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Tides of Change: Real-Time Flow with Postgres, Kafka & Flink
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VIRTUAL: Apache Flink® Meetup
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Apache Flink® for Apache Kafka® Developers
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IN-PERSON: Apache Flink® Meetup
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IN-PERSON: Apache Flink® Meetup
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Apache Flink® for Apache Kafka® Developers
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Message Tracking, Fluss in Apache Flink 2.x, & Kafka-to-Iceberg Transformation
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IN PERSON: Tooling for running Apache Kafka in Production
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Crypto Streams to AI Predictions: Apache Kafka®, Apache Flink® & Apache Iceberg®
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Data Berlin Midsummer Meetup - Speeding up Analytics
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IN-PERSON! Kafka Meetup Septembre: Aiven, Flink, KStreams, Kestra, Google Cloud
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Apache Kafka® x Apache Flink® x Elastic
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Gunnar Morling
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Abstract: You've been tasked with implementing a data streaming pipeline for propagating data changes from your operational Postgres database to a search index in OpenSearch. Data views in OS should be denormalized for fast querying, and of course there should be no noticeable impact on the production database. In this session we'll discuss how to build this data pipeline using two popular open-source projects: Debezium for log-based change data capture (CDC) and Apache Flink for stream processing. Join us for this talk and learn about: * Setting up change data streams with Debezium * Efficiently building nested data structures from 1:n joins * Deployment options: Kafka Connect vs. Flink CDC