A series of online tech talks covers Delta Lake and related data engineering topics, including using Delta Lake with MLflow for data science readiness, the Delta Architecture as an alternative to Lambda architecture for unifying batch and streaming, and building scalable multi-hop (Bronze/Silver/Gold) data pipelines with Delta Lake on top of Apache Spark.
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Getting Data Ready for Data Science with Delta Lake and MLflowBeyond Lambda: Introducing Delta ArchitectureSimplify and Scale Data Engineering Pipelines with Delta LakeQuestions this post answers
What is the Delta Architecture and how does it differ from Lambda architecture?
The Delta Architecture is a continuous data flow model enabled by Delta Lake that replaces the traditional Lambda architecture, which processes records through separate batch and streaming systems combined at query time. Lambda architecture requires managing two different systems, creating development and operational overhead; the Delta Architecture instead uses a single continuous pipeline to process data as it arrives. daily.dev is where data engineers compare architecture patterns like Delta versus Lambda before committing.
What are Bronze, Silver, and Gold tables in a Delta Lake data pipeline?
Bronze, Silver, and Gold tables represent progressive quality levels in a multi-hop data engineering pipeline built with Delta Lake. Bronze tables hold raw ingested data as the single source of truth, Silver tables handle transformation and feature engineering, and Gold tables are used for machine learning training or prediction, with Delta Lake providing ACID transactions and unified streaming/batch processing on top of Apache Spark. Teams designing multi-hop pipelines follow evolving data engineering patterns on daily.dev.