data-lake-platform

Data lake architecture combines ingestion tools like dlt and Airbyte, transformation via SQLMesh or dbt, and open storage formats like Iceberg or Delta Lake with ACID guarantees. This approach enables schema evolution, data quality controls, and scalable deployments across self-hosted or cloud environments while maintaining cost efficiency.

Both dbt and SQLMesh support lakehouse transformation workflows. SQLMesh and dbt can model data independently; choose based on your lineage tracking, state management, and scheduling needs. Integrate either with orchestrators like Dagster or Airflow for end-to-end pipeline automation.

Iceberg and Delta Lake integrate with multiple query engines including ClickHouse, DuckDB, Doris, and StarRocks. Each engine offers different performance characteristics and feature support, so selection depends on your workload type, latency requirements, and analytical patterns.

The medallion architecture organizes data into bronze (raw), silver (cleaned), and gold (analytics-ready) layers. Use dbt or SQLMesh to define transformations between layers, store each layer in Iceberg or Delta Lake, and orchestrate via Dagster or Airflow with governance and quality checks at each stage.

Streaming ingestion via Kafka or Flink enables real-time data flows into your lake. Combine streaming sources with batch tools like dlt and Airbyte, transform via SQLMesh or dbt, and orchestrate the full pipeline with Dagster or Airflow to maintain consistency across ingestion patterns.

Production data lakes require schema enforcement, quality assurance checks, and governance applied during ingestion and modeling stages. Implement via transformation logic in dbt or SQLMesh, orchestrate validations in Dagster or Airflow, and leverage ACID storage formats to prevent data corruption and ensure compliance.