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https://github.com/great-expectations/great_expectations

Always know what to expect from your data.
https://github.com/great-expectations/great_expectations

cleandata data-engineering data-profilers data-profiling data-quality data-science data-unit-tests datacleaner datacleaning dataquality dataunittest eda exploratory-analysis exploratory-data-analysis exploratorydataanalysis mlops pipeline pipeline-debt pipeline-testing pipeline-tests

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Always know what to expect from your data.

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README 

        
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Great Expectations

================================================================================



*Always know what to expect from your data.*



Important announcements regarding our upcoming 1.0 release

================================================================================

We’re planning a ton of work to take GX OSS to the next level as we officially graduate it to 1.0!



Our biggest goal is to improve the user and contributor experiences by **streamlining the API**, based on the feedback

we’ve

gotten from the community (thank you!) over the years.



Learn more about our plans for 1.0 and how we’ll be making this transition in

our [blog post](https://greatexpectations.io/blog/the-next-step-for-gx-oss-1-0).



As we gear up for the launch of our 1.0 release early next year, we want to share an important update regarding our

current development process.



Temporary hold on PRs

--------------------------------------------------------------------------------



We’re temporarily pausing the acceptance of new pull requests (PRs). We’re going to be updating the API and codebase

frequently and significantly over the next few months—we don’t want contributors to spend time and effort only to find

that we’ve just implemented a breaking change for their work.



Looking forward

--------------------------------------------------------------------------------

We deeply value the contributions and engagement of our community. Please hold onto your fantastic ideas and PRs until

after the 1.0 release, when we will be excited to resume accepting them. We appreciate your understanding and support

as we make this final push toward this exciting milestone. Please watch for updates in our

[slack](https://greatexpectations.io/slack) community, and thank you for being a crucial part of our journey!



What is GX?

--------------------------------------------------------------------------------



Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing,

documentation, and profiling.



Data practitioners know that testing and documentation are essential for managing complex data pipelines. GX makes it

possible for data science and engineering teams to quickly deploy extensible, flexible data quality testing into their

data stacks. Its human-readable documentation makes the results accessible to technical and nontechnical users.



[See Down with Pipeline Debt!](https://greatexpectations.io/blog/down-with-pipeline-debt-introducing-great-expectations/)

for an introduction to our philosophy of pipeline data quality testing.



Key features

--------------------------------------------------



### Seamless operation



GX fits into your existing tech stack, and can integrate with your CI/CD pipelines to add data quality exactly where you

need it. Connect to and validate your data wherever it already is, so you can focus on honing your Expectation Suites to

perfectly meet your data quality needs.



### Start fast



Get useful results quickly even for large data volumes. GX’s Data Assistants provide curated Expectations for different

domains, so you can accelerate your data discovery to rapidly deploy data quality throughout your pipelines.

Auto-generated Data Docs ensure your DQ documentation will always be up-to-date.



![data_assistant_plot_expectations_and_metrics](./docs/readme_assets/data_assistant_plot_expectations_and_metrics.png)



### Unified understanding



Expectations are GX’s workhorse abstraction: each Expectation declares an expected state of the data. The Expectation

library provides a flexible, extensible vocabulary for data quality—one that’s human-readable, meaningful for technical

and nontechnical users alike. Bundled into Expectation Suites, Expectations are the ideal tool for characterizing

exactly what you expect from your data.



- `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`

- ...and [many more](https://greatexpectations.io/expectations)



### Secure and transparent



GX doesn’t ask you to exchange security for your insight. It processes your data in place, on your systems, so your

security and governance procedures can maintain control at all times. And because GX’s core is and always will be open

source, its complete transparency is the opposite of a black box.



### Data contracts support



Checkpoints are a transparent, central, and automatable mechanism for testing Expectations and evaluating your data

quality. Every Checkpoint run produces human-readable Data Docs reporting the results. You can also configure

Checkpoints to take Actions based on the results of the evaluation, like sending alerts and preventing low-quality data

from moving further in your pipelines.



