Press Release (ePRNews.com) - Aug 04, 2025 (UTC) - Qbeast, a Bellevue, Washington-based data infrastructure startup with roots in Barcelona, Spain, announced, that it has secured $7.6 million in a seed funding round. Led by Peak XV’s Surge (formerly Sequoia Capital India), with participation from HWK Tech Investment and Elaia Partners, the round will accelerate Qbeast’s mission to optimize analytics performance in open Lakehouse platforms like Delta Lake, Apache Iceberg, and Apache Hudi. Founded in 2022, Qbeast addresses the inefficiencies of modern data lakes, where up to 90% of compute resources are wasted on irrelevant data scans, as noted by Databricks. The company’s multidimensional indexing technology promises faster, simpler, and more cost-efficient analytics for AI-driven enterprises.
The $7.6 million seed round, Qbeast’s first major funding milestone, was led by Peak XV’s Surge, a program known for backing high-growth startups like Razorpay and Khatabook. HWK Tech Investment and Elaia Partners, both with expertise in deep tech and data infrastructure, also participated. The funds will support:
- Team Expansion: Growing engineering and go-to-market teams in Bellevue and Barcelona.
- Product Development: Enhancing compatibility with Lakehouse formats and supporting new analytics use cases, including generative AI.
- Market Penetration: Deepening integrations with cloud platforms like Databricks, Snowflake, and AWS to drive enterprise adoption.
| Funding Round | Amount | Year | Lead Investors | Supporting Investors |
|---|---|---|---|---|
| Seed | $7.6M | 2025 | Peak XV’s Surge | HWK Tech Investment, Elaia Partners |
Qbeast’s Technology
Qbeast’s platform introduces advanced multidimensional indexing to open Lakehouse architectures, enabling efficient data querying for analytics and AI workloads. By leveraging a tree-based indexing approach, Qbeast targets only relevant data, reducing compute costs and query times by up to 90%. Key features include:
- Multidimensional Indexing: Organizes data across multiple dimensions for faster retrieval in complex analytics tasks.
- Seamless Integration: Supports Delta Lake, Apache Iceberg, and Apache Hudi, compatible with platforms like Databricks, Snowflake, and AWS.
- Cost Optimization: Minimizes compute waste, addressing the “hidden tax” of inefficient data scans.
- Open-Source Foundation: Built on research from the Barcelona Supercomputing Center (BSC), fostering community collaboration.
Originating from BSC’s academic research, Qbeast combines cutting-edge innovation with industry expertise, making it a versatile solution for data-intensive sectors like finance, healthcare, and retail.
Leadership and Expertise
Founded by Cesare Cugnasco (CEO), Pol Santamaria, and Vincenzo Mullè, Qbeast’s leadership team brings expertise in data engineering, distributed systems, and academic research. Their work at BSC laid the foundation for Qbeast’s indexing technology, which has been adapted for commercial applications. Headquartered in Bellevue to leverage the U.S. tech ecosystem, Qbeast maintains strong ties to Barcelona’s research community, enabling it to attract top talent in data infrastructure.
Industry Impact and Challenges
Open Lakehouse architectures have become the standard for scalable enterprise analytics, but inefficiencies in data scanning hinder performance, with Databricks estimating 90% compute waste. Qbeast’s platform addresses this bottleneck, enabling faster and more cost-effective queries for AI and analytics workloads. As global data volumes are projected to reach 175 zettabytes by 2025, Qbeast’s technology is well-positioned to meet growing demand.
Challenges include competition from established players like Databricks, which offers optimization tools like Delta Lake, and emerging data lake solutions like Starburst and Dremio. Enterprise adoption may also face hurdles due to the complexity of integrating new indexing solutions. Qbeast mitigates these through its open-source approach, seamless compatibility, and partnerships with major cloud providers.
Stakeholder Perspectives
Cesare Cugnasco, Qbeast’s CEO, stated, “This funding validates our vision to make open Lakehouse platforms faster and more cost-efficient. We’re excited to scale our team and technology to meet the growing demands of AI-driven analytics.” A spokesperson from Peak XV’s Surge added, “Qbeast’s multidimensional indexing technology is a game-changer for the Lakehouse era, addressing a critical bottleneck in data analytics. We’re thrilled to support their mission.”
Future Outlook
With $7.6 million in funding, Qbeast is poised to expand its presence in the $30 billion data lake market. The company plans to deepen integrations with cloud platforms, enhance support for generative AI, and grow its open-source community. By 2030, Qbeast aims to capture a significant share of the analytics market, leveraging its academic roots and industry partnerships to drive innovation.
