Welcome to Day 29 of the Spark Mastery Series.
Today we build a real-world streaming system — the kind used in e-commerce, fintech, and analytics platforms.
This pipeline handles:
🌟 Why This Architecture Works
Failures are recoverable, data is trustworthy, and performance is stable.
🌟 Key Patterns Used
🌟 Interview Value
You can now explain:
🚀 Summary
We built:
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Today we build a real-world streaming system — the kind used in e-commerce, fintech, and analytics platforms.
This pipeline handles:
- Streaming ingestion
- CDC upserts
- Data quality
- Exactly-once guarantees
- Real-time KPIs
🌟 Why This Architecture Works
- Bronze preserves raw truth
- Silver maintains latest state via MERGE
- Gold serves analytics with windows & watermarks
Failures are recoverable, data is trustworthy, and performance is stable.
🌟 Key Patterns Used
- foreachBatch + MERGE for CDC
- Delta Lake for ACID & idempotency
- Watermark to bound state
- Append/update output modes
- Separate checkpoints per query
🌟 Interview Value
You can now explain:
- Exactly-once semantics
- CDC in streaming
- State management
- Watermarking
- Streaming performance tuning
🚀 Summary
We built:
- A complete real-time ETL pipeline
- CDC upserts with Delta
- Streaming metrics with windows
- Fault-tolerant design
- Production best practices
Follow for more such content. Let me know if I missed anything. Thank you!!
More...