Great Expectations provides a mechanism to automatically generate expectations, using a feature called a Profiler. A Profiler builds an Expectation Suite from one or more Data Assets. It usually also validates the data against the newly-generated Expectation Suite to return a Validation Result. There are several Profilers included with Great Expectations.
A Profiler makes it possible to quickly create a starting point for generating expectations about a Dataset. For example, during the init flow, Great Expectations uses the SampleExpectationsDatasetProfiler to demonstrate important features of Expectations by creating and validating an Expectation Suite that has several different kinds of expectations built from a small sample of data. A Profiler is also critical to generating the Expectation Suites used during Profiling.
You can also extend Profilers to capture organizational knowledge about your data. For example, a team might have a convention that all columns named “id” are primary keys, whereas all columns ending with the suffix “_id” are foreign keys. In that case, when the team using Great Expectations first encounters a new dataset that followed the convention, a Profiler could use that knowledge to add an expect_column_values_to_be_unique Expectation to the “id” column (but not, for example an “address_id” column).
Last updated: Feb 18, 2020