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Data engineering patterns for CDC, streaming pipelines, synchronization, observability data, and production data movement.

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Data Engineering

Windowing in Stream Processing: Tumbling, Sliding, and Session Windows

Windowing in Stream Processing: Timing is Everything Mental Model > Applying Staff-level engineering principles to build robust, production-grade software. In stream processing (Kafka Streams, Flink, Spark Streaming), yo…

Apr 20, 20267 min readAdvancedDeep Dive

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Kafka Streams: Real-Time Stream Processing Without a Separate Cluster

Kafka Streams is a Java library for building real-time stream processing applications. Unlike Flink or Spark Streaming, it has no separate cluster — it runs as a library inside your Java application. Each instance of you…

Apr 24, 20257 min read

Deep DiveMessaging & Event-Driven Mastery

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Data EngineeringAdvanced

Change Data Capture with Debezium: Real-Time Data Synchronization Patterns

Change Data Capture (CDC) is one of those techniques that, once you understand it, you see it everywhere. The pattern: instead of your application explicitly publishing events when data changes, let the database engine i…

Feb 1, 202511 min read

Deep DiveKafka Production Playbook

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