Qbeast, a next-generation data optimization platform, has raised $7.6 million in seed funding to tackle inefficiencies in the open Lakehouse ecosystem. The round was led by Peak XV’s Surge (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners. The fresh capital will support team growth, expansion into new analytics use cases, and acceleration of Qbeast’s mission to make open data platforms faster, simpler, and more cost-efficient.
As enterprise data volumes skyrocket and AI pipelines strain infrastructure, the open Lakehouse architecture — built on formats like Delta Lake, Apache Iceberg, and Apache Hudi — has become the analytics standard. However, up to 90% of compute resources are wasted scanning irrelevant data, according to Databricks. Qbeast addresses this challenge with intelligent multi-dimensional indexing that dramatically reduces compute usage while accelerating queries.
Developed out of the Barcelona Supercomputing Center, Qbeast integrates directly into Delta, Iceberg, and Hudi tables to prioritize only the data required for a query. The platform supports filtering across any combination of data attributes — such as time, geography, or customer type — enabling significant performance improvements for both real-time and historical workloads. Unlike traditional partitioning, Qbeast optimizes across multiple dimensions simultaneously. Compatibility with engines like Spark, Databricks, Snowflake, DuckDB, and Polars allows seamless integration without code rewrites or infrastructure changes.
To drive the next phase of growth, cloud infrastructure veteran Srikanth Satya has been appointed CEO. With decades of experience at AWS and Microsoft Azure, Satya brings deep technical and strategic expertise to the leadership team.
"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."
While data lakes offer massive storage, most are inefficient and costly to operate. Qbeast introduces a drop-in indexing layer that delivers sub-second performance and cuts costs significantly — without requiring migration to new architectures. In production, Qbeast has demonstrated 2–6x faster queries and up to 70% reductions in compute costs across finance, healthcare, and retail workloads.
"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."
Qbeast’s founding team includes experts in distributed systems and open data research. The core technology was created by Cesare Cugnasco, CSO, and Paola Pardo during their time at the Barcelona Supercomputing Center, where they pioneered multi-dimensional indexing for scalable analytics. Unlike closed platforms that require vendor lock-in, Qbeast integrates natively with the tools already in use across sectors including finance, healthcare, and retail.