Migrating big-data pipelines from on-premise to Azure Databricks

The challenge

• A legacy on-premise big-data platform constrained scalability and slowed pipeline development.
• High-volume machine and engineering data demanded elastic compute and incremental processing.
• Logic was duplicated across applications, with limited testing and governance.

Our approach

• Migrated the pipelines to a medallion (bronze / silver / gold) lakehouse on Azure Databricks with Unity Catalog governance.
• Built incremental ingestion with Structured Streaming and Change Data Feed, merging sources into master Delta tables.
• Applied a clean architecture — pure, fully-tested business-logic modules wired to readers and writers through a data-getter abstraction.
• Extracted reusable infrastructure into a versioned internal Spark library, published and shared across the team’s applications.
• Hardened delivery with Azure DevOps CI/CD, Databricks Asset Bundles for per-environment jobs, and strict quality gates (linting, typing, 90%+ test coverage).

Architecture

Outcomes

• A scalable, governed cloud lakehouse replacing the constrained legacy platform.
• Incremental, CDC-based master tables instead of costly full rebuilds.
• A reusable Spark library that standardizes pipelines across multiple applications.
• Multi-environment, automated deployments with high test and code-quality standards.