Barcelona-based Qbeast has just secured a fresh pool of €2.5 million for its solution making data lakes faster, cheaper and simpler. The startup now plans to accelerate its go-to-market strategy.
It’s a data-driven world we’re living in and managing all that data has critical importance for businesses across industries. Data gives insights into a company’s performance, informs decisions and can be used to make predictions.
A spin-off from the Barcelona Supercomputing Center – Centro Nacional de Supercomputación (BSC-CNS), Qbeast wants to make it easier to manage so-called data lakes, helping teams to better predict and optimize. The startup has just secured new investment as it plans to go to market.
- €2.5 million raised in a Seed round led by Elaia
- New investors s Sabadell Venture Capital and Uber Founding CTO Oscar Salazar and existing investor Inveready also participated
- Previous investors include BStartup Banco Sabadell and business angels.
Founded in 2020, Qbeast was launched by Cesare Cugnasco, Pol Santamaria, Paola Pardo, Clemens Jesche, and Nicolas Escartin with the aim of aligning flexibility and efficiency in data analytics.
Cesare Cugnasco, CEO at Qbeast: “Companies dealing with data have little choice: if they want to know what is happening in their business, they need to use a data warehouse. While if they want to predict the future, optimize its processes and use Machine Learning, they need a data lake. They end up using different technologies and needing different people, but also with double the cloud bill and double the time to develop, which is a huge problem. At Qbeast, we are going to change this by making the life of data teams easier and data tools more efficient.”
The startup was developed within the Barcelona Supercomputing Center and optimizes the organisation of data to make engineers work faster and cheaper – giving them more insights, an enhanced ability to build data products and to train ML models at scale.
The Barcelona-based team is building a community around the open-source data lake format. . Faster analytics and ML model training can reduce clod costs and energy consumption. For example, Qbeast reports that for one of its clients, it has improved execution time in its data analytics by 68% with full precision and by up to 50 times using sampling, enabling faster analytics, cloud cost savings, and lower energy consumption.
So far, the young company is proving interesting to Marketplaces, E-Commerce platforms, IoT, and companies from industries such as Advertising & Marketing, Manufacturing, Retail, and Financial Services.
The new funding will enable the team to accelerate go-to-market by filling key commercial positions and further investing in product development, technology, and partnerships.
Sébastien Lefebvre, Partner at Elaia: “We are proud to back Qbeast and their highly capable team anchored in advanced research. The founders are on a mission to reduce the friction that prevents hundreds of thousands of companies from efficiently leveraging data lakes because of their perceived complexity. There is a lot of value to be created by Qbeast and we can’t wait to start this journey together.”