Welcome to Day 20 of the Spark Mastery Series. Today we address a harsh truth:
Real data is messy, incomplete, and unreliable.
If your Spark pipeline can’t handle bad data, it will fail in production. Let’s build pipelines that survive reality.
🌟 Why Data Quality Matters
Bad data leads to:
- Wrong dashboards
- Broken ML models
- Financial losses
- Loss of trust Data engineers are responsible for trustworthy data.
🌟 Enforce Schema Early
Always define schema explicitly.
Benefits:
- Faster ingestion
- Early error detection
- Consistent downstream processing
Never rely on inferSchema in production.
🌟 Capture Bad Records, Don’t Drop Them
Using badRecordsPath ensures:
- Pipeline continues
- Bad data is quarantined
- Audits are possible This is mandatory in regulated industries.
🌟 Apply Business Rules in Silver Layer
Silver layer is where data becomes trusted.
Examples:
- Remove negative amounts
- Validate country codes
- Drop incomplete records
- Deduplicate Never mix business rules in Bronze.
🌟 Observability & Metrics
Track record counts for every job.
Example:
Input: 1,000,000
Valid: 995,000
Invalid: 5,000
If invalid spikes → alert immediately.
🌟 Delta Lake Safety Net
With Delta:
- Rollback bad writes
- Reprocess safely
- Audit changes This is why Delta is production-critical.
🚀 Summary
We learned:
- What bad records are
- How to enforce schema
- How to capture corrupt data
- How to apply data quality rules
- How to track metrics
- How Delta helps recovery
Follow for more such content. Let me know if I missed anything. Thank you
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