Qbeast Secures €6.5M Funding In Seed Round

The round was led by Surge, Peak XV’s scale-up program (formerly Sequoia Capital India), with backing from HWK TechInvestment and Elaia Partners.

Qbeast is working to improve the efficiency of open lakehouse architectures—such as Delta Lake, Apache Iceberg, and Apache Hudi—which often consume excessive computing power. Databricks estimates that as much as 90% of compute resources can be wasted scanning irrelevant data.

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Lakehouse technology merges the strengths of data lakes and data warehouses into one platform. It lets organizations store vast amounts of raw data, like a data lake, while enabling structured queries and analytics, like a data warehouse. This unified system supports both big data processing and business intelligence without shifting data across 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.

Qbeast plans to use the funding to grow its team, expand analytics use case support, and boost performance and cost-efficiency in open data environments.

Key platform upgrades will include auto-tuning, adaptive indexing, and tighter integration with compute engines and cloud platforms. The goal: to become the go-to indexing layer for open Lakehouse systems—enabling smarter data operations without added complexity.

To drive this next phase, former AWS and Microsoft Azure executive Srikanth Satya has been appointed CEO, tasked with scaling Qbeast’s technology and operations.

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.”

Qbeast’s platform boosts data processing by connecting directly to Delta, Iceberg, and Hudi tables. Its multi-dimensional indexing enables fast filtering across fields like time, region, and customer segment—speeding up both real-time and historical queries within a single table.

Compatible with engines like Spark, Databricks, Snowflake, DuckDB, and Polars, Qbeast works without altering existing pipelines or adding new storage layers.

By tackling high compute costs and sluggish queries in open-format data lakes, Qbeast delivers 2–6x faster performance and up to 70% cost savings across sectors like 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 stems from research in distributed systems and multi-dimensional indexing by Cesare Cugnasco (now CSO) and Paola Pardo at the Barcelona Supercomputing Center. Their work laid the foundation for a platform that integrates seamlessly with open data formats and existing tools—avoiding vendor lock-in and pipeline overhauls.

“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.

About Qbeast

Qbeast enhances open lakehouse platforms by improving speed, efficiency, and ease of use. With multi-dimensional indexing and smart data layout, it tackles performance and cost issues in Delta Lake, Iceberg, and more—across engines like Spark and DuckDB. Born at Barcelona Supercomputing Center, Qbeast drives global impact from three continents.