Enterprise AI Lakehouse Platform
Executive Summary
A unified lakehouse architecture on Databricks combining data engineering, ML feature stores, and real-time analytics to power AI workloads processing 50TB+ daily across the organization.
Business Problem
Data was siloed across 30+ systems with no unified access layer. Data scientists spent 70% of their time on data preparation. ML features were duplicated across teams with inconsistent definitions. Real-time analytics were impossible due to batch-only pipelines with 24-hour latency.
Architecture Overview
Built on Databricks with a medallion architecture (bronze/silver/gold layers). Bronze ingests raw data from all sources. Silver applies cleaning, deduplication, and schema enforcement. Gold serves curated datasets, feature tables, and ML training sets. Unity Catalog provides governance across all layers.
Solution Design
Deployed on Azure Databricks with Delta Lake as the storage format. Structured Streaming enables real-time ingestion. Feature Store centralizes ML feature definitions with point-in-time correctness. MLflow manages experiment tracking and model registry. Power BI connects to Gold layer for executive dashboards.
Architecture Diagrams
Technology Stack
Security
Unity Catalog enforces column-level security and row-level access control. All data is encrypted at rest (AES-256) and in transit (TLS 1.3). PII is tokenized in the Silver layer with access governed by data classification tags.
Scalability
Auto-scaling Databricks clusters handle burst workloads up to 500 nodes. Delta Lake's transaction log enables concurrent reads and writes at scale. The platform processes 50TB+ daily with sub-minute freshness for streaming tables.
Performance Metrics
- Data freshness improved from 24 hours to under 5 minutes for streaming sources
- Data scientist productivity increased 60% (less time on data prep)
- 50TB+ daily processing with 99.9% pipeline reliability
- ML feature computation time reduced from hours to minutes
- Query performance improved 10x with optimized Gold layer
- 30+ source systems unified into single governed platform
Challenges
- Migrating 5 years of historical data from legacy systems without downtime
- Handling schema evolution across 200+ tables without breaking downstream consumers
- Implementing point-in-time feature correctness for ML training to prevent data leakage
- Balancing cost between always-on streaming clusters and batch processing
- Establishing data ownership and quality SLAs across 12 contributing teams
Lessons Learned
- Medallion architecture clarity is essential — strict contracts between layers prevents 'garbage in, garbage out'
- Feature Store adoption requires executive sponsorship — teams won't share features without incentives
- Unity Catalog governance should be implemented from day one, not retrofitted
- Streaming and batch should coexist — not everything needs real-time, optimize for cost
- Data quality checks at the Silver layer catch 95% of issues before they reach consumers
Business Outcomes
- $4.5M annual savings from decommissioning legacy ETL systems
- Time-to-insight reduced from weeks to hours for new analytics requests
- 500+ data scientists and analysts self-service accessing governed data
- ML model deployment time reduced from months to days
- Single source of truth established for all business metrics
- Enabled real-time fraud detection saving $8M annually
Questions
FAQ
Why Databricks over a custom Spark deployment?
Databricks provides managed infrastructure, Unity Catalog governance, built-in MLflow, and Delta Lake optimizations that would require significant engineering effort to replicate. The total cost of ownership was 40% lower than a self-managed alternative.
How do you handle late-arriving data?
Delta Lake's ACID transactions and time travel capabilities allow us to retroactively correct data. Late arrivals trigger downstream recomputation only for affected partitions, minimizing reprocessing costs.
What's the cost model for this platform?
We use a chargeback model where each business unit pays for their compute consumption. Shared infrastructure (governance, monitoring) is centrally funded. This incentivizes efficient query patterns and appropriate cluster sizing.
Future Roadmap
Expanding to support multi-cloud with Databricks on AWS for disaster recovery, adding automated data quality scoring with AI-driven anomaly detection, and implementing a data marketplace for monetizing curated datasets.