Qbeast, a spin-off of the Barcelona Supercomputing Centre, created to solve the toughest trade-offs in lakehouse technology, has secured $7.6M (nearly €6.57M) in seed funding to enhance its data optimisation platform.
The funding round was led by Peak XV’s Surge (formerly Sequoia Capital India), with additional participation from HWK TechInvestment and Elaia Partners.
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Qbeast aims to address inefficiencies in open Lakehouse architectures, where formats like Delta Lake, Apache Iceberg, and Apache Hudi often lead to high compute resource usage. According to Databricks, up to 90 per cent of resources can be spent scanning irrelevant data.
Lakehouse technology combines elements of data lakes and data warehouses into a single system. It allows organisations to store large volumes of raw data (like a data lake) while also supporting structured querying and analytics (like a data warehouse). This approach enables unified data management for both big data processing and business intelligence without moving data between systems.
Juan Santamaría, CEO and Managing Partner at HWK TechInvestment, says, “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.”
“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,” adds Sébastien Lefebvre, Partner & Deep Tech Investor at Elaia.
Capital utilisation
Qbeast will use its new funding to expand its team, broaden support for analytics use cases, and improve performance and cost efficiency in open data environments.
The company plans to enhance its platform with features such as auto-tuning, adaptive indexing, and deeper integration with compute engines and cloud providers. Qbeast aims to become the standard indexing layer for open Lakehouse architectures, supporting data-driven operations without increasing operational burden.
To guide the company’s expansion, Srikanth Satya, with prior roles at AWS and Microsoft Azure, has been named CEO. His role will focus on scaling the company’s operations and technology footprint.
Srikanth Satya, co-founder and CEO of Qbeast, says, “Data teams shouldn’t have to choose between speed, cost, and openness. 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.”
Making open lakehouse platforms faster, efficient and easy to use
Qbeast offers a platform that connects to Delta, Iceberg, and Hudi tables to improve data processing efficiency. Its multi-dimensional indexing supports filtering across various columns such as time, region, and customer segment, allowing faster execution of real-time and historical queries in one table.
The platform works with compute engines like Spark, Databricks, Snowflake, DuckDB, and Polars without requiring pipeline changes or new storage layers.
Qbeast addresses rising compute costs and slow query performance in open-format data lakes by applying indexing that improves speed and lowers costs. In real-world use, the platform has shown performance gains of 2–6x and compute cost savings of up to 70 per cent across workloads in finance, healthcare, and retail.
Flavio Junqueira, CTO of Qbeast and co-creator of Apache ZooKeeper and Apache BookKeeper, mentions, “There is an undesirable compute cost hidden in the data layout that has been highly neglected by the market for data lakehouses. 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.”
Qbeast’s technology is based on research in distributed systems and multi-dimensional indexing by Cesare Cugnasco, now CSO, and Paola Pardo at the Barcelona Supercomputing Center. This work formed the basis of the platform, which integrates with open data formats and existing tools without requiring vendor lock-in or pipeline rewrites.
The platform is used across sectors, including finance, retail, and healthcare, supporting analytics, AI, and business intelligence workloads built on open formats.
“We believe every organisation, not just the tech elite, should be able to extract value from their data without incurring massive cloud costs or hiring a team of engineers to tune performance,” adds Satya.
