UC- Secure, Scalable, and Compliant Data Architecture Solution

Description:

This use case outlines the implementation of a modern, secure, and scalable data architecture using Azure-native services to support end-to-end data operations — from ingestion and storage to transformation, governance, and reporting. The solution organizes data in layered formats (Bronze, Silver, Gold) and ensures regulatory compliance, access control, and BI enablement.

Actors:

• Data Engineers – design and build data pipelines, transformations

• Data Architects – define the architecture and governance frameworks

• BI Analysts / Power Users – consume curated data for insights and reporting

• Data Stewards – manage data cataloging, quality, and compliance

• Security Admins – enforce data access policies and auditing

• Azure Platform Team – maintain infrastructure, monitor services

Preconditions:

• Azure subscription and access to required services

• Initial connectivity with on-premise/cloud data sources

• Data classification, compliance, and security requirements defined

• User roles and responsibilities established

• Governance framework scoped (e.g., lineage, catalog, DQ rules)

Flow of Events:

1. Data Ingestion

   • Azure Data Factory (ADF) ingests data from multiple sources (databases, APIs, flat files, etc.) into Azure Data Lake Storage Gen2 (ADLS Gen2) Bronze layer (raw zone)

2. Data Storage & Organization

   • Data is stored in hierarchical, zone-based storage within ADLS Gen2:

     1. Bronze: raw, ingested data

     2. Silver: cleansed and validated data

     3. Gold: curated and transformed data ready for analytics

3. Data Processing & Transformation

   • Azure Databricks processes data using Spark notebooks and Delta Lake for efficient transformations (Bronze → Silver → Gold)

4. Data Governance & Cataloging

   • Microsoft Purview is used to catalog datasets, track lineage, assign classifications, and ensure compliance policies are enforced

5. Analytics & Reporting Access

   • Transformed Gold layer data is loaded into Synapse Analytics

   • Power BI and other tools connect to Synapse for interactive dashboards and reporting

6. Security & Monitoring

   • Role-based access control (RBAC) and data masking implemented

   • Audit and monitoring logs configured via Azure Monitor and Purview scans

Postconditions

• Data is ingested, transformed, and governed with traceability

• Gold data is available for secure BI/SQL access

• Data lineage, catalog, and access controls are in place

• Auditable compliance with internal and external policies achieved

Benefits

• Scalability: Supports large-scale data workloads across various domains

• Security: Role-based access and governance reduce data breach risks

• Compliance: Tracks lineage and classification for regulatory needs (e.g., GDPR)

• Reusability: Layered data approach enables reuse across business units

• Operational Efficiency: Automation through ADF and Databricks reduces manual work

• Improved Decision Making: Timely access to curated data enhances analytics outcomes

Tools & Technology Used

• Azure Data Factory (ADF) – Data orchestration and pipeline management

• Azure Data Lake Storage Gen2 (ADLS Gen2) – Centralized data lake with hierarchical namespace

• Azure Databricks – Distributed data processing and transformation

• Azure Synapse Analytics – SQL-based querying, data warehousing, BI interface

• Microsoft Purview – Data cataloging, lineage tracking, and compliance governance