Superconductive scores $21M Series A funding to sustain growth of its Great Expectations open source framework for data quality
Ensuring data quality is essential for analytics, data science and machine learning. Superconductive’s Great Expectations open source framework wants to do for data quality what test-driven development did for software quality
Technical debt is a well-known concept in software development. It’s what happens when unclear or forgotten assumptions are buried inside a complex, interconnected codebase, and it leads to poor software quality. The same thing also applies to data pipelines, it’s called pipeline debt, and it’s time we did something about it.
That’s the gist of what motivated Abe Gong and James Campbell to start Great Expectations in 2018. Great Expectations is an open-source framework that aims to make it easier to test data pipelines, and therefore increase data quality.
Today Superconductive, the force behind Great Expectations, has announced it has received $21 million in Series A funding led by Index Ventures with CRV and Root Ventures participating. ZDNet caught up with Gong to learn more about Great Expectations.
The antidote to technical debt is no mystery: automated testing. Testing builds self-awareness by systematically surfacing errors. Virtually all modern software teams rely heavily on automated testing to manage complexity — except the teams that build data pipelines.
Read the full article on ZDNet
Related posts:
Is open source the way to go for observability? Grafana Labs scores $24M Series A funding to try to prove this
A troubleshooting platform for free: Netdata scores $14.2M funding to extend its open-source application monitoring platform
Streamlit wants to revolutionize building machine learning and data science applications, scores $21 million Series A funding
Open-source growth and venture capital investment: Data, databases, challenges, and opportunities