From the course: Data Quality Testing with Great Expectations
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Running Validations - Great Expectations Tutorial
From the course: Data Quality Testing with Great Expectations
Running Validations
Okay, now we have the important building blocks of our data testing pipeline. Access to the data and the data test, that is our expectations. Now it's time to run those tests to see whether the data actually meets those expectations. In Gx, a validation is the process of running an expectation against a batch of data to evaluate whether the expectation holds for that batch of data or not. In Gx, you can validate an expectation in two ways. You can run an ad hoc validation on a batch of data by calling the validate method on a batch and passing in an expectation. Or you can create a permanent validation definition that is stored in your data context. I know this sounds pretty abstract, so let's put this all together in our Jupyter Notebook and run some validations. Let's run an ad hoc validation first. Remember, we created a batch of data earlier called myBatch and a simple expectation called myNotNullExpectation, we can simply use the validate method on the batch object to run a…
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