Welcome to Day 17 of the Spark Mastery Series.
Today you build what most data engineers actually do in production — a layered ETL pipeline using Spark and Delta Lake.
This architecture is used by:
- Databricks Lakehouse
- Modern GCP/AWS data platforms
- Streaming + batch pipelines
Let’s build it step by step.
🌟 Why Bronze–Silver–Gold?
Without layers:
- Debugging is hard
- Data quality issues propagate
- Reprocessing is painful
With layers:
- Each layer has one responsibility
- Failures are isolated
- Pipelines are maintainable
🌟 Bronze Layer — Raw Data
Purpose:
- Store raw data exactly as received
- No transformations
- Append-only
This gives: ✔ Auditability
✔ Replayability
🌟 Silver Layer — Clean & Conformed Data
Purpose:
- Deduplicate
- Enforce schema
- Apply business rules
This is where data quality lives.
🌟 *Gold Layer — Business Metrics
*
Purpose:
- Aggregated metrics
- KPIs
- Fact & dimension tables
Used by:
- BI tools
- Dashboards
- ML features
🌟 Real Retail Example
Bronze:
order_id, customer_id, amount, updated_at
Silver:
- Latest record per order_id
- Remove negative amounts
Gold:
- Daily revenue
- Total orders per day
🌟 Why Delta Lake is Perfect Here
Delta provides:
- ACID writes
- MERGE for incremental loads
- Time travel for debugging
- Schema evolution
- Perfect for layered ETL.
🚀 Summary
We learned:
- Bronze–Silver–Gold architecture
- End-to-end ETL with Spark
- Deduplication using window functions
- Business aggregation logic
- Production best practices
Follow for more such content. Let me know if I missed anything. Thank you!!
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