How to Validate data without a Checkpoint
As part of the new modular expectations API in Great Expectations, Validation Operators have evolved into Class-Based CheckpointsThe primary means for validating data in a production deployment of Great Expectations.. This means running a Validation without a Checkpoint is no longer supported in Great Expectations version 0.13.8 or later. Please read Checkpoints and Actions to learn more.
This guide originally demonstrated how to load an Expectation SuiteA collection of verifiable assertions about data. and ValidateThe act of applying an Expectation Suite to a Batch. data without using a CheckpointThe primary means for validating data in a production deployment of Great Expectations.. As that process might have been suitable for environments or workflows where a user does not want to or cannot create a Checkpoint, e.g. in a hosted environment. However, this workflow is no longer supported as of Great Expectations version 0.13.8 or later.
Instead, the recommended workflow is to use an in-memory instance of a Checkpoint loaded from a YAML configuration or Python dictionary that is also defined in-memory. Documentation for this process is forthcoming.