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This changes the business itself, but it also changes the architecture of the software that supports it. More importantly — going back to where we started — it's the reason that databases themselves must also change. How Event Streaming Relates
While popular stream processing technologies have tables and SQL, just like a database does, they have a different purpose. These technologies move data between different pieces of software and allow that data to be manipulated as the data flows. They use the same joins, filters, summarizations, etc., that are traditionally applied to database tables, but apply them to data-in-motion. But should technologies like these be a substitute for traditional databases? If you've ever used a stream processor like Apache Flink or Kafka Streams, or the streaming elements of Spark or ksqlDB, you're quite unlikely to think so. These technologies don't feel much like traditional databases at all. If you run a query, you will find that an answer does not come back.
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