Data Engineering Best Practices for ML Projects

# 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 1. **Test your data**: Unit tests for transformations 2. **Document schemas**: Future you will thank you 3. **Monitor freshness**: Alert on stale data 4. **Separate concerns**: Ingestion, transformation, serving ## Conclusion Good data engineering is invisible when it works. Invest in quality and automation.