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Solid hands-on tutorial from MarkTechPost on building unified Apache Beam pipelines that handle both batch and streaming data with the same codebase. Event-time windowing and late data handling are crucial skills when you're building production ML systems that need to process data reliably. Worth bookmarking if you're working on data infrastructure.

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A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with Event-Time Windowing Using DirectRunner

In this tutorial, we demonstrate how to build a unified Apache Beam pipeline that works seamlessly in both batch and stream-like modes using the DirectRunner. We generate synthetic, event-time–aware data and apply fixed windowing with triggers and allowed lateness to demonstrate how Apache Beam consistently handles both on-time and late events. By switching only the […] The post A Coding Implementation to Build a Unified Apache Beam Pipeline Demonstrating Batch and Stream Processing with

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