Qbeast Secures $7.6M in Funding to Scale Open Data Platforms

Data optimization platform Qbeast has received $7.6 million in seed funding to enhance its open data platforms, including Delta Lake and Apache Iceberg.

Funding will support expansion in team and product capabilities

Led by Peak XV’s Surge, with participation from HWK Tech Investment and Elaia Partners, the new injection of cash will fund team expansion, broaden product support across more analytics cases, and enable the company’s mission to make open data platforms faster, simpler, and more cost-efficient.

“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. 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.”

Satya added: “We believe every organization, 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.”

Why Qbeast leadership is committed to data-focused solutions

Qbeast’s platform integrates directly with Delta Lake, Apache Iceberg, and Apache Hudi, accelerating workloads by prioritizing the data required. It features multidimensional indexing that handles complex filters across columns, such as time, region, or customer segment, to optimize for both real-time and historical queries in a single table. These advancements are the result of research at the Barcelona Supercomputing Center.

According to the company, data lakes are massive but lack intelligence, leading to technical challenges. Qbeast addresses this through drop-in indexing to deliver sub-second performance and cost savings, without locking an organization into a new stack. Qbeast has already delivered query speedups of 2-6x and compute cost reductions of up to 70 percent for workloads in finance, healthcare, and retail. 

“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, 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.”

Modern problems require modern solutions

Cesare Cugnasco, CSO of Qbeast, and Paola Pardo have led the research at the Barcelona Supercomputing Center.

“We believe Qbeast is solving a fundamental challenge in the modern data stack,” said Juan Santamaría, CEO and Managing Partner at HWK TechInvestment. “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.”

The future for Qbeast includes plans to extend its platform with auto-tuning, adaptive indexing, and increased engine support across cloud providers and use cases. The company aims to be the default indexing layer for open lakehouse architectures.

“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 & Deep Tech Investor at Elaia.

DartPoints, a data center solutions provider, also recently received significant funding to scale their platform. Read more about their influx of cash and how DartPoints will use it to push data center solutions forward.