Product descriptions:
Quantum Capital Group, a leading private capital firm with over $30 billion raised and invested across the global energy sector, relies on data to identify and value potential oil and gas investments. As the scale of their data grew to 1.5 billion records across multiple vendors and internal sources, maintaining consistency and accessibility across analyses became increasingly complex. By adopting Lakebase, Quantum extended their lakehouse with relational capabilities that improve structure, governance and usability. Together with Unity Catalog and Lakeflow, Lakebase integrates relational data management into Quantum’s existing lakehouse foundation, allowing deal teams to quickly access and share trusted data and conduct robust, thorough diligence across opportunities.
Building a consistent, trusted foundation for deal evaluation
For Quantum Capital Group, investment decisions depend on a clear understanding of subsurface assets, including where wells are drilled, their performance and how returns may evolve over time. Supporting this analysis requires integrating 1.5 billion records spanning six vendor feeds and decades of proprietary data.
As these datasets grew, differences in formats, naming conventions and reporting standards made it harder for teams to consistently work from the same information. Engineers, geologists and analysts often relied on different tools and datasets, which introduced additional coordination overhead and extended diligence timelines. “We rely on understanding the rock quality, what’s been drilled, what’s been produced historically, what’s producing today and what’s left to recover,” Ian Brown, Head of Digital Engineering at Quantum Capital Group, said. “To move faster and collaborate more effectively, we needed our data to be relational and governed in a way that our teams and existing systems were already familiar with.”
While Quantum’s lakehouse environment was already well curated and scalable, many downstream tools used by engineering and deal teams operate most effectively with relational data. Without a shared relational framework and consistent governance, teams sometimes applied different assumptions to similar datasets, extending evaluation cycles. Quantum enhanced their Databricks environment by integrating relational capabilities that meet the performance expectations of existing tools, support materialized and governed datasets, and enable teams to self-serve using familiar systems, while preserving the scalability of the lakehouse.
Lakebase delivers relational power to the lakehouse
Quantum adopted Lakebase, the operational database integrated into the lakehouse, to simplify data management while maintaining the openness and scale of their existing platform. Lakebase builds on Quantum’s lakehouse foundation by adding relational capabilities that align with how teams and downstream systems already work.
Each data feed now lands in its own schema within Lakebase and is standardized through common naming conventions, stored procedures and triggers. This structure ensures that engineering, analytics and business tools consistently query the same governed datasets. The Postgres interface allows teams to work in familiar SQL environments, while PostGIS integration supports advanced geospatial analysis, enabling engineers to visualize and query subsurface data directly in Databricks.
“Lakebase brings everything together,” Ian said. “It lets us apply relational principles at the same scale we already enjoyed in Databricks, and with tools our teams already know.”
Lakebase works alongside Unity Catalog and Lakeflow to provide lineage, metadata and row-level access controls while streamlining ingestion through change data capture (CDC). These enhancements reduced duplication and eliminated more than 100 redundant tables, simplifying the data landscape and improving governance across the organization.
Equally important, the relational structure made data more accessible across teams, from engineers building enrichment algorithms in notebooks to analysts modeling economic scenarios in Tableau or Spotfire. As Ian noted, “Lakebase made data human again.” It provides an approachable interface for nontechnical users while continuing to meet the performance and governance expectations of enterprise-scale data engineering.
Accelerating deal evaluation and improving investment performance
Lakebase has had a meaningful impact on how Quantum evaluates and manages investment opportunities. The firm now runs over 11,000 unique data queries per month, reflecting broad adoption across their 150-person organization. Data is no longer isolated or dependent on data engineering teams; business users can query, visualize and analyze directly from governed datasets.
Deal evaluation workflows that previously required extended manual reconciliation can now be completed more efficiently. Engineers and analysts merge geological, production and financial data in a single environment, allowing deal teams to assess opportunities more quickly and accurately. “It’s democratized our data in a big way,” Ian said. “Everyone can see and query the same tables, understand the structure and build insights directly.”
By unifying data and enhancing analytical consistency, Lakebase enables Quantum to evaluate more opportunities and identify those with the best risk-adjusted returns. Teams can spend more time on deeper analysis and evaluate increasingly complex deals with confidence, knowing their work is grounded in trusted, governed data.
“Lakebase has been the missing piece,” Ian concluded. “It gave us the clarity and organization to trust our data and the confidence to innovate on top of it.”