Data Engineering & Analytics Solution

📌 Project Overview

This project demonstrates a complete data engineering and analytics solution using Microsoft Azure. It integrates multiple cloud-based services to efficiently ingest, process, and analyze data, ensuring a seamless and scalable data pipeline. The final result is an interactive Power BI dashboard that delivers actionable business insights.

🔀 ADF Pipeline

This section illustrates the Azure Data Factory (ADF) pipeline used for data orchestration:

Figure: ADF Pipeline automating the data flow process.

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📊 Power BI Dashboard

Below is a preview of the Power BI dashboard built in this project:

Figure: Power BI Dashboard showcasing business insights.

Architecture Workflow

1️⃣ Data Ingestion - Azure Data Lake Storage

• Source data is stored in Azure Data Lake.

2️⃣ Data Orchestration - Azure Data Factory (ADF)

• ADF automates the end-to-end process.
• Metadata File Check:
  • If missing, an error email notification is triggered.
  • If present, metadata parameters are extracted.
• Data Copying:
  • Extracts files from the client’s Data Lake.
  • Stores them in a centralized storage Data Lake.

3️⃣ Data Processing - Azure Databricks & PySpark

• Securely connects to Azure Data Lake using Azure Key Vault & Databricks scopes.
• Reads raw data files and uploads them to the workspace.
• Processes data using PySpark Notebooks:
  • Cleans and resolves data inconsistencies.
  • Applies transformations and enrichment.
  • Creates Delta Tables for optimized storage and querying.

4️⃣ Data Visualization - Power BI

• Power BI connects to cleaned Delta Tables.
• Implements business logic using DAX (Data Analysis Expressions).
• Builds an interactive dashboard for insightful data exploration.

🛠️ Technologies & Tools Used

• Azure Data Lake Storage – Secure and scalable data storage.
• Azure Data Factory – Workflow automation and data orchestration.
• Azure Databricks (PySpark) – Large-scale data processing and transformation.
• Azure Key Vault – Secure credential management.
• Power BI – Data modeling and visualization.
• Delta Lake – Reliable and high-performance storage layer.

Key Features

✅ Automated Data Pipeline – ADF-driven workflow ensures seamless execution.
✅ Secure Data Access – Azure Key Vault protects sensitive credentials.
✅ Optimized Storage – Delta Lake enables fast and efficient queries.
✅ Advanced Data Processing – PySpark handles large-scale transformations.
✅ Interactive Dashboards – Power BI delivers real-time business insights.

Conclusion

This project showcases expertise in data engineering, cloud orchestration, and data analytics using Microsoft Azure, Databricks, and Power BI. It enables businesses to automate workflows, ensure data consistency, and derive meaningful insights for data-driven decision-making.