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DTSTAMP:20261103T091709Z
DTSTART:20261105T223000Z
DTEND:20261105T231500Z
SUMMARY:Transactional Change Stream Processing With Debezium and Apache Fli
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LOCATION:Room 2002
DESCRIPTION:Gunnar Morling\n\nApache Flink is commonly used for processing 
 Debezium change data events: for running continuous queries enabling real-
 time analytics as the data in your OLTP store changes\, for filtering and 
 transforming change data feeds\, or for creating denormalized data views s
 ourced from the change data events of multiple tables. While powerful\, th
 is processing happens message by message\, resulting in the emission of pa
 rtial results to downstream consumers while the change events originating 
 from a single transaction in the source database are processed. Oftentimes
 \, that’s not desired: instead\, results should only be emitted once all
  the events from a transaction have been received. In this talk\, we’ll 
 explore how this problem can be solved by leveraging Debezium’s transact
 ion metadata. It describes how many events of which type belong to a given
  transaction in a database like Postgres or MySQL. We’ll show how to tak
 e advantage of this information for implementing an innovative watermarkin
 g approach which\, together with a custom output buffer\, ensures that eve
 nt consumers will only ever receive transactionally consistent data.
URL:https://sf26.rmoff.net/s/transactional-change-stream-processing-with-de
 bezium-and-128
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