Day 17: Building a Real ETL Pipeline in Spark Using Bronze–Silver–Gold Architecture
Published: (December 17, 2025 at 11:54 AM EST)
1 min read
Source: Dev.to
Source: Dev.to
Welcome to Day 17 of the Spark Mastery Series.
Today you’ll build what most data engineers actually do in production—a layered ETL pipeline using Spark and Delta Lake.
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
Benefits
- 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
| Layer | Example Transformations |
|---|---|
| Bronze | order_id, customer_id, amount, updated_at |
| Silver | - Keep latest record per order_id- Remove negative amounts |
| Gold | - Daily revenue - Total orders per day |
Why Delta Lake is Perfect Here
- ACID writes
MERGEfor incremental loads- Time travel for debugging
- Schema evolution
- Ideal for layered ETL
Summary
- Bronze–Silver–Gold architecture
- End‑to‑end ETL with Spark
- Deduplication using window functions
- Business aggregation logic
- Production best practices