![Image of data contact support](./docs/readme_assets/data-contract-support.png)



### Readable for collaboration



Everyone stays on the same page about your data quality with GX’s inspectable, shareable, and human-readable Data Docs.

You can publish Data Docs to the locations where you need them in a variety of formats, making it easy to integrate Data

Docs into your existing data catalogs, dashboards, and other reporting and data governance tools.



![Image of data docs](./docs/readme_assets/datadocs-update.jpg)



Quick start

-------------------------------------------------------------



To see Great Expectations in action on your own data:



You can install it using pip



```

pip install great_expectations

```



and then run



```python

import great_expectations as gx



context = gx.get_context()

```



(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](https://docs.greatexpectations.io/docs/terms/supporting_resource/), which will teach you how

to get up and running in minutes.)



For full documentation, visit [https://docs.greatexpectations.io/](https://docs.greatexpectations.io/).



If you need help, hop into our [Slack channel](https://greatexpectations.io/slack)—there are always contributors

and other users there.



Integrations

-------------------------------------------------------------------------------

Great Expectations works with the tools and systems that you're already using with your data, including:



	

		

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What is GX _not_?

-------------------------------------------------------------



Great Expectations is _not_ a pipeline execution framework. Instead, it integrates seamlessly with DAG execution tools

like [Spark]( https://spark.apache.org/), [Airflow](https://airflow.apache.org/), [dbt]( https://www.getdbt.com/)

, [prefect](https://www.prefect.io/), [dagster]( https://github.com/dagster-io/dagster)

, [Kedro](https://github.com/quantumblacklabs/kedro), [Flyte](https://flyte.org/), etc. GX carries out your data quality

pipeline testing while these tools execute the pipelines.



Great Expectations is _not_ a database or storage software. It processes your data in place, on your existing systems.

Expectations and Validation Results that GX produces are metadata about your data.



Great Expectations is _not_ a data versioning tool. If you want to bring your data itself under version control, check

out tools like [DVC](https://dvc.org/), [Quilt](https://github.com/quiltdata/quilt),

and [lakeFS](https://github.com/treeverse/lakeFS/).



Great Expectations is _not_ a language-agnostic platform. Instead, it follows the philosophy of “take the compute to the

data” by using the popular Python language to support native execution of Expectations in pandas, SQL (via SQLAlchemy),

and Spark environments.



Great Expectations is _not_ exclusive to Python programming environments. It can be invoked from the command line

without a Python environment. However, if you’re working into another ecosystem, you may want to explore

ecosystem-specific alternatives such as [assertR](https://github.com/ropensci/assertr) (for R environments)

or [TFDV](https://www.tensorflow.org/tfx/guide/tfdv) (for Tensorflow environments).



Who maintains Great Expectations?

-------------------------------------------------------------



Great Expectations OSS is under active development by GX Labs and the Great Expectations community.



What's the best way to get in touch with the Great Expectations team?

--------------------------------------------------------------------------------



If you have questions, comments, or just want to have a good old-fashioned chat about data quality, please hop on our

public [Slack channel](https://greatexpectations.io/slack) or post in

our [Discourse](https://discourse.greatexpectations.io/).



Can I contribute to the library?

--------------------------------------------------------------------------------



Absolutely. Yes, please.

See [Contributing code](https://github.com/great-expectations/great_expectations/blob/develop/CONTRIBUTING_CODE.md)

, [Contributing Expectations](https://github.com/great-expectations/great_expectations/blob/develop/CONTRIBUTING_EXPECTATIONS.md)

, [Contributing packages](https://github.com/great-expectations/great_expectations/blob/develop/CONTRIBUTING_PACKAGES.md)

,

or [Contribute to Great Expectations documentation](https://github.com/great-expectations/great_expectations/tree/develop/docs)

, and please don't be shy with questions.



How do I stay up to date with Great Expectations?

--------------------------------------------------------------------------------



You can get updates on everything GX with our email

newsletter. [Subscribe here!](https://greatexpectations.io/newsletter?utm_source=github&utm_medium=social&utm_campaign=newsletter&utm_content=form)