Data Warehouse - The Ultimate Guide [2025] | Master Data Modeling

Data warehouse | Data Modeling | Databricks | PySpark | SparkSQL In this video, I walk you through key Data Warehouse concepts, including ETL Pipelines, Incremental Data Loading, Dimensional Data Modeling, Star Schema vs. Snowflake Schema, and Slowly Changing Dimensions along with their practical implementation in Databricks using Spark SQL. Timestamps: 0:00 Introduction 07:15 What is Data Warehouse? 12:42 Database VS Data Warehouse 23:19 What is Data Warehousing 28:29 ETL Layers 34:04 Incremental Loading 46:10 Databricks Free Account 48:27 Databricks Overview 57:32 Incremental Data Loading using Spark SQL 1:19:02 What is Data Modeling 1:25:45 What is Dimensional Data Modeling 1:29:06 Fact Table and Dimension Tables 1:37:37 STAR Schema VS SNOWFLAKE Schema 1:46:23 Dimension Tables and Fact Table using Spark SQL 2:30:39 Types of Fact Tables 2:40:34 Types of Dimension Tables 2:49:47 Slowly Changing Dimensions 3:00:00 Implementing SCD Type 1 in Databricks with Spark SQL WATCH MY PREVIOUS VIDEOS ➡️Azure End To End Data Project https://youtu.be/6_hXeNg9TJ0?si=9naCovTmgcZn0NQQ ➡️Databricks Tutorial https://youtu.be/P5pEeR3xQpI?si=XbQYrYkrVp2jKjfE ➡️PySpark Full Course https://youtu.be/94w6hPk7nkM?si=Y2EUnifXrZRK3MOj ➡️PySpark Interview Ques (2025) https://youtu.be/fOCiis31Ng4?si=SNSxDEG1m_n8vH1p REPOSITORY ➡️GitHub https://github.com/anshlambagit/Data-Warehouse-Guide-2025 ➡️Book https://www.kimballgroup.com/data-warehouse-business-intelligence-resources/books/data-warehouse-dw-toolkit/ ➡️Report https://fortune.com/education/careers/data-engineer-salary/ ======================================= Join this channel to SUPPORT MY HARDWORK: https://www.youtube.com/channel/UCu7lQE-L5gzt8aD7zuuufjw/join ======================================= Connect with ME LinkedIn - https://www.linkedin.com/in/ansh-lamba-793681184/ Telegram - https://t.me/anshlambadatafam ========================= For COLLABORATION 👇 anshlambacollab@gmail.com ========================= ⭐Hashtags⭐ #database #dataengineering #datawarehouse #pyspark