Bellevue-based startup Qbeast has successfully secured $7.6 million in a seed funding round to advance its data optimization platform. The company, which spun out of the Barcelona Supercomputing Center, aims to revolutionize analytics on open data platforms like Delta Lake, Apache Iceberg, and Apache Hudi. By using a unique multi-dimensional indexing layer, Qbeast’s technology helps companies process data more efficiently and at a lower cost.
The funding round was led by Peak XV’s Surge, with participation from HWK Tech Investment and Elaia Partners. The new capital will be used to expand Qbeast’s team, broaden its product support across more analytics use cases, and continue its mission to make open data platforms faster, simpler, and more cost-efficient. The company’s platform works by prioritizing only the necessary data for a given query, which it claims can result in 2-6x faster queries and up to 70% lower compute costs. This is a significant improvement over traditional methods that can waste up to 90% of computing resources on irrelevant data.
With a new CEO, cloud infrastructure veteran Srikanth Satya, at the helm, Qbeast plans to extend its platform with features like auto-tuning and adaptive indexing. The goal is to become the default indexing layer for open Lakehouse architectures, enabling organizations across various sectors—including finance, retail, and healthcare—to extract value from their data without incurring massive cloud costs or being locked into proprietary systems.
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This article was written with the assistance of Ai.