Production Real-Time Streaming Pipeline

How can we assemble stream ingestion, watermarking, JSON unnesting, static enrichment, and checkpointed export into a production job?

Production⏱ 3 Minutes⭐ 890 XP

🤔 THE QUESTION

How can we assemble stream ingestion, watermarking, JSON unnesting, static enrichment, and checkpointed export into a production job?

💡 WHAT IS IT?

Building an end-to-end real-time pipeline with robust watermarking, error isolation, and fault-tolerant checkpointing.

🎯 WHAT IS IT USED FOR?

24/7 mission-critical data engineering infrastructure processing millions of real-time transactions per minute.

💻 EXAMPLE

query = streaming_raw.withWatermark("event_time", "15 minutes").join(broadcast(df_static_lookup), "lookup_key").writeStream.format("parquet").option("checkpointLocation", "s3://lakehouse/checkpoints/prod_stream/").option("path", "s3://lakehouse/gold/live_events/").outputMode("append").start()

🎯 Mission Objectives

Practice typing production-grade PySpark code for Production Real-Time Streaming Pipeline.

✓ Full streaming pipeline
✓ Watermarking and enrichment
✓ Production streaming operations

🚀 Start Mission

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