AZ-Data-Engineering-End-to-End

Modernizing On-Premises Infrastructure with Azure and Databricks

Azure Data Engineering Workflow Diagram
The Architecture: From Legacy to Cloud-Scale

A robust cloud-native platform that modernizes and consolidates on-premises SQL environments into Azure.

On-Premises Ingestion

Securely migrates legacy Microsoft SQL Server data using Azure Data Factory with high throughput and data integrity.

Modern Processing Power

Leverages Azure Databricks for massive-scale Spark transformations and Azure Synapse for enterprise data warehousing.

Medallion Lakehouse Architecture

Organizes raw data into a Bronze, Silver, and Gold Medallion pattern within Azure Data Lake Gen2.

Enterprise-Grade Insight Serving

Connects Power BI directly to the Gold layer for real-time executive dashboarding and KPIs.

Unified Governance & Security

Secured via Databricks Unity Catalog, Azure Active Directory, and Azure Key Vault for credentials.

Architectural Pattern: Layered Architecture (N-tier)

The system is built on a clean Layered Architecture (N-tier), ensuring separation of concerns:

  • Data Ingestion Layer: Moves on-prem data via Azure Data Factory.
  • Processing Layer: Handles large-scale transformations via Azure Databricks and Synapse.
  • Storage & Serving Layer: ADLS Gen2 Lakehouse and Power BI for intuitive reporting.
Functional Requirements
  • Data Migration: Extract data from on-premises SQL servers to Azure cloud securely.
  • Data Transformation: Clean and aggregate data into a Medallion architecture (Bronze, Silver, Gold).
  • Business Intelligence: Provide real-time dashboards and KPIs via Power BI.
  • Secret Management: Securely store connection credentials and tokens without hardcoding.
Non-Functional Requirements
  • Performance: speed and responsiveness via Azure Databricks Spark clusters.
  • Security: protection against unauthorized access enforced by Azure Active Directory and Key Vault.
  • Usability: ease of use provided by seamless Power BI dashboard integrations.
  • Reliability: system stability and availability maintained by managed Azure Data Factory pipelines.
  • Scalability: ability to handle growth via elastic Azure Synapse data warehousing.
  • Maintainability: ease of updates and fixes using centralized Unity Catalog governance.
  • Portability: ability to run in different environments due to decoupled storage (ADLS) and compute (Databricks).

Building the Future of Data-Driven Decisions.

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