From Design to Deployment: Data Warehousing with Azure Synapse Analytics

The repo simulates query load, optimizes performance, and offers practical guidance for building data warehouses with Azure Synapse Analytics. 🚀

The project follows the following architecture,

Directories 📂

1. data: The datasets, fact, and dimension tables for the data warehouse
2. Queries_Part4.sql: SQL code for querying and analyzing the created data warehouse
3. setup.json: ARM (Azure Resource Manager) template. It is a block of code that defines the infrastructure and configuration for the project
4. setup.ps1: PowerShell script to provision the Azure Synapse Workspace along with the tables of the data warehouse (configured in setup.sql)
5. setup.sql: SQL script for creating the tables of the data warehouse
6. table_creation_codes.sql: SQL script to create the fact and dimension tables with dedicated SQL pool

Prerequisites:

1. An active Azure subscription
2. Knowledge of Azure Data Fundamentals, Good to begin from here

Getting Started 🚀

1. Clone the Repository:

   git clone https://github.com/tahhnik/Designing-Large-Scale-Data-Warehouse-with-Azure-Synapse-Analytics.git
   

To Set Up on the Azure Portal (Detailed walkthrough is in the blog posts)

Clone the repository inside Azure workspace through PowerShell on Azure Portal

git clone https://github.com/tahhnik/Designing-Large-Scale-Data-Warehouse-with-Azure-Synapse-Analytics.git

2. Explore the Directories: Navigate into each directory to find detailed automation scripts, SQL codes for queries, and configurations.

3. Follow the Blog: Implementation details and insights are documented in the associated series of blog posts in Medium.

   ⚡If you want to skip the initial processes like data modeling, and schema design and directly jump onto building the warehouse on Azure Synapse Analytics, follow the Blog Three,

   ⚡If you want to skip the building warehouse processes on Azure Synapse Analytics and directly jump onto querying the tables with SQL (T-SQL in this context), follow the Blog Four

Tools Explored 🛠️

1. Azure Synapse Analytics: Enterprise analytics service for data warehouses and big data systems.
2. Azure Portal: Unified console to manage Azure resources.
3. Azure Stream Analytics: Real-time stream processing engine.
4. Azure Machine Learning: Cloud service for ML project lifecycle.
5. Azure Data Lake Storage Gen2: Scalable storage for data lakes.
6. Power BI: Business analytics service for data visualization.
7. Azure Function Apps: Serverless applications for event-driven scenarios.
8. Azure Cosmos DB: Globally distributed, multi-model database.

Blog Implementation 📝

To implement this project, follow the step-by-step guide in our detailed blog post. Learn how each tool plays a crucial role in creating and scaling a data warehouse on Azure.

Blog One: From Design to Deployment: Data Warehousing with Azure Synapse Analytics (Part 1: Architecture)

Components:

• The architecture of the data warehouse
• The details of the data pipeline
• Brief Discussions of the tools and processes used

Blog Two: From Design to Deployment: Data Warehousing with Azure Synapse Analytics (Part 2: Data Modeling and Schema Design)

Components:

• Exploring the attributes of each logical entity in the context of retail companies

• The details of the schema and developing the snowflake schema

• 

• Brief Discussions of the data model

Blog Three: From Design to Deployment: Data Warehousing with Azure Synapse Analytics (Part 3: Design and Creation)

Components:

• Provisioning the Azure Synapse Analytics Workspace with UI and ARM templates

• Provisioning dedicated SQL pool within Azure Synapse Analytics Workspace

• Creating the SQL database and tables (facts and dimensions)

• Loading data into the tables

• 

Blog Four: From Design to Deployment: Data Warehousing with Azure Synapse Analytics (Part 3: Querying the Data Warehouse) (is being written at this moment)

Components:

• Querying the data warehouse
• Showcasing the analytical capabilities of Azure Synapse Analytics

Acknowledgments 🙌

My humble gratitude to my friends and family who are the constant support of my works and endeavors

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