Qbeast, a spinout from the Barcelona Supercomputing Centre, has raised €6.5 million (approx. $7.6M USD) in seed funding to scale its open-source data optimization platform for lakehouse architectures.
The round was led by Peak XV’s Surge (formerly Sequoia Capital India) with participation from HWK TechInvestment and Elaia Partners.
Solving the speed–scale tradeoff in enterprise data workloads
Qbeast is building a next-generation layer for lakehouse systems (a hybrid of data lakes and warehouses), optimized for AI and analytics at scale. Its technology solves key bottlenecks in data partitioning, indexing, and query acceleration, helping large teams run machine learning and analytics workloads faster — without massive infrastructure overhead.
Originally developed as a research project, Qbeast has evolved into a high-performance, open-source engine integrated with platforms like Apache Spark and Databricks.
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Backed by deep tech talent and a European R&D legacy
Led by CEO Srikanth Satya, Qbeast’s team combines deep domain expertise from both academic research and enterprise software. The company aims to commercialize a cloud-native version of its product while contributing to the open-source ecosystem and supporting adoption across data-intensive industries like finance, pharma, and manufacturing.
The fresh capital will support product development, go-to-market hiring, and community expansion across Europe and North America.
