Article: From Batch to Micro-Batch Streaming: Lessons Learned the Hard Way in a Delta Index Pipeline
📰 InfoQ AI/ML
Learn how to migrate a batch pipeline to micro-batch streaming using Spark Structured Streaming, improving predictability and reliability
Action Steps
- Migrate a batch pipeline to micro-batch Spark Structured Streaming to improve efficiency
- Replace fragile S3 completion markers with partition-based watermarks for better reliability
- Implement overlap-window correctness to ensure accurate data processing
- Design restart strategies for predictable pipeline behavior
- Test and validate the micro-batch pipeline for optimal performance
Who Needs to Know This
Data engineers and architects can benefit from this article to improve their pipeline's efficiency and scalability. It's also relevant for teams working with Spark and Structured Streaming
Key Insight
💡 Micro-batch streaming can improve pipeline predictability and reliability, but requires careful design and implementation
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🚀 Migrate from batch to micro-batch streaming with Spark Structured Streaming for improved efficiency and reliability! 📈
Key Takeaways
Learn how to migrate a batch pipeline to micro-batch streaming using Spark Structured Streaming, improving predictability and reliability
Full Article
This article describes how a production delta-index pipeline migrated from scheduled batch to micro-batch Spark Structured Streaming. It covers why record-level streaming was rejected, how partition-based watermarks replaced fragile S3 completion markers, overlap-window correctness, and restart-as-design strategies for better predictabi
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