Qbeast, a Barcelona Supercomputing Center spinout focused on optimizing open data lake platforms, has raised $7.6 million in seed funding. The round was led by Peak XV’s Surge program (formerly Sequoia Capital India), with additional backing from HWK Tech Investment and Elaia Partners.
The fresh capital will support team expansion, extend the platform's analytics capabilities, and accelerate its mission to simplify and speed up open data architectures—without driving up infrastructure costs.
Modern data lake formats like Delta Lake, Apache Iceberg, and Apache Hudi have become standards for scalable analytics. But they come with an often-overlooked cost: excessive compute usage. According to Databricks, up to 90% of compute resources are wasted scanning irrelevant data.
Qbeast addresses this inefficiency with a drop-in indexing layer that integrates directly with these open formats. “Data teams shouldn't have to choose between speed, cost, and openness,” said Srikanth Satya, Qbeast’s recently appointed CEO. “We built Qbeast to make high-performance analytics simple and accessible, without locking organizations into proprietary systems.”
The platform already supports integrations with Spark, Databricks, Snowflake, DuckDB, and Polars, delivering 2–6x query speedups and up to 70% compute cost savings in production environments across sectors like finance, healthcare, and retail.
Instead of traditional partitioning or single-dimension sort orders, Qbeast offers multi-dimensional indexing that allows simultaneous filtering by any combination of attributes—like time, geography, or customer type. The technology makes it easier to run both real-time and historical queries from the same dataset.
“There is an undesirable compute cost hidden in the data layout that has been highly neglected by the market for data lakehouses,” said Flavio Junqueira, Qbeast CTO and co-creator of Apache ZooKeeper and Apache BookKeeper. “Our technology enables customers across verticals to reduce or even eliminate such costs in a manner that embraces the openness of the data lakehouse stack.”
With the new funding, Qbeast plans to broaden support for more analytics engines, add auto-tuning capabilities, and introduce adaptive indexing features. The long-term goal is to become the go-to indexing layer for open lakehouse architectures—allowing companies to scale analytics workloads without rewriting their pipelines or investing in costly proprietary platforms.
Qbeast also intends to grow its engineering team and expand its presence across global markets, focusing on enterprise clients looking for performance and cost improvements within their existing data stacks.
Qbeast was founded based on research at the Barcelona Supercomputing Center by Cesare Cugnasco and Paola Pardo. Their work on multi-dimensional indexing laid the foundation for the current platform, which now serves customers in sectors ranging from finance to healthcare.
CEO Srikanth Satya brings decades of cloud infrastructure experience from roles at AWS and Microsoft Azure. His appointment marks a new phase of scale for the company, leveraging his expertise in global cloud operations.
The leadership team also includes CTO Flavio Junqueira and CSO Cesare Cugnasco, both of whom have deep roots in distributed systems and open-source development.
In addition to Peak XV’s Surge, the seed round includes HWK Tech Investment and Elaia Partners. Both firms see Qbeast as a critical building block in the evolving data stack.
“We believe Qbeast is solving a fundamental challenge in the modern data stack,” said Juan Santamaría, CEO and Managing Partner at HWK Tech Investment. “In a context of data volume explosion, their multi-dimensional indexing layer has the potential to become critical for every company moving to a lakehouse model.”
“By empowering enterprises to unlock more value from their data with less complexity and expense, Qbeast aims to become the cornerstone indexing layer for modern data stacks,” added Sébastien Lefebvre, Partner at Elaia.
