Day 27: Building Exactly-Once Streaming Pipelines with Spark & Delta Lake

Welcome to Day 27 of the Spark Mastery Series.
Today we combine Structured Streaming + Delta Lake to build enterprise-grade pipelines.

This is how modern companies handle:

• Real-time ingestion
• Updates & deletes
• CDC pipelines
• Fault tolerance

🌟 Why Exactly-Once Matters

Without exactly-once:

• Metrics inflate
• Revenue doubles
• ML models break
• Trust is lost

Delta Lake guarantees correctness even during failures.

🌟 The ForeachBatch Pattern

foreachBatch is the secret weapon for streaming ETL.

It allows:

• MERGE INTO
• UPDATE / DELETE
• Complex batch logic
• Idempotent processing

This is how CDC pipelines are built.

🌟 CDC with MERGE - The Right Way

Instead of:

• Full table overwrite
• Complex joins

We use:

• MERGE INTO
• Transactional updates
• Efficient incremental processing

🌟 Real-World Architecture

Kafka / Files
   ↓
Spark Structured Streaming
   ↓
Delta Bronze (append)
   ↓
Delta Silver (merge)
   ↓
Delta Gold (metrics)

This architecture:
✔ Scales
✔ Recovers from failure
✔ Supports history & audit

🚀 Summary

We learned:

• Exactly-once semantics
• Streaming writes to Delta
• CDC pipelines with MERGE
• ForeachBatch pattern
• Handling deletes
• Streaming Bronze–Silver–Gold

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