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Best Practices for Building a Reliable Lakehouse

This is a practical playbook for building a production-grade data lakehouse. It walks through foundational principles — naming conventions, least-privilege access, automated CI/CD testing — before diving into medallion architecture. Furthermore, metadata-driven design patterns show how configuration tables and dynamic notebook orchestration eliminates hard-coded pipelines. The deck covers star schema modeling, guidance on choosing between Spark, Pandas, and SQL, and data quality enforcement using DQX with YAML data contracts. Finally, we dive into security best practices and performance optimizations.


Watch the full presentation at the below link.


 
 
 

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