Featured Posts

Tags
analytics announcement announcement discussion survey apache kafka apache kafka apicurio avro aws batch caassandra camel cassandra cdc channels community community stories connectors containers cqrs custom data engineering data replication datalake db2 ddd debezium debezium server debezium ui deduplication discussion dlt dlthub docker elasticsearch event sourcing exactly once semantics example examples features fedora flink hiring ibmi iceberg images informix integration introduction jaeger jdbc json kafka kafka streams kafka streams kogito ksql kubernetes lakehouse machine learning mariadb metrics microservices mongo mongodb monitoring mysql news newsletter notifications online learning operator oracle outbox performance postgres presentation production pydbzengine python quarkus questdb rds releases schema scylla secrets sentry serialization signaling smt snapshots spanner spark sql sqlserver tensorflow testcontainers tests time series timescaledb topics tracing transactions ui vagrant vitess website

Debezium Blog

When it comes to replicating operational data for analytics, Change Data Capture (CDC) is the gold standard. It offers scalability, near real-time performance, and captures all data modifications, ensuring your analytical datasets are always up-to-date. Debezium is a leading tool in this space, connecting to a wide range of databases and exporting CDC events in various formats like JSON and Avro, making integration with diverse systems a breeze.

While Debezium itself is a Java-based project, the data engineering world increasingly relies on Python. This blog post demonstrates how to leverage Debezium within a Python environment, using pydbzengine. We’ll explore how to use these technologies to build a robust and scalable CDC solution.

Read More    

In this post, we are going to talk about a CDC-CQRS pipeline between a normalized relational database, MySQL, as the command database and a de-normalized NoSQL database, MongoDB, as the query database resulting in the creation of DDD Aggregates via Debezium & Kafka-Streams.

Read More