SQL Data Warehouse from Scratch | Full Hands-On Data Engineering Project

Introduction

This tutorial provides a comprehensive guide to building an SQL Data Warehouse from scratch, based on the video by Data with Baraa. You will learn about data warehousing concepts, ETL processes, and practical steps for setting up a data warehouse. This guide is suitable for beginners and intermediate users interested in data engineering.

Step 1: Understand Data Warehouse Concepts

• Learn the basics of a data warehouse:
  • A centralized repository for structured and semi-structured data.
  • Supports business intelligence and analytics processes.
• Familiarize yourself with key terms:
  • ETL (Extract, Transform, Load): The process of moving data from source systems to the data warehouse.
  • Star Schema: A data modeling technique that organizes data into facts and dimensions.

Step 2: Prepare Project Materials

• Gather necessary materials and tools:
  • SQL Server Management Studio (SSMS)
  • Git for version control
  • Notion or similar tools for project planning
• Access free materials from the channel's website for additional resources.

Step 3: Plan the Project

• Use Notion to outline your project plan:
  • Define project goals and objectives.
  • Create a timeline for each phase of development.

Step 4: Design the Data Architecture

• Determine the architecture of your data warehouse:
  • Identify the layers: Bronze, Silver, and Gold.
  • Choose the right approach (e.g., cloud vs. on-premise).

Step 5: Initialize the Project

• Set up your project environment:
  • Create a Git repository for version control.
  • Define naming conventions for your database objects.

Step 6: Create Database and Schemas

• In SQL Server Management Studio, execute the following commands to create your database and schemas:

CREATE DATABASE YourDatabaseName;
GO
USE YourDatabaseName;
CREATE SCHEMA Bronze;
CREATE SCHEMA Silver; 
CREATE SCHEMA Gold;

Step 7: Build the Bronze Layer

• Analyze source systems to understand the data structure.
• Create Data Definition Language (DDL) for the tables to be used.
• Develop SQL load scripts for data ingestion.
• Create stored procedures to automate data loading processes.
• Document the data flow for clarity and future reference.

Step 8: Build the Silver Layer

• Explore and understand the data in the Bronze layer.
• Clean and load the necessary datasets (e.g., customer information, product information, sales details).
• Use SQL commands to perform data cleaning and loading:

INSERT INTO Silver.CleanedCustomerData
SELECT * FROM Bronze.RawCustomerData
WHERE ConditionsForCleaning;

• Create a stored procedure for loading cleaned data.

Step 9: Build the Gold Layer

• Understand data modeling principles:
  • Differentiate between dimensions and facts.
  • Choose between star and snowflake schemas based on your requirements.
• Create dimension tables (e.g., Customers, Products) and fact tables (e.g., Sales).
• Build the Star Schema model to organize your data effectively.

Step 10: Document Data Flow and Complete the Project

• Create a data catalog to maintain a record of all data assets.
• Finalize your documentation to ensure clarity and completeness.
• Review and test your data warehouse to confirm that it meets the project requirements.

Conclusion

In this tutorial, you learned how to build an SQL Data Warehouse from scratch, covering essential concepts, project planning, and practical implementation steps. As a next step, consider exploring advanced topics in data warehousing or diving deeper into ETL tools and techniques to enhance your data engineering skills.