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Maintaining data quality doesn’t have to be hard. GX Cloud is now in public preview: try it today and see value in minutes. Great Expectations is a powerful platform that's revolutionizing data quality and collaboration. Find out why companies around the world are choosing GX.
GX OSS is a data quality platform designed by and for data engineers. It helps you surface issues quickly and clearly while also making it easier to collaborate with nontechnical stakeholders. Its powerful technical tools start with Expectations: expressive and extensible unit tests for your data.
Nov 2, 2021 · Automated data profiling. Great Expectations can also profile your data automatically and based on results generate the expectations based on profiling.
Feb 4, 2021 · Feb 4, 2021. What is Great Expectations? Great Expectations is a useful tool to profile, validate, and document data. It helps to maintain the quality of data throughout a data...
Oct 15, 2021 · Introduction. In this tutorial, you will set up a local deployment of Great Expectations, an open source data validation and documentation library written in Python.
Great Expectations documentation. Learn everything you need to know about GX Cloud and GX Open Source Software (OSS)
Use this quickstart to install GX OSS, connect to sample data, build your first Expectation, validate data, and review the validation results. This is a great place to start if you're new to GX OSS and aren't sure if it's the right solution for you or your organization.
Key Features. Expectations are assertions about your data. In Great Expectations, those assertions are expressed in a declarative language in the form of simple, human-readable Python methods. For example, in order to assert that you want the column “passenger_count” to be between 1 and 6, you can say: Python. expect_column_values_to_be_between(
Aug 10, 2023 · In this article, we will discuss data quality and useful methods you can use for validating the completeness of your data pipelines using great_expectations. Data Quality:
With Great Expectations, you can assert what you expect from the data you load and transform, and catch data issues quickly – Expectations are basically unit tests for your data. Not only that, but Great Expectations also creates data documentation and data quality reports from those Expectations.
