Qbeast Lands $7.6M to Speed Up Lakehouse Data Indexing

Qbeast has raised $7.6 million in seed financing to expand its open, multi-dimensional indexing layer for Delta Lake, Apache Iceberg and Apache Hudi. The round was led by Peak XV’s Surge (formerly Sequoia Capital India) with participation from HWK Tech Investment and Elaia Partners.

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Originating from research at the Barcelona Supercomputing Center, Qbeast accelerates analytics by creating indexes that allow query engines to read only the data needed for each request. The software plugs directly into existing tables and works with Apache Spark, Databricks, Snowflake, DuckDB and Polars without storage-format changes or pipeline rewrites. In production tests the company reports 2- to 6-fold query-time reductions and up to 70 percent lower compute costs.

As part of the funding announcement, Qbeast named former AWS and Microsoft Azure executive Srikanth Satya as chief executive officer. The new capital will be used to hire additional engineering staff, widen support for more analytics workloads, and add features such as auto-tuning and adaptive indexing.

CTO Flavio Junqueira, a co-creator of Apache ZooKeeper and Apache BookKeeper, said the technology addresses the compute overheads that arise from current data-lakehouse layouts while remaining engine- and format-neutral.

Investors HWK Tech Investment and Elaia Partners cited the growing volume of enterprise data and the shift to lakehouse architectures as key drivers for adopting an indexing layer like Qbeast’s.

The company plans to extend its platform across more cloud environments and aims to position its indexing layer as an essential component of open data stacks.