Data Transformation & ETL Pipelines
Learn data transformation, ETL pipelines, and data processing workflows. Build robust, scalable data systems with industry best practices.
What is Data Transformation?
Data transformation is the process of converting data from one format, structure, or value set to another. It's a critical component of data integration and analytics workflows.
Common transformations include format conversion (JSON to CSV), data cleaning, normalization, aggregation, and enrichment.
Understanding ETL
Extract
Collect data from various sources like databases, APIs, files, and streaming services.
Transform
Clean, validate, enrich, and convert data into the desired format and structure.
Load
Write the transformed data to target systems like databases, data warehouses, or files.
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Common Transformation Patterns
Data Cleaning
- → Remove duplicates
- → Handle missing values
- → Fix data types
- → Standardize formats
Data Enrichment
- → Add derived fields
- → Join with reference data
- → Calculate aggregations
- → Geocode addresses
Best Practices
- → Idempotency: Ensure transformations produce the same result when run multiple times
- → Error Handling: Implement robust error handling and logging
- → Data Quality: Validate data at each stage of the pipeline
- → Performance: Optimize for throughput and latency
- → Monitoring: Track pipeline health and data quality metrics
- → Testing: Unit test transformations and integration test pipelines
Data Format Conversion
JSON ↔ CSV
Flatten nested structures for spreadsheets
JSON ↔ XML
Convert between API formats
JSON ↔ YAML
Config file transformations