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:
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)
Data Storage & Organization
Data is stored in hierarchical, zone-based storage within ADLS Gen2:
Bronze: raw, ingested data
Silver: cleansed and validated data
Gold: curated and transformed data ready for analytics
Data Processing & Transformation
Azure Databricks processes data using Spark notebooks and Delta Lake for efficient transformations (Bronze → Silver → Gold)
Data Governance & Cataloging
Microsoft Purview is used to catalog datasets, track lineage, assign classifications, and ensure compliance policies are enforced
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
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