Ian Brown

Quantum Capital Group screens hundreds of energy investment opportunities each year, supported by more than 1.5 billion records across public, premium and proprietary datasets. While its Databricks Lakehouse already provided a scalable and governed foundation, the next challenge was enabling teams and downstream tools to work more efficiently from the same trusted data as deal volume and complexity increased. In this session, Quantum shares how it extended its Lakehouse with Databricks Lakebase to introduce relational structure and familiarity without sacrificing openness or scale. Attendees will learn how Lakebase integrates with Delta Lake, Unity Catalog and Lakeflow to support governed, materialized datasets that teams can access using SQL, geospatial tools and existing analytics platforms. The talk focuses on how relational access and governance helped Quantum scale human-led diligence across engineering and investment teams, evaluate complex deals with greater confidence.

Quantum Capital Group (QCG) screens hundreds of deals across the global Sustainable Energy Ecosystem, requiring deep technical due diligence. With over 1.5 billion records sourced from public, premium and proprietary datasets, their challenge was how to efficiently curate, analyze and share this data to drive smarter investment decisions. QCG partnered with Databricks & Tiger Analytics to modernize its data landscape. Using Delta tables, Spark SQL, and Unity Catalog, the team built a golden dataset that powers proprietary evaluation models and automates complex workflows. Data is now seamlessly curated, enriched and distributed — both internally and to external stakeholders — in a secure, governed and scalable way. This session explores how QCG’s investment in data intelligence has turned an overwhelming volume of information into a competitive advantage, transforming deal evaluation into a faster, more strategic process.