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Brytlyt secures $5m for GPU-powered analytics database

Brytlyt funding

London-based data analytics and visualisation startup Brytlyt has raised $5m in a Series A extension from AlbionVC and existing investors Amadeus Capital Partners and Finch Capital.

The new funding will be used for recruitment and the deployment of new products across Europe and the US.

The investment marks the release of Brytlyt.io, a fully serverless deployment model for analytics and data science workloads.

It comes at a time when the data analysis sector is forecast to be worth over $75bn by 2024, according to Morgan Stanley.

Founded in 2013 by husband-and-wife duo Richard and Maria Heyns, the startup helps the world’s largest telcos, retailers and financial institutions to make sense of their data through analysis and visualisation technology. It claims to be the only GPU database working with patent-pending software built on PostgreSQL, an open source database.

The computational firepower of GPUs makes them well-suited for crunching big data. It means companies processing billion-row data sets in areas including telcos, retail and finance can drastically reduce the processing time of large datasets.

Richard Heyns, CEO of Brytlyt, said: “The investment will give us the resources to drive our strategic growth. The launch of Brytlyt.io delivers a unique way to access high-performance analytics tooling and is a genuine catalyst for positive disruption in data analytics.”

Cat McDonald, investor at AlbionVC, said: “Organisations that can extract insights from big data in a timely manner will be the leaders of tomorrow. Our investment in Brytlyt.io is in firm support of that thesis. We welcome Brytlyt.io and visionary founder Richard into our growing portfolio of cutting-edge data companies that includes Quantexa, Solidatus, Elliptic and more”

Nick Kingsbury, partner, Amadeus Capital Partners, added: “Since our initial investment the company has continued to win customers and innovate. Its latest product offers world-beating performance with corresponding lower compute cost for data intensive analytics applications, provided on a pay-per-use basis.”