Data Engineering Best Practices on Databricks

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    Architecture / Concept Overview: Data Engineering Best Practices on Databricks

    Best-practice data engineering on Databricks follows a layered architecture with clear separation of concerns, automated quality gates, and infrastructure-as-code deployment patterns.

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    *Best-practice pipeline architecture with quality gates and monitoring at every layer.*

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    *Six pillars of data engineering excellence on Databricks.*

    Key Terms

    Prerequisites and Setup

    • A Databricks workspace with Unity Catalog enabled.
    • Familiarity with Lakeflow Declarative Pipelines, Jobs, and Delta Lake.
    • A version-controlled repository for pipeline code (Databricks Repos or external Git).

    Step-by-Step Implementation

      Configuration Reference

      Data Engineering Best Practices on Databricks configuration options
      PracticeConfigurationRecommendation
      Auto-optimize writesdelta.autoOptimize.optimizeWriteEnable on all tables
      Auto-compactdelta.autoOptimize.autoCompactEnable on append-heavy tables
      Change data feeddelta.enableChangeDataFeedEnable on silver tables for downstream CDC
      Vacuum retentiondelta.deletedFileRetentionDuration7 days minimum for time travel
      Logging levelspark.databricks.delta.properties.defaults.logRetentionDuration30 days
      Z-Order columnsOPTIMIZE ... ZORDER BYFrequently filtered columns

      Monitoring, Cost, and Security Considerations

      Common Pitfalls and Recommended Patterns

        Frequently Asked Questions