Spanish data infrastructure startup Qbeast has raised €6.5 million in Seed funding to accelerate the future of open Lakehouse analytics while expanding the team and furthering product development.
The round was led by Peak XV’s Surge programme (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners. The capital injection will support Qbeast’s broader mission to simplify and optimise big data analytics across modern data stacks.
“Data teams shouldn’t have to choose between speed, cost, and openness,” said Srikanth Satya, CEO of Qbeast. “We built Qbeast to make high-performance analytics simple and accessible, without locking organisations into proprietary systems. In a world where data is growing faster than ever, we’re here to ensure every company can turn that data into value on their own terms.”
Founded in 2020 out of research conducted at the Barcelona Supercomputing Centre, Qbeast offers a plug-and-play data indexing platform that enhances performance on open Lakehouse formats such as Delta Lake, Apache Iceberg, and Apache Hudi.
The company’s origins trace back to academic work by Cesare Cugnasco, Qbeast’s CSO, and Paola Pardo, who together researched multi-dimensional indexing at the Barcelona Supercomputing Centre. This academic foundation forms the bedrock of Qbeast’s drop-in indexing layer – one that allegedly avoids the limitations of one-dimensional partitioning and instead supports compound filtering across multiple attributes.
At the core of its innovation is multi-dimensional indexing – a method that prioritises relevant data access based on columnar attributes like time, location, or customer segment, eliminating the need to scan entire datasets for complex queries. This approach offers sub-second performance without sacrificing the openness or flexibility that data teams demand.
The platform integrates with existing data infrastructure and compute engines including Spark, Databricks, Snowflake, DuckDB, and Polars, allowing engineering teams to accelerate workloads without modifying pipelines or shifting storage layers. In production, Qbeast claims its indexing technology has delivered query speedups of 2–6x and compute cost reductions of up to 70% across verticals like finance, healthcare, and retail.
The funding will also fuel international expansion under the leadership of newly appointed CEO Srikanth Satya, a cloud infrastructure expert with experience from AWS and Microsoft Azure.
“There is an undesirable compute cost hidden in the data layout that has been highly neglected by the market for data lakehouses,” shared Flavio Junqueira, CTO of Qbeast 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 and that is both engine and format neutral.“
The appeal of Qbeast’s offering lies in its balance of open standards and high-performance analytics. It aims to become the go-to indexing layer for organisations building scalable AI and analytics pipelines without increasing compute waste.
“We believe Qbeast is solving a fundamental challenge in the modern data stack. 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,” added Juan Santamaría, CEO and Managing Partner at HWK TechInvestment.
With plans to introduce features like auto-tuning, adaptive indexing, and broader compatibility with cloud engines, Qbeast is positioning itself as the performance backbone of the open data stack.
“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,” said Sébastien Lefebvre, Partner & DeepTech Investor at Elaia.
