ETL & Data Pipelines
How data moves from messy sources to clean analytics tables: data formats, ETL vs ELT, extraction with pagination, transformation with pandas, warehouse loading and upserts, batch processing, orchestration DAGs, idempotency, data quality checks, incremental watermarks, streaming vs batch, star schemas, and data lakes. Ends with a full mini pipeline capstone.
Every lesson runs real code in your browser, checked the moment you submit. No installing anything, no setup, no waiting.
Start ETL & Data Pipelines freeWhat this course covers
- What Is Data Engineering?
- Data Sources and Formats
- ETL vs ELT
- Extract: Pulling the Data
- Transform: Cleaning and Conforming
- Load: Into the Warehouse
- Batch Processing
- Orchestration: The Pipeline Conductor
- Idempotency and Retries
- Data Quality Checks
- Incremental Loads and Watermarks
- Streaming vs Batch
- Star Schema: Shaping Data for Analytics
- Data Lakes and Lakehouses
- Capstone: Build a Mini Pipeline
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