Qbeast, a data infrastructure company that originated from research at the Barcelona Supercomputing Center, has raised US$7.6 million in seed funding led by Peak XV’s Surge, with participation from HWK Tech Investment and Elaia Partners.

The new capital will be used to expand the team and broaden product support for analytics use cases.

The funding announcement was made from Bellevue, WA.

Qbeast provides a platform that integrates with open data formats like Delta Lake, Apache Iceberg, and Apache Hudi to optimize data queries and reduce compute costs.

Qbeast’s technology uses multi-dimensional indexing to accelerate both real-time and historical queries, and works with compute engines such as Spark, Databricks, Snowflake, DuckDB, and Polars.

Srikanth Satya, formerly of AWS and Microsoft Azure, has been appointed CEO to lead the company’s next phase.

🔗 Source: Qbeast

🧠 Food for thought

1️⃣ Hidden compute waste creates massive cost burden for data-driven companies

The data lakehouse architecture has created an unexpected efficiency problem that’s draining enterprise budgets.

Companies using popular open formats like Delta Lake, Apache Iceberg, and Apache Hudi are wasting significant compute resources scanning irrelevant data, according to multiple industry sources12.

This inefficiency becomes particularly costly as enterprises ramp up AI spending, with large companies projected to spend at least $25 million on AI initiatives in 20252.

The problem is amplified by the explosive growth in data volumes, where traditional partitioning and indexing methods struggle to keep pace with complex multi-dimensional queries across time, geography, and customer segments.

Early adopters testing solutions like Qbeast’s multi-dimensional indexing report compute cost reductions of up to 70% in production environments across finance, healthcare, and retail23.

This suggests the compute waste issue represents a significant hidden tax on data infrastructure that many organizations may not fully recognize until they implement more efficient indexing strategies.

2️⃣ Research institutions continue driving breakthrough data infrastructure innovations