Great Expectations (software)

Great Expectations (software)
Name	Great Expectations
Developer	OpenSource Community, Superconductive
Released	2017
Programming language	Python
Operating system	Cross-platform
License	Apache License 2.0

Great Expectations (software) is an open-source data quality and data testing platform designed to validate, document, and profile data within data pipelines. It enables data engineers, data scientists, and analysts to define "expectations" about datasets and integrates with orchestration, storage, and analytics systems to enforce data contracts. The project emphasizes readable, testable assertions and automated documentation to improve data reliability across production environments.

Overview

Great Expectations provides a declarative framework for expressing data expectations that integrates with tools used by practitioners in Data engineering, Data science, Machine learning, Apache Airflow, and Apache Spark. It produces human-readable "data docs" that serve similar purposes to artifacts used in Continuous delivery and Software testing pipelines, enabling teams to monitor data quality like they monitor code quality. The platform interfaces with storage systems such as Amazon S3, Google Cloud Storage, and Microsoft Azure while connecting to compute engines including Databricks, Snowflake, and PostgreSQL.

History and Development

The project originated in 2017, founded by engineers who had worked on data reliability challenges at startups and enterprises influenced by practices from Netflix and LinkedIn teams. Development accelerated with contributions from the open-source community and commercial support from Superconductive, a company formed to steward the project and provide enterprise features. Over time, releases incorporated integrations inspired by platforms like Airflow, Prefect, and dbt, and the community governance model drew lessons from foundations such as the Apache Software Foundation and Linux Foundation.

Architecture and Components

Great Expectations is implemented primarily in Python and organizes functionality into components: Expectation Suites, Validators, Checkpoints, and Data Docs. Expectation Suites capture assertions similar to test suites found in JUnit and pytest workflows; Validators apply expectations to execution contexts like Apache Spark dataframes or Pandas dataframes; Checkpoints orchestrate evaluation steps analogous to CI/CD jobs; Data Docs render results for stakeholders akin to dashboards produced by Tableau or Looker. The platform interacts with metadata stores and configuration backends, working alongside services such as PostgreSQL, MySQL, MongoDB, and cloud identity systems maintained by Okta or Auth0.

Core Concepts and Features

Key concepts include Expectations, Expectation Suites, Batches, Validators, and Data Docs. Expectations are declarative assertions comparable to assertions in Selenium or RSpec, supporting quantitative checks like column ranges and qualitative checks like pattern matching inspired by practices from Regex usage in Perl and Python. Data profiling and inference borrow statistical principles used in libraries like NumPy and pandas. The platform supports versioning and collaboration patterns similar to Git workflows and produces lineage-related metadata useful for integration with OpenLineage and governance tools influenced by Apache Atlas.

Integrations and Ecosystem

Great Expectations integrates with orchestration and transformation ecosystems including Apache Airflow, dbt, Prefect, and Dagster, and storage/compute platforms such as Snowflake, Databricks, BigQuery, Amazon Redshift, and Apache Spark. Monitoring and observability integrations align with systems like Prometheus, Grafana, and Sentry for alerting and metrics. The ecosystem includes community-contributed connectors and plugins inspired by adapter patterns in Hibernate and SQLAlchemy, and enterprise connectors paralleling integrations from Confluent and Fivetran.

Use Cases and Adoption

Organizations use Great Expectations to implement data contracts, validate ETL pipelines, monitor model input quality for TensorFlow and PyTorch models, and generate compliance artifacts for regulations such as frameworks adopted in GDPR-affected workflows. Use cases range from retail analytics for companies like Walmart and Target-scale operations to fintech pipelines following practices in Goldman Sachs and JPMorgan Chase data teams. Adoption spans startups, academic institutions modeled after data practices at Stanford University and MIT, and government agencies that emphasize reproducibility following examples from NASA and NOAA.

Licensing and Governance

Great Expectations is released under the Apache License 2.0, enabling permissive use similar to many projects incubated by the Apache Software Foundation. Governance combines a core maintainers team, corporate stewardship by Superconductive, and community contributors following contribution models used by projects like Kubernetes and TensorFlow. Roadmaps and release management often mirror practices adopted by large open-source projects including Linux distributions and cloud-native projects overseen by the Cloud Native Computing Foundation.

Category:Data quality Category:Open-source software