KEEBO
Offers no annual fees and 29.
| Date | Investors | Amount | Round |
|---|---|---|---|
| investor investor | €0.0 | round | |
| investor | €0.0 | round | |
* | N/A | Acquisition | |
| Total Funding | 000k | ||
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Keebo provides a platform designed to automate data warehouse optimization for data teams. The company's main offering is an automated, self-learning solution that aims to accelerate analytical queries by up to 100 times and reduce cloud data warehouse costs by 30-70%. This is achieved without requiring manual tuning or code changes from the end-users. The platform works with existing data warehouses like Snowflake, Amazon Redshift, and Google BigQuery. It uses machine learning to understand query patterns and automatically implement optimizations, such as creating and managing query acceleration data structures and optimizing warehouse configurations.
Keebo was founded in 2019 by Istvan Szegedi and Barzan Mozafari. Barzan Mozafari, who serves as the Chief Scientist, is also a professor of Computer Science and Engineering at the University of Michigan, specializing in database systems and machine learning. His academic research laid the foundation for Keebo's technology. Istvan Szegedi, the CEO, has a background in product management and engineering, having previously worked at companies like Cloudera and Hortonworks. The business model is subscription-based, offering its optimization platform as a service to enterprises that rely heavily on cloud data analytics.
The company raised an $11.5 million Series A funding round in 2023, led by GreatPoint Ventures with participation from Engineering Capital, GFT Ventures, and an investment from the founder of Datadog. This funding was intended to expand the engineering team and support the company's go-to-market strategy. Keebo targets businesses with large-scale data operations, helping their data engineers and architects to reduce manual effort and cut down on escalating cloud computing expenses while improving query performance for data analysts and scientists.
Keywords: data warehouse optimization, query acceleration, cloud cost management, automated analytics, machine learning, data engineering, Snowflake optimization, big data, database performance, enterprise software