Fivetran will now lead the Great Expectations open source project, the company announced, taking stewardship of the GX Core framework that has become a cornerstone of data quality for modern data platforms. The move is part of a broader push for open data infrastructure as organisations grapple with the demands of AI.
Under the planned transition, GX Core will remain a community-driven open source project. Fivetran intends to support ongoing maintenance, ecosystem integrations, and community engagement. The company also plans to hire engineering talent with experience developing GX Core to bolster those efforts. A Fivetran employee has clarified on Reddit that there are no plans to create a commercial offering for GX Core, reinforcing its open source nature.
“The GX community has always focused on helping practitioners build systems they can trust,” said Hernan Alvarez, CEO of Great Expectations. “We believe Fivetran understands the growing importance of open, reliable data infrastructure in the AI era and is well positioned to help support the continued growth of the GX Core community.”
How Great Expectations ensures data quality for AI
Great Expectations is an open source Python-based framework that enables data teams to define “Expectations” — assertions about data properties — to validate datasets and ensure quality throughout the DataOps lifecycle. First released in 2018 by data engineers Abe Gong and James Campbell, it has been adopted widely across industries and integrates with tools such as Snowflake, Airflow, and Databricks.
The framework allows teams to define, validate, and monitor data quality across modern data platforms, helping improve trust in analytics, operations, and AI systems. As organisations scale AI initiatives and autonomous workflows, trusted data has become increasingly critical. Poor data quality is a primary reason for AI failures, leading to unreliable outputs, bias, and ethical risks. Great Expectations addresses this by providing a systematic way to catch data issues early, ensuring that the data feeding AI models is reliable. The global data quality tools market is projected to reach $15.96 billion by 2034, reflecting the growing importance of such infrastructure in the AI era.
Alvarez, who previously held product leadership roles at DataRobot and Algorithmia, has emphasised the critical role of high-quality data in machine learning. The GX community’s work dovetails with a broader industry trend toward AI-powered data quality solutions that automate anomaly detection and enable continuous monitoring across multi-cloud environments.
Fivetran’s vision for open data infrastructure
Fivetran’s stewardship builds on its vision for Open Data Infrastructure (ODI), an architectural approach advocating for data stored once in open formats and usable across tools, compute engines, and AI systems without vendor lock-in. The concept was announced alongside Fivetran’s planned merger with dbt Labs in October 2025, an all-stock deal that would create a combined entity with a projected annual recurring revenue of around $600 million. George Fraser, Fivetran’s CEO, will lead the combined company, with dbt Labs CEO Tristan Handy serving as co-founder and President. The merger is subject to regulatory approval and expected to close in mid to late 2026.
Fivetran, founded in 2012 and headquartered in Oakland, California, was valued at $5.6 billion as of May 2026. Anjan Kundavaram, Fivetran’s Chief Product Officer, who joined the company in May 2024 after product leadership roles at Precisely and Hitachi Vantara, said: “AI increases the importance of open and trustworthy data systems. Practitioners need infrastructure that works across clouds, platforms, and tools without locking them into closed ecosystems. The GX community has built an important part of that foundation.”
Fivetran’s own Managed Data Lake Service is built on these ODI principles, reflecting the company’s commitment to open, interoperable data infrastructure that gives organisations control over their data and costs.
