Foundational, a data management platform that helps organizations build, manage, and optimize their data pipelines, is now generally available secures $8million in seed funding.
This round was led by Viola Ventures and Gradient Ventures, with participation from Asymmetric Capital Partners as well as founders and executives from Datadog, Intuit, Meta, Wiz, and others.
With the money, the company hopes to advance its technology and add new features to its products that will help data developers build at scale.
Alon Nafta said: “The inability to validate simple code changes, and the lack of visibility and controls, and the time-consuming nature of data engineering create a dramatic bottleneck on innovation and adoption of data and AI. We’re excited to join forces with Viola Ventures and Gradient Ventures to build new tools and technologies as more companies try to leverage data at scale and introduce AI to the business.”
When they started Foundational back in 2022, they already knew that writing production-grade code for data was hard, mostly because they have all experienced it ourselves. They had a thesis around why it’s hard, which is the inherent inability to understand the entire set of downstream dependencies at build-time. However this was just the beginning.
They spoke with hundreds of companies that experienced immense challenges that typically show up when the data stack is growing. They learned that understanding dependencies is one thing, but actually confirming if a code change is going to introduce a data problem is entirely different.
First, because it requires a complete understanding of the semantics and produces extremely accurate lineage, and second, because that code change can arrive from a variety of places: The operational database, ELT pipelines in the warehouse, Spark code, schema changes, and many others. They also learned that data issues were not just about data quality – they’re about impact on privacy, degradation in performance, and even inflation of cloud costs.
Analyzing source code while it’s still in git is not easy. It requires a combination of static analysis, dynamic debugging, reverse engineering, and most recently – AI. Until today, Foundational has analyzed close to 60,000 pull requests in SQL, Python, and Scala. Over time, the analysis definitely became easier.
About Foundational
Foundational, Too many data breaches occur. They make it simple to identify, address, and avoid problems in your code before you release it and contaminate the data. Use Foundational to uncover and prevent problems, establish controls and boundaries, and proactively identify code and data concerns. Without requiring any code changes, Foundational may be set up in a matter of minutes.
