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Welcome back to DataToCrunch. 📌 In this project, I have utilized Databrick platform to create Automated Lakeflow Declarative Pipeline(DLT). In the initial part, I have covered the necessary theory to create this pipeline & workflow followed by the practical part. Topics covered : 📂 Landing Layer – Auto Loader ingestion of customers & accounts data 🥉 Bronze Layer – Data cleaning with expectations & data quality checks 🥈 Silver Layer – Transformations, Slowly Changing Dimensions (SCD1 & SCD2) 🥇 Gold Layer – Business aggregations & materialized views for analytics 📊 BI & Reporting – Building dashboards powered by the Gold Layer ✅ Key concepts: backfilling, append flow, auto CDC, expectations, lineage, and governance ⚙️ How DLT + Workflows automate orchestration, improve reliability, and simplify operations This project demonstrates how Databricks LakeFlow (DLT) makes data engineering pipelines declarative, scalable, and production-ready, while powering dashboards for real-time insights. Dataset Available on https://github.com/RutujaKadam95/Auto... 🔖 TimeStamp: Theory - 0:00:00 - Intro 0:00:29 - Difficulties Before Lakeflow Declarative Pipelines 0:02:52 - How problems were solved with lakeflow declarative pipeline 0:06:13 - Databricks Lakeflow Overview 0:11:03 - Jobs & Pipeline Databricks Platform Overview 0:15:59 - Lakeflow Connect - Data Ingestion 0:16:59 - Batch Processing & Streaming Processing 0:18:30 - Data Ingestion Ways( Create Table, Copy Into, Autoloader ) 0:23:47 - Lakeflow Declarative Pipeline/ Delta Live Tables 0:26:18 - Datasets - Streaming Table, Materialized View, Temporary View 0:39:03 - Expectations 0:52:34 - Append Flow, Auto CDC, Apply Changes, Backfilling 1:09:31 - DLT - Databricks Platform Overview 1:25:49 - Workflows 1:32:09 - Workflows Databricks Platform Overview 1:48:41 - Project Overview 1:52:00 - Data Understanding 1:56:08 - Part part begins 1:59:36 - Data Ingestion 2:01:58 - DLT creation 2:07:15 - Bronze Work 3:03:59 - Silver Work 3:37:32 - Gold Work 3:47:51 - Dashboards 3:53:19 - Workflows 4:12:56 - Summary 💡 Don’t forget to like, share, and subscribe to DataToCrunch for more hands-on data engineering projects! #azure #databricks #databrickstutorial #etl #dataanalysis #dataanalysis #dataengineer #dataengineering #dataengineeringessentials