# Great Expectations **Source:** https://geo.sig.ai/brands/great-expectations **Vertical:** Modern Data Stack & Analytics Engineering **Subcategory:** Data Quality & Validation **Tier:** Challenger **Website:** greatexpectations.io **Last Updated:** 2026-04-14 ## Summary San Francisco CA open-source data quality framework; raised $40M+; GX Cloud adds hosted monitoring and collaboration on top of the widely-used OSS library. ## Company Overview Great Expectations is a data quality and validation company founded in 2018 and headquartered in San Francisco, California. The company was founded by Abe Gong and James Campbell to commercialize the Great Expectations open-source Python framework, which they had originally built to solve data quality problems at their previous companies. The Great Expectations framework introduced the concept of treating data as code — defining expected data behaviors as declarative "expectations" in code, running them as part of CI/CD pipelines, and generating human-readable validation reports.\n\nGreat Expectations raised $40 million in funding from investors including Index Ventures and CRV. The open-source framework became one of the most widely adopted data quality tools, with millions of downloads and an active community of contributors. It supports a broad range of data sources including Pandas DataFrames, Spark, SQL databases, and all major cloud data warehouses, and integrates with orchestration tools like Airflow, Dagster, and Prefect. GX Cloud, the commercial SaaS product, adds a managed platform for sharing validation results, tracking data quality trends over time, setting up alert routing, and collaborating on data quality remediation across data teams.\n\nGreat Expectations's code-first approach and deep Pythonic integration make it the preferred data quality tool for data engineering teams with strong software engineering backgrounds. Its strength in the developer community, large library of community-contributed expectations and plugins, and integration with every major data platform give it broad reach across the data engineering ecosystem. The company has positioned GX Cloud as the collaboration and observability layer on top of the battle-tested open-source foundation. ## Frequently Asked Questions ### What is the Great Expectations open-source framework? Great Expectations is a Python library for defining, documenting, and validating data quality expectations — assertions about what data should look like, such as a column never being null, values falling within a range, or a dataset having a minimum number of rows. Expectations are defined in code, run as part of data pipelines, and generate Data Docs, human-readable HTML reports showing validation results that can be shared with technical and non-technical stakeholders. ### What is GX Cloud and how does it extend the open-source library? GX Cloud is the commercial SaaS platform built on top of the Great Expectations open-source library. It provides a hosted interface for viewing and sharing validation results, tracking data quality metrics over time, configuring alert routing to Slack and other channels when validations fail, and managing expectations and checkpoints across multiple pipelines and data sources in a collaborative team environment. ### How does Great Expectations integrate with data orchestration tools? Great Expectations integrates natively with Apache Airflow, Dagster, Prefect, and other orchestration tools so data quality validation runs automatically as part of pipeline DAGs. When a validation fails, the pipeline can halt execution, alerting data engineers before bad data propagates to downstream consumers. First-class integrations ensure that GX checkpoints can be triggered as steps in existing workflow definitions without custom glue code. ### What is Great Expectations and how is it used? Great Expectations is an open-source Python library and data quality framework that allows data teams to define, document, and validate expectations about their data — such as column types, value ranges, uniqueness constraints, and referential integrity — automatically checking these rules whenever data is processed. ### How does Great Expectations integrate with data pipelines? Great Expectations integrates as a validation step within data pipelines built on Airflow, dbt, Prefect, Dagster, Spark, and other orchestration tools, triggering data quality checks at defined checkpoints and surfacing failures before bad data reaches downstream consumers. ### What is a Data Doc in Great Expectations? Data Docs are human-readable HTML reports automatically generated by Great Expectations that document the expectations defined for a dataset and show the results of validation runs, providing a shared artifact that data engineers, analysts, and business stakeholders can use to understand data quality status. ### Is Great Expectations available as a managed service? Yes, GX Cloud is the managed SaaS version of Great Expectations offered by the company, providing a hosted environment for defining expectations, running validations, viewing results, and collaborating across teams without requiring self-hosted infrastructure management. ### What data sources does Great Expectations support? Great Expectations supports a wide range of data sources including Pandas DataFrames, Spark DataFrames, PostgreSQL, MySQL, BigQuery, Snowflake, Redshift, and files in CSV, JSON, and Parquet formats, connecting to most data stores used in modern analytical pipelines. ## Tags data-warehouse, analytics, saas, b2b, developer-tools, open-source, platform, cloud-native, scaleup --- *Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*