Azure Data Engineering Pipeline: Real-Time Project

This project demonstrates an end-to-end data engineering pipeline on Azure, designed to address a hypothetical business scenario and deepen my understanding of data workflows.

Project Summary

In this project, I built a comprehensive data pipeline using Azure services to address a business need for insights on customer demographics and sales trends. Data from an on-premises SQL database is ingested, transformed, and visualized in a Power BI dashboard. This dashboard provides key metrics on gender distribution and product category performance, allowing for data exploration by date, product category, and gender.

Business Requirements

The business identified a need for improved insights into customer demographics—specifically, gender distribution and its impact on purchasing behavior. Key objectives include:

• Sales by Gender and Product Category: Dashboard metrics for total sales, revenue, and customer demographics.
• Data Filtering: Flexible filtering options by product category, gender, and date.
• User-Friendly Interface: An intuitive dashboard for stakeholders to interact with key metrics.

Solution Outline

To meet these needs, the project includes the following phases:

Data Ingestion

1. Extract customer and sales data from an on-premises SQL Server.
2. Load the data into Azure Data Lake Storage (ADLS) using Azure Data Factory (ADF).

Data Transformation

1. Clean and process data using Azure Databricks, creating structured layers: Bronze (raw data), Silver (cleansed), and Gold (aggregated insights).

Data Storage and Visualization

1. Load the transformed data into Azure Synapse Analytics.
2. Use Power BI to create interactive dashboards, enabling business users to view sales and demographic insights.

Automation

1. Automate the pipeline to refresh daily, ensuring up-to-date insights.

Technology Stack

• Azure Data Factory: Orchestration of data movement and transformation.
• Azure Data Lake Storage: Storage for raw and processed data.
• Azure Databricks: Data transformation and processing.
• Azure Synapse Analytics: Data warehousing and SQL-based analytics.
• Power BI: Data visualization and reporting.
• Azure Key Vault: Secure management of credentials.
• SQL Server: On-premises database as the data source.

Steps to Set Up

Prerequisites

• Azure account with sufficient credits
• Access to an on-premises SQL database

Step 1: Azure Environment Setup

• Create a resource group and provision necessary Azure services (Data Factory, Data Lake Storage, Databricks, Synapse Analytics, Key Vault).

Step 2: Data Ingestion

• Set up SQL Server and restore the AdventureWorks database.
• Use ADF to copy data from SQL Server to the Bronze layer in ADLS.

Step 3: Data Transformation

• Configure Databricks to access ADLS.
• Perform data cleaning and aggregation in Databricks, moving data from Bronze to Silver to Gold layers.

Step 4: Data Storage and Reporting

• Load aggregated data into Synapse Analytics.
• Create Power BI dashboards to visualize business metrics.

Step 5: Automation and Monitoring

• Schedule the pipeline to run daily in ADF.
• Use monitoring tools to track the success of pipeline runs.

Step 6: Security and Access Control

• Manage permissions through Azure Entra ID (formerly Azure Active Directory).

Step 7: Testing

• Insert new data in SQL to verify the end-to-end pipeline flow and ensure updated reports in Power BI.

Conclusion

This project provides a structured pipeline for analyzing customer demographics and their impact on sales performance. With automated data flows, stakeholders have access to real-time, actionable insights.