agency-data-engineer

Architecting a medallion lakehouse pipeline involves structuring data into Bronze, Silver, and Gold layers using Delta Lake to ensure quality and lineage. This approach cleanses raw events and aggregates them into business metric tables.

Enforcing schema contracts and data quality in Spark pipelines requires automated validation checks using Great Expectations. This ensures idempotent execution and prevents silent data corruption during processing.

Yes, dbt can build Gold layer aggregations on a Delta Lakehouse. It models the transformed data from the Silver layer into business-ready metric tables, ensuring reliable and cost-optimized incremental processing.

The best way to process streaming Kafka events with Spark is to ingest raw events into a Bronze layer, cleanse them into a Silver layer, and aggregate them into Gold-layer business metrics with targeted freshness.

Yes, Delta Lake supports idempotent execution for incremental data processing. It enforces schema contracts and automated data quality checks to maintain pipeline reliability and prevent silent data corruption.

You need great_expectations for data engineering pipelines to apply automated data quality validation. It enforces schema contracts and prevents silent data corruption within your medallion architecture.