Great Expectations

Great Expectations provides an open-source Python-based framework designed for data testing, documentation, and profiling. The core of its functionality lies in "Expectations," which are verifiable assertions about data that function as clear, expressive data quality tests. The company serves data engineers, data scientists, and developers who need to maintain data quality throughout the entire data lifecycle, from ingestion to consumption. The business model is centered around a commercial offering, GX Cloud, which is a fully managed SaaS platform that builds upon the open-source core. This end-to-end platform aims to streamline data quality management, enabling teams to collaborate and ensure data reliability.

The company was founded in 2017 by Abe Gong and James Campbell. Abe Gong, the CEO, previously led data science teams at Jawbone and Fitbit, where he encountered the data quality challenges that inspired the creation of Great Expectations. James Campbell, the former CTO, has a background in software engineering and data infrastructure. The project originated from the founders' experiences with the high costs and inefficiencies of ad-hoc data testing. They aimed to create a shared, open standard for data quality. The company is backed by venture capital firms, having raised a $40 million Series B round in 2022 led by Tiger Global, with participation from Index Ventures, Coatue, and other investors. This followed a $21 million Series A in 2021.

The open-source tool, GX Core, integrates with various data sources and execution engines like pandas, Spark, and SQL. It automatically generates data documentation and data quality reports from the Expectations, making it easier for teams to understand and trust their data. GX Cloud extends these capabilities with features for collaboration, monitoring, and governance in a hosted environment, targeting enterprise-level data teams.

Keywords: data quality, open-source, data testing, data validation, Python framework, data profiling, data documentation, data governance, SaaS, data engineering