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Barcelona-based data infrastructure startup, Qbeast, has raised $7.6 million in seed funding to accelerate development of its intelligent indexing platform designed to reduce compute waste and enhance performance in open data ecosystems.
The round was led by Peak XV’s Surge, with participation from HWK Tech Investment and Elaia Partners. The capital injection will support team expansion, product development, and market reach as Qbeast scales its operations.
Rooted in research from the Barcelona Supercomputing Center, Qbeast addresses a critical bottleneck in modern data platforms: the high cost of scanning massive datasets in open formats such as Delta Lake, Apache Iceberg, and Apache Hudi. According to industry estimates, up to 90% of compute resources are wasted on irrelevant data processing in traditional architectures.
Qbeast’s innovation lies in its multi-dimensional indexing engine, which allows analytical queries to target only the necessary data—dramatically reducing latency and compute spend. The technology supports filtering across multiple dimensions (e.g., time, geography, customer segment) in real time or historical contexts, delivering 2–6x query speedupsand up to 70% reductions in compute costs in production environments across sectors such as finance, healthcare, and retail.
“There’s a hidden cost in data layout that has long been ignored in data lakehouses,” noted Flavio Junqueira, Qbeast’s CTO and co-creator of Apache ZooKeeper and Apache BookKeeper. “Our approach unlocks efficiency without compromising the openness or flexibility of the modern data stack.”
The platform integrates natively with existing tools—including Spark, Databricks, Snowflake, DuckDB, and Polars—without requiring teams to rewrite pipelines or migrate data. It’s both engine-agnostic and format-neutral, enabling rapid deployment across diverse analytics environments.
To guide its next growth phase, Qbeast has appointed Srikanth Satya as CEO. A seasoned cloud infrastructure leader with previous leadership roles at AWS and Microsoft Azure, Satya brings deep technical and strategic expertise to the role.
“We believe every organisation should be able to harness the power of open data without excessive cost or complexity,” Satya said. “Qbeast was built to simplify analytics and unlock performance without locking teams into proprietary solutions.”
With a growing team of distributed systems experts and strong investor backing, Qbeast aims to establish itself as the default indexing layer for open Lakehouse architectures. Future roadmap priorities include auto-tuning capabilities, adaptive indexing, and deeper integration across cloud providers.
“Qbeast is solving one of the most pressing infrastructure challenges in today’s data-driven economy,” added Juan Santamaría, CEO of HWK TechInvestment. “As data volumes explode, their indexing solution becomes essential for scalable and efficient analytics.”
“Their approach to multi-dimensional indexing aligns perfectly with the vision of open, high-performance data architectures,” said Sébastien Lefebvre, Partner at Elaia. “Qbeast has the potential to become a foundational layer in the modern data stack.”
As demand for real-time analytics and AI-driven workloads intensifies, Qbeast’s drop-in solution offers a compelling value proposition for organisations looking to cut cloud costs, boost data agility, and stay competitive in an increasingly data-saturated world.
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