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. If the video has really helped you to have better understanding of how Lakeflow Declarative Pipeline works in Databricks, why don't subscribe to this channel, share this video with your connection, like and comment for better reach Link to Files: https://drive.google.com/drive/folder... See you in the next video #Databricks hashtag#LakehouseArchitecture hashtag#Lakeflow hashtag#DeclarativePipeline hashtag#MedallionArchitecture hashtag#AutoLoader hashtag#SparkSQL hashtag#DataEngineering hashtag#ScalableSolutions hashtag#ModernETL hashtag#DatabricksMVP