Why it matters: As data volumes surge, efficient data handling becomes crucial. Qbeast's solution minimizes wasted compute resources, making analytics more cost-effective. The platform's unique multi-dimensional indexing improves query performance across diverse data attributes.
What's next: Qbeast plans to introduce auto-tuning and adaptive indexing, aiming to be the default indexing layer for open Lakehouse architectures. The platform promises significant speed and cost improvements for industries like finance and healthcare.
Bellevue, WA - August 4, 2025: As enterprise data volumes explode and AI pipelines strain modern infrastructure, the open Lakehouse architecture has emerged as the new standard for analytics at scale. But while formats like Delta Lake, Apache Iceberg, and Apache Hudi are powerful, they come with a hidden tax: up to 90% of compute resources are wasted scanning irrelevant data, according to Databricks. Today Qbeast, the next-generation data optimization platform, announces a $7.6 million seed round to fix that.
The seed round was led by Peak XV's Surge (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners. The new capital will fund team expansion, broaden product support across more analytics use cases, and double down on the company's mission to make open data platforms faster, simpler, and more cost-efficient.
Born out of research at the Barcelona Supercomputing Center, Qbeast's platform plugs directly into existing Delta, Iceberg, and Hudi tables and accelerates workloads by prioritizing just the data you need. Its multi-dimensional indexing can handle complex filters across columns like time, region, or customer segment – optimizing for both real-time and historical queries in a single table. Unlike traditional partitioning or sort orders that work in single dimensions, Qbeast enables simultaneous filtering across any combination of data attributes. And it integrates with popular compute engines like Spark, Databricks, Snowflake, DuckDB, and Polars without requiring teams to rewrite pipelines or adopt a new storage layer.
To lead the next chapter of the company’s growth, Srikanth Satya, a cloud infrastructure veteran with decades of experience at AWS and Microsoft Azure, has been appointed as Qbeast's CEO. His deep technical expertise in cloud-native architecture and strategic leadership will steer Qbeast through its next phase of global expansion.
"Data teams shouldn't have to choose between speed, cost, and openness," said Satya. "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."
Today's data lakes are massive, but not smart - and this is where the technical challenge lies. Everyone's storing their data in open formats, but compute costs are exploding and most queries are painfully slow. Qbeast solves this with drop-in indexing that delivers sub-second performance and cost savings, without locking you into a new stack. In production environments, Qbeast has already delivered query speedups of 2–6x and compute cost reductions of up to 70% 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," 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 team behind Qbeast includes heavy hitters in distributed systems and open data. The company's core technology is rooted in research conducted by Cesare Cugnasco, CSO of Qbeast and Paola Pardo during their time at the Barcelona Supercomputing Centre, where breakthrough work in multi-dimensional indexing laid the foundation for today's platform. Unlike closed platforms that require vendor lock-in or significant rewrites, Qbeast plays natively with the tools data teams already use, serving organizations across finance, retail, healthcare and beyond — any team using open formats to power analytics, AI, or business intelligence at scale.
"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," added Satya.
“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”, said Juan Santamaría, CEO and Managing Partner at HWK TechInvestment.
“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.
Looking ahead, Qbeast plans to extend its platform with auto-tuning, adaptive indexing, and deeper engine support across cloud providers and use cases. The goal: to become the default indexing layer for open Lakehouse architectures and unlock a future where data-driven innovation doesn't come at the cost of performance, scalability, or sanity.
