Qbeast provides a Universal Storage Engine that optimizes data layout for analytics and AI workloads, enabling efficient querying and faster insights. By reducing the amount of data processed and accelerating query execution times, Qbeast addresses the challenges of slow data retrieval and high operational costs in data lakehouse environments.
Funding
$3.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
IIFounders
Product
Problem
Data lakehouses face challenges in efficiently querying and processing large datasets for analytics and AI workloads. Slow data retrieval and high operational costs hinder the ability to gain timely insights from data.
Solution
Qbeast provides a Universal Storage Engine (USE) that optimizes data layout within a data lakehouse, enabling faster query execution and reduced data processing. By intelligently organizing data, Qbeast minimizes the amount of data that needs to be read and processed for each query. This leads to accelerated query performance, reduced AI training times, and lower operational costs. Qbeast integrates with existing data lakehouse environments, such as Databricks and Snowflake, to streamline data management and improve overall efficiency.
Target Audience
The primary target audience includes data engineers, data scientists, and analytics professionals working with data lakehouses who need to improve query performance and reduce data processing costs.
Features
- Optimizes data layout for analytics and AI workloads
- Enables efficient querying through data sampling
- Reduces the amount of data read and processed per query
- Integrates with Databricks and Snowflake
- Open-source architecture
- Scales with data needs