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.

Supporting Resources