Qbeast Secures $7.6M for Faster, Simpler Open Data Platforms
Qbeast, a startup specialising in open data platforms, has secured $7.6 million in seed funding. The company aims to make these platforms faster, simpler, and more Costco-efficient. With its innovative approach, Qbeast is set to become the default indexing layer for open lakehouse architectures.
Qbeast's platform integrates directly with Delta Lake, Apache Iceberg, and Apache Hudi, accelerating workloads significantly. It offers multidimensional indexing, allowing for optimised real-time and historical queries in a single table. This has resulted in impressive query speedups of 2-6x and compute cost reductions of up to 70 percent in sectors like finance, healthcare, and retail.
Led by Srikanth Satya, CEO of Qbeast, the company's mission is to extend its platform with auto-tuning, adaptive indexing, and increased engine support. Satya, who previously conducted research at the Barcelona Supercomputing Center, has a proven track record in developing open, multi-dimensional indexing layers for large-scale data platforms.
With the recent seed funding, Qbeast plans to expand its team and broaden its product capabilities. By making open data platforms faster and more cost-efficient, Qbeast is poised to become a key player in the open lakehouse architecture landscape.
