Key Features
Expectations
Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues.
Expectations are declarative, flexible and extensible.
expect_column_values_to_not_be_null
expect_column_values_to_match_regex
expect_column_values_to_be_unique
expect_column_values_to_match_strftime_format
expect_table_row_count_to_be_between
expect_column_median_to_be_between

Tests are docs and docs are tests
This feature is in beta
Many data teams struggle to maintain up-to-date data documentation. Great Expectations solves this problem by rendering Expectations directly into clean, human-readable documentation.
Since docs are rendered from tests, and tests are run against new data as it arrives, your documentation is guaranteed to never go stale. Additional renderers allow Great Expectations to generate other type of "documentation", including slack notifications, data dictionaries, customized notebooks, etc.
Automated data profiling
This feature is experimental
Wouldn't it be great if your tests could write themselves? Run your data through one of Great Expectations' data profilers and it will automatically generate Expectations and data documentation. Profiling provides the double benefit of helping you explore data faster, and capturing knowledge for future documentation and testing.Automated profiling doesn't replace domain expertise—you will almost certainly tune and augment your auto-generated Expectations over time—but it's a great way to jump start the process of capturing and sharing domain knowledge across your team.


Batteries-included data validation
Expectations are a great start, but it takes more to get to production-ready data validation. Where are Expectations stored? How do they get updated? How do you securely connect to production data systems? How do you notify team members and triage when data validation fails?
Great Expectations supports all of these use cases out of the box. Instead of building these components for yourself over weeks or months, you will be able to add production-ready validation to your pipeline in a day. This “Expectations on rails” framework plays nice with other data engineering tools, respects your existing name spaces, and is designed for extensibility.
Pluggable and extensible
Every component of the framework is designed to be extensible: Expectations, storage, profilers, renderers for documentation, actions taken after validation, etc. This design choice gives a lot of creative freedom to developers working with Great Expectations.
Recent extensions include:
- Renderers for data dictionaries
- BigQuery and GCS integration
- Notifications to MatterMost
- We're very excited to see what other plugins the data community comes up with!
Quick Start
pip install great_expectations
great_expectations init
We recommend deploying within a virtual environment. If you’re not familiar with pip, virtual environments, notebooks, or git, you may want to check out the Supporting Resources section to set up your environment.
View our full documentation
Ready to dive in and start implementing? Head to our docs to take the next leap
learn moreSee our progess on GitHub
We keep our GitHub issues update with what we are working on while addressing our communities issues.
learn moreFind help on Slack
Feel free to ask us a question on slack! There are always contributors and other users there.
learn moreJoin us on Discuss
A place where you can find and share advice on GE implementing and keep up on some of the more cutting edge developments on Great Expectations
learn moreIntegrations
Some integrations are not yet fully tested and documented.
Please reach out on slack with questions. If you're feeling really motivated, you can help us make our integrations better by contributing!
Pandas
Great for in-memory machine learning pipelines!
Spark
Good for really big data.
Postgres
Leading open source database
BigQuery
Google serverless massive-scale SQL analytics platform
Dagster
A data orchestrator for machine learning, analytics, and ETL.
Databricks
Managed Spark Analytics Platform
MySQL
Leading open source database
Microsft SQL Server
Leading open source database
AWS Redshift
Cloud-based data warehouse
AWS S3
Cloud based blob storage
Snowflake
Cloud-based data warehouse
Prefect
Open source workflow management system
Apache Airflow
An open source orchestration engine
Other SQL Relational DBs
Most RDBMS are supported via SQLalchemy
Jupyter Notebooks
The best way to build Expectations
Slack
Get automatic data quality notifications!