Profiling evaluates a data asset and summarizes its observed characteristics. By computing the observed properties of data, Profiling helps to reason about the data’s expected properties when creating expectation suites.

Profiling results are usually rendered into HTML - see Data Docs. GE ships with the default BasicDatasetProfiler, which will produce an expectation_suite and so validation_results that compile to a page for each table or DataFrame including an overview section:


And then detailed statistics for each column:


Profiling is still a beta feature in Great Expectations. Over time, we plan to extend and improve the BasicDatasetProfiler and also add additional profilers.

Profiling relies on automated inspection of data batches to generate and encode expectations. Together, encoding expectations, testing data, and presenting expectation validation results are the three core services offered by GE.

Warning: BasicDatasetProfiler will evaluate the entire batch without limits or sampling, which may be very time consuming. As a rule of thumb, we recommend starting with batches smaller than 100MB.

Expectations and Profiling

In order to characterize a data asset, profiling creates an expectation suite. Unlike the expectations that are typically used for data validation, these expectations do not necessarily apply any constraints; they can simply identify statistics or other data characteristics that should be evaluated and made available in GE. For example, when the BasicDatasetProfiler it encounters a numeric column, it will add an expect_column_mean_to_be_between expectation but choose the min_value and max_value to both be None: essentially saying only that it expects the mean to exist.

  "expectation_type": "expect_column_mean_to_be_between",
  "kwargs": {
    "column": "rating",
    "min_value": null,
    "max_value": null

To “profile” a datasource, therefore, the BasicDatasetProfiler included in GE will generate a large number of very loosely-specified expectations. Effectively it is asserting that the given statistic is relevant for evaluating batches of that data asset, but it is not yet sure what the statistic’s value should be.

In addition to creating an expectation suite, profiling data tests the suite against data. The validation_result contains the output of that expectation suite when validated against the same batch of data. For a loosely specified expectation like in our example above, getting the observed value was the sole purpose of the expectation.

  "success": true,
  "result": {
    "observed_value": 4.05,
    "element_count": 10000,
    "missing_count": 0,
    "missing_percent": 0

Running a profiler on a data asset can also be useful to produce a large number of expectations to review and potentially transfer to a new expectation suite used for validation in a pipeline.

See the Profiling Reference for more information.