Automated Databricks Lakeflow Declarative Pipeline with Medallion Architecture and Autoloader

In this end-to-end high-level data engineering video, I will show you how to architecture scalable and high-performance Databricks Lakeflow Declarative Pipeline with medallion architecture and databricks autoloader cloud_files function to continuously ingest read data files as they arrive.

You'll learn how to:
💹 Create databricks unity catalog and schemas for landing, bronze, silver and gold medallion architecture.
💹 Create Managed UC volumes, directories and upload files
💹 Create Lakeflow ETL Pipeline using advanced mode
💹 Manage source code files and folder
💹 Read files from volumes directories and write to bronze schema with autoloader as streaming live tables
💹 Read and clean bronze streaming live tables and write to silver schema
💹 Combine fact and dimension streaming live tables with left join
💹 Implement Materialized Views with Constraints
💹 Manage Pipeline Graph
💹 Create business-level aggregates queries and write to gold schema
💹 Schedule the ETL job
💹 Visualize gold-level materialized queries using AI/BI dashboard
💹 Load more files to fact sales volume directory
💹 Monitor the incremental data loading in the AI/BI dashboard

This solution is typical of how Databricks Lakeflow Declarative Pipeline should be architected in a production environment and this can be adopted by enterprises.

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