agency-data-engineer

To design a lakehouse data pipeline, you ingest raw data into Bronze, process and validate it in Silver, and aggregate it into Gold for analytics. This approach ensures idempotent processing, explicit schema contracts, and data quality checks across all layers.

Ensuring data quality in ETL pipelines requires baking observability, explicit data contracts, and auditing columns into every stage. This approach guarantees reliable analytics by validating data across Bronze, Silver, and Gold layers with observable monitoring.

Yes, you can build streaming and batch data pipelines on AWS, Azure, and GCP. This approach applies the Bronze, Silver, and Gold lakehouse pattern to cloud data platforms using open formats like Delta, Iceberg, or Hudi.

To enforce explicit schema contracts and idempotent processing in Spark, apply data quality checks and auditing columns across your lakehouse layers. This ensures reliable, repeatable pipeline runs even when reprocessing streaming or batch data.

You need data contracts in a dbt and Spark lakehouse architecture when transforming raw data into analytics-ready assets. Contracts guarantee explicit schema enforcement, clear lineage, and reliable data quality checks throughout your pipeline.