As enterprises move AI from experimentation to production, traditional data architectures are increasingly challenged by the need for real-time operational data, analytics, AI, and persistent application context to work together. This white paper explores how Databricks Lakebase can help address this challenge by bringing operational workloads closer to the data, analytics, AI, and governance capabilities of the Lakehouse.
MathCo’s Lakebase LTAP Accelerator provides a practical path for turning this platform capability into a production-ready enterprise foundation. The white paper examines how MathCo combines reusable architecture patterns, implementation assets, and an eight-phase methodology to help enterprises address the key requirements of Lakebase adoption, including integration, scalability, performance, data freshness, governance, and operational readiness.
Inside the white paper:
- How AI is changing the requirements for enterprise data architectures
- Where fragmentation between operational and analytical systems creates complexity
- What Databricks Lakebase changes for enterprise applications and AI workloads
- How MathCo’s six reusable LTAP capabilities support production adoption
- The eight-phase methodology for moving from implementation to governed production
- How MathCo applied the approach to an Always-On Marketing Mix Modeling solution, with measurable improvements in refresh time, data-preparation effort, analysis speed, and insights adoption
Discover how MathCo helps enterprises operationalize Databricks Lakebase and build a scalable foundation for real-time applications and AI.
