Qbeast provides a data optimization platform that plugs into Delta, Iceberg, and Hudi tables and accelerates analytics workloads by prioritizing the data users need. Its multi-dimensional indexing handles complex filters across columns like time, region, or customer segment and optimizes both real-time and historical queries in a single table. The platform integrates with compute engines such as Spark, Databricks, Snowflake, DuckDB, and Polars without requiring teams to rewrite pipelines or adopt a new storage layer. The company’s core technology is rooted in research conducted at the Barcelona Supercomputing Center by Cesare Cugnascom and Paola Pardo. Led by newly appointed CEO Srikanth Satya and CTO Flavio Junqueira, Qbeast plans to use the new capital for team expansion and to support the product across more analytics use cases. Qbeast raised $7.6M in seed funding to advance product development and growth. Qbeast is a Barcelona Supercomputing Center spinout that provides an open-source solution to align and standardize data lakes for operational machine learning. The platform aims to let teams store raw, live data for future ML workloads while reducing duplication between data lakes and warehouses. Qbeast claims its tooling yields a 68% improvement in timeframes and up to 50x faster sampling workloads. The company says its open-source license is advanced and is trying to build a developer community to establish a single, reliable format for data lake infrastructure. Early commercial traction includes a paid subscription from an unnamed cybersecurity company and an ongoing collaboration with Preply to support ML model training. Qbeast argues standardizing the stack will cut engineering friction, cloud costs and development time for customers. Qbeast commercializes a patented data-engineering technology developed at the Barcelona Supercomputing Center (BSC) and the UPC, offering a cloud platform that reorganizes data with multidimensional indexing and sampling to accelerate analysis. The platform can reduce computational needs and, according to the company, enable analyses up to 100× faster and make data teams as much as 3× more productive. Qbeast was founded in July 2020 as a BSC spin-off and holds exploitation rights via a technology-transfer agreement. Founders Cesare Cugnasco and Yolanda Becerra developed the core system while working at BSC and patented the approach. The company plans to use new capital to expand R&D, further develop the product, and reach initial commercial sales. Qbeast is actively hiring and intends to grow its engineering team from seven to twenty to support product development and go-to-market efforts.

Total raised

$11M

Funding rounds

3

Latest round

Seed

Latest activity

Aug 2025