Enterprise Lakehouse & Data Architecture

Operational data from many systems had to be onboarded quickly and consistently, governed centrally, and trusted by downstream consumers — without every new source becoming a bespoke pipeline.

1. 01Bronze, Silver, and Gold medallion layers on Delta Lake separate raw, conformed, and business-ready data.
2. 02Unity Catalog provides central governance, access control, and lineage.
3. 03A metadata-driven framework handles onboarding, orchestration, audit, and operational patterns for new sources.
4. 04An enterprise data quality framework runs automated, metadata-defined validation and exception handling.
5. 05A reusable API integration framework securely ingests REST endpoints from operational systems.
6. 06Azure DevOps CI/CD promotes code and configuration across environments.

Configuration over code for onboarding

New sources are described in metadata and run through shared, tested pipelines instead of hand-written jobs.

Quality rules as data

Validation rules are stored as metadata so they can scale to thousands of business rules and be reviewed without code changes.

Governance from day one

Unity Catalog permissions, lineage, and auditing are part of the platform foundation rather than added later.

DevOps for data

Standardized CI/CD, environment promotion, monitoring, and recovery procedures make the platform reliable to operate.

• Azure
• Azure Databricks
• Delta Lake
• Unity Catalog
• Auto Loader
• Lakeflow
• Azure Data Factory
• Azure DevOps

• Consistent, repeatable onboarding across diverse data sources.
• Automated validation and exception controls across thousands of business rules.
• Standardized CI/CD, access control, monitoring, and recovery across environments.

• The second pipeline is where frameworks pay off — design for the hundredth source early.
• Data quality is a product feature; make failures visible to the people who own the data.
• Bringing DevOps discipline to Databricks is as much about process as it is about tooling.