Welcome to Day 21 of the Spark Mastery Series. Today we stop talking about theory and build a real production data pipeline that handles bad data gracefully.
This is the kind of work data engineers do every day.
🌟 Why Data Quality Pipelines Matter
In production:
- Bad data WILL arrive
- Pipelines MUST not fail
- Metrics MUST be trustworthy
A good pipeline: ✔ Captures bad data ✔ Cleans valid data ✔ Tracks metrics ✔ Supports reprocessing
🌟 Bronze → Silver → Gold in Action
- Bronze keeps raw truth
- Silver enforces trust
- Gold delivers insights
This separation is what makes systems scalable and debuggable.
🌟 Key Patterns Used
- Explicit schema
- badRecordsPath
- Deduplication using window functions
- Valid/invalid split
- Audit metrics table
- Delta Lake everywhere …
Welcome to Day 21 of the Spark Mastery Series. Today we stop talking about theory and build a real production data pipeline that handles bad data gracefully.
This is the kind of work data engineers do every day.
🌟 Why Data Quality Pipelines Matter
In production:
- Bad data WILL arrive
- Pipelines MUST not fail
- Metrics MUST be trustworthy
A good pipeline: ✔ Captures bad data ✔ Cleans valid data ✔ Tracks metrics ✔ Supports reprocessing
🌟 Bronze → Silver → Gold in Action
- Bronze keeps raw truth
- Silver enforces trust
- Gold delivers insights
This separation is what makes systems scalable and debuggable.
🌟 Key Patterns Used
- Explicit schema
- badRecordsPath
- Deduplication using window functions
- Valid/invalid split
- Audit metrics table
- Delta Lake everywhere
🌟 Why This Project is Interview-Ready
We demonstrated:
- Data quality handling
- Fault tolerance
- Real ETL architecture
- Delta Lake usage
- Production thinking
This is senior-level Spark work.
🚀 Summary We built:
- End-to-end data quality pipeline
- Bronze/Silver/Gold layers
- Bad record handling
- Audit metrics
- Business-ready data
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