Equirus InnovateX Fund Leads $1 Million Seed Round in Datazip to Revolutionize Data Engineering

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Bengaluru-based data engineering startup, Datazip, has successfully raised $1 million in a seed funding round, with Equirus InnovateX Fund (EIF) leading the investment by contributing Rs 6 crore. This is EIF’s second investment from its $30 million tech-focused venture capital fund. The startup had previously secured $202,000 from Anicut Capital and other investors, adding to its growing support within the investment community.

Datazip, co-founded in 2022 by Sandeep DevarapalliShubham Baldava, and Rohan Khameshra, is at the forefront of data engineering innovation. The startup is building a composable lakehouse platform, a cutting-edge solution designed to boost the productivity of data engineers by tenfold. The platform enhances the ability of organizations to optimize their data workflows and extract real-time insights, providing a competitive edge in a fast-paced digital landscape.

The fresh funds will be used to drive product development and innovation in data engineering and analytics within India. Datazip is already making significant strides, having onboarded 15 clients, and it plans to expand its operations over the next year. The startup’s goal is to transform how businesses manage large datasets in real-time, a critical capability in industries that increasingly rely on big data for strategic decision-making.

The data lakehouse platform market, valued at $11.1 billion in 2023, is experiencing robust growth and is projected to expand at a compound annual growth rate (CAGR) of 22.9% through 2033, potentially reaching $66.6 billion. Datazip’s innovative platform positions it well to capitalize on this rapid market growth as businesses seek scalable, efficient solutions for their data needs.

This seed round marks a significant step for Datazip as it accelerates product development and aims to redefine the future of data engineering, helping companies streamline their data pipelines and harness the full potential of their data.

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