agency-data-engineer

To build a data lakehouse pipeline with medallion architecture, structure data into Bronze, Silver, and Gold layers to ensure traceability from raw ingestion to business-ready metrics. This pattern enforces schema contracts and automated quality validation.

Delta Lake handles idempotent pipeline execution by supporting ACID transactions and schema contract enforcement. This ensures reliable, fault-tolerant data infrastructure by preventing silent data corruption and allowing pipelines to re-run without duplicating records.

Yes, you can use PySpark with Great Expectations for automated data quality monitoring. This combination enforces schema contracts and validates data reliability within ETL pipelines, preventing silent data corruption during high-throughput lakehouse processing.

The best way to implement CDC and real-time streaming in a Delta Lake lakehouse is by architecting idempotent, observable pipelines. This approach supports high-throughput, fault-tolerant data infrastructure with automated lineage tracking and incremental processing.

Migrating from a legacy warehouse to a modern lakehouse without losing data quality requires building incremental, self-healing ETL pipelines. Using a medallion architecture with automated validation ensures high data quality and freshness SLAs during the transition.

You do not strictly need dbt-core to enforce schema contracts in a Delta Lake pipeline, as Delta Lake natively supports schema enforcement. However, combining dbt-core with Great Expectations provides enhanced automated data quality validation and lineage tracking.