[Funding alert] Berlin-based Startup GlassFlow Secures $1.1M in Pre-Seed Funding

Event-driven data pipelines allow many of the items and services that we utilise on a daily basis to function effectively, even though it may not be a topic of discussion at the dinner table or something you or I think about on a regular basis.

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A potential application for event-driven data pipelines is the open-source Kafka Apache system, which was developed by the Apache Software Foundation and initially made available in 2011.

GlassFlow seeks to transform this data-hungry process by beginning at the bottom and offering developers everywhere a more straightforward, efficient, and adaptable solution.

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GlassFlow claims that its technology scales automatically to satisfy the performance needs of the pipelines and that it provides developers with a serverless conversion engine that lets them establish applications as functions and events.

Roosh Ventures principal Andrew Tymovskyi further said, “The demand for real-time data processing is skyrocketing, and Glassflow’s streamlined approach is a game-changer, By consolidating complex tools into a single platform, it empowers teams to deliver results in record time. What truly excites us is the stellar team behind Glassflow, led by Armend and Ashish, boasting over a decade of expertise in real-time data processing, with their prior collaborative successes spelling a promising journey ahead.”

About GlassFlow

To match customer expectations, organisations use sophisticated tech platforms for data streaming. Applications' adaptability is hampered by the difficulties in creating and upholding event-driven data pipelines, which frequently call for significant data engineering work.

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The goal of GlassFlow is to allow Python developers from various backgrounds and enterprises of all sizes to take full advantage of streaming data by democratising real-time data processing.

The product comes with a serverless transformation engine that allows customers define applications as functions and events, as well as an intuitive configuration that adjusts automatically to the performance requirements of the pipelines.