regular database query, which does all of its work at read-time, a materialized view does nearly all of its work at write-time. Transforming columns with structured dataĬonfigure ksqlDB for Avro, Protobuf, and JSON schemasĪ materialized view, sometimes called a " materialized cache", is an approach to precomputing the results of a query and storing them for fast read access.