Data Science
Data Engineering Best Practices for ML Projects
Build reliable data pipelines for machine learning. Data quality, validation, versioning, and automation.
November 28, 2024
2 min read
By Uğur Kaval
Data EngineeringETLData QualityMachine Learning
Data Engineering Best Practices for ML Projects
Data quality is the foundation of successful ML. Here are best practices for data engineering.
Data Quality
Validation
Validate data at every step:
- Schema validation
- Range checks
- Null handling
- Outlier detection
Monitoring
Track data quality metrics:
- Completeness
- Accuracy
- Consistency
- Timeliness
Data Versioning
Why Version Data?
- Reproducibility
- Debugging
- Rollback capability
- Compliance
Tools
- DVC (Data Version Control)
- Delta Lake
- LakeFS
Pipeline Design
Idempotency
Pipelines should produce same results when run multiple times.
Incremental Processing
Process only new data when possible.
Error Handling
Graceful failure and retry logic.
Logging
Comprehensive logging for debugging.
Storage
Data Lake vs Data Warehouse
- Lake: Raw data, schema-on-read
- Warehouse: Processed data, schema-on-write
File Formats
- Parquet: Columnar, efficient for analytics
- Delta: Parquet + ACID transactions
- JSON: Flexible but less efficient
Orchestration
Tools
- Apache Airflow
- Prefect
- Dagster
DAG Design
Keep DAGs simple and modular.
Best Practices
- Test your data: Unit tests for transformations
- Document schemas: Future you will thank you
- Monitor freshness: Alert on stale data
- Separate concerns: Ingestion, transformation, serving
Conclusion
Good data engineering is invisible when it works. Invest in quality and automation.
