Azure Data Lake Architecture

Big Data

Azure Data Lake Architecture

Role: Data Engineer | Stack: ADLS, Databricks, Azure Data Factory, Power BI

Rebuilt a fragmented reporting stack into a governed lakehouse with clear medallion layers. The goal was to reduce data incidents, improve dashboard freshness, and make onboarding of new data domains predictable.

Scope

Integrated 9 source systems (CRM, ERP, billing, and web events) into ADLS through Azure Data Factory. Standardized 120+ raw tables into curated Silver models and 30 domain-focused Gold marts for BI consumption.

Architecture Snapshot

Azure lakehouse architecture with Bronze Silver Gold layers and governance controls. — Medallion flow with quality checks, governance policies, and business marts.

Impact Metrics

-42% Pipeline Failures

-31% Compute Cost

+2.4x Data Onboarding Speed

Execution Notes

Added schema drift detection, data contracts for critical entities, and row-level audit columns across all curated layers. Partitioning and compaction strategy in Databricks reduced scan volume while keeping SLA consistency for daily reporting.