🚀 Databricks Full Course | Build an End-to-End Data Engineering Project

🚀 Learn Databricks from fundamentals to advanced Data Engineering by building a complete End-to-End Data Engineering Project! Welcome to the Databricks Full Course — a complete practical guide to learning Databricks, PySpark, Data Engineering, Delta Lake, Databricks SQL, Unity Catalog, Auto Loader, Delta Live Tables (DLT), Structured Streaming, Jobs, Workflows and more through hands-on examples. 📂 DATASET & PROJECT FILES: 📌 GitHub: https://github.com/manish040596/DATAB... In this course, we don't just learn individual Databricks features — we bring everything together by building a complete End-to-End Data Engineering Project using the Medallion Architecture (Bronze, Silver & Gold). Whether you're a beginner learning Databricks, a Data Engineer preparing for interviews, or a professional looking to build real-world Data Engineering projects, this course takes you from fundamentals to practical implementation. 🚀 WHAT YOU WILL LEARN ✅ Databricks Fundamentals ✅ Databricks Lakehouse Architecture ✅ Databricks Free Edition ✅ Unity Catalog ✅ Managed & External Tables ✅ Databricks Volumes ✅ PySpark Fundamentals ✅ PySpark DataFrames ✅ Data Transformation & Filtering ✅ Joins, Union & Aggregations ✅ Window Functions ✅ Pivot & Unpivot ✅ Handling NULLs & Duplicates ✅ Date Functions ✅ Explode Function ✅ User Defined Functions (UDFs) ✅ repartition() vs coalesce() vs partitionBy() ✅ Spark SQL ✅ Databricks SQL ✅ SQL Parameters & Snippets ✅ Query History & Monitoring ✅ SQL Dashboards & Alerts ✅ Databricks Jobs & Workflows ✅ Job Scheduling & Triggers ✅ IF/ELSE & For-Each Workflows ✅ Job Monitoring & Notifications ✅ Structured Streaming ✅ Auto Loader ✅ Incremental Data Ingestion ✅ Schema Handling & Evolution ✅ COPY INTO ✅ Delta Live Tables (DLT) ✅ Lakeflow Declarative Pipelines ✅ Streaming Tables ✅ Materialized Views ✅ GitHub Integration ✅ Databricks Branches ✅ CI/CD Concepts ✅ Production Job Deployment 🏗️ END-TO-END MEDALLION ARCHITECTURE PROJECT The highlight of this course is a complete End-to-End Data Engineering Project using the Medallion Architecture. 🥉 BRONZE LAYER Ingest raw source data using Auto Loader and store it in Delta tables. 🥈 SILVER LAYER Clean, validate, transform and standardize data using PySpark and Spark SQL. 🥇 GOLD LAYER Create business-ready datasets and analytical tables for reporting and analytics. 🔄 COMPLETE DATA ENGINEERING PIPELINE Source Data ↓ Auto Loader / Ingestion ↓ Bronze Layer ↓ Silver Layer ↓ Gold Layer ↓ Databricks SQL / Analytics You will also see how Databricks Jobs, Workflows, monitoring and GitHub/CI-CD concepts fit into a complete Data Engineering solution. 🎯 WHO SHOULD WATCH THIS COURSE? 🔹 Aspiring Data Engineers 🔹 Data Engineers 🔹 ETL Developers 🔹 Cloud Data Engineers 🔹 Big Data Developers 🔹 PySpark Developers 🔹 Databricks Developers 🔹 Students learning Data Engineering 🔹 Professionals preparing for Data Engineering interviews 🔹 Anyone looking to build a real-world Data Engineering project 💡 WHY THIS COURSE? Instead of learning individual concepts in isolation, this course follows a simple: LEARN → PRACTICE → BUILD approach. You will start with Databricks fundamentals and gradually move toward advanced Data Engineering concepts before bringing everything together in a complete Medallion Architecture project. By the end of this course, you will have a clear understanding of how Databricks components work together to build modern Lakehouse and Data Engineering solutions. 📚 TOPICS COVERED Databricks | PySpark | Data Engineering | Delta Lake | Spark SQL | Databricks SQL | Unity Catalog | Volumes | Auto Loader | COPY INTO | Structured Streaming | Delta Live Tables | Lakeflow Declarative Pipelines | Jobs & Workflows | GitHub | CI/CD | Medallion Architecture 🚀 START LEARNING If you want to learn Databricks through practical Data Engineering, this course takes you from fundamentals to a complete End-to-End Data Engineering Project. 👍 Like the video if you find it useful. 💬 Comment below with the Databricks topic you want to learn next. 🔔 Subscribe for more practical Data Engineering tutorials covering Databricks, PySpark, Snowflake, Microsoft Fabric, AWS and Azure. 🔗 CONNECT & RESOURCES 📂 GitHub / Dataset: https://github.com/manish040596/DATAB...