Build robust ML data pipelines with medallion architecture, automated validation, schema enforcement, and Git-like data versioning. Ensure data quality from ingestion through model training.
Transaction data pipelines, fraud feature engineering, regulatory data lineage
Patient data validation, clinical data pipelines, HIPAA-compliant versioning
User behavior pipelines, product catalog processing, recommendation features
IoT sensor pipelines, quality metrics processing, predictive maintenance data
Content metadata pipelines, engagement metrics, personalization features
Choose ETL vs ELT and implement medallion layers (Bronze/Silver/Gold)
Define data schemas and infer from training data using TFDV or Great Expectations
Create declarative expectations for data quality, ranges, and relationships
Implement Git-like versioning with lakeFS or DVC for reproducibility
Monitor training/serving data for distribution drift and schema changes
Integrate validation into pipelines with automated quality gates
| Component | Function | Tools |
|---|---|---|
| Medallion Architecture | Bronze/Silver/Gold data quality tiers | Delta Lake, Apache Iceberg, Hudi |
| Data Validation | Schema validation, anomaly detection, skew detection | TensorFlow Data Validation, Great Expectations |
| Data Versioning | Git-like version control for data lakes | lakeFS, DVC, Delta Lake Time Travel |
| Expectations | Declarative data quality rules | Great Expectations, Pandera, Deequ |
| Pipeline Orchestration | DAG execution, dependency management | Apache Airflow, Prefect, Dagster |
| Data Processing | Batch and streaming transformations | Apache Spark, Flink, dbt |
Let us help you implement robust data infrastructure that ensures ML-ready data quality.
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