Glossary of Terms
Action: A Python class with a run method that takes a Validation Result and does something with it
Batch: A selection of records from a Data Asset.
Batch Request: Provided to a Datasource in order to create a Batch.
CLI: Command Line Interface
Checkpoint: The primary means for validating data in a production deployment of Great Expectations.
Checkpoint Store: A connector to store and retrieve information about means for validating data in a production deployment of Great Expectations.
Custom Expectation: An extension of the Expectation class, developed outside of the Great Expectations library.
Data Asset: A collection of records within a Datasource which is usually named based on the underlying data system and sliced to correspond to a desired specification.
Data Connector: Provides the configuration details based on the source data system which are needed by a Datasource to define Data Assets.
Data Context: The primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components.
Data Docs: Human readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc.
Data Docs Store: A connector to store and retrieve information pertaining to Human readable documentation generated from Great Expectations metadata detailing Expectations, Validation Results, etc.
Datasource: Provides a standard API for accessing and interacting with data from a wide variety of source systems.
Evaluation Parameter: A dynamic value used during Validation of an Expectation which is populated by evaluating simple expressions or by referencing previously generated metrics.
Evaluation Parameter Store: A connector to store and retrieve information about parameters used during Validation of an Expectation which reference simple expressions or previously generated metrics.
Execution Engine: A system capable of processing data to compute Metrics.
Expectation: A verifiable assertion about data.
Expectation Store: A connector to store and retrieve information about collections of verifiable assertions about data.
Expectation Suite: A collection of verifiable assertions about data.
Metric: A computed attribute of data such as the mean of a column.
Metric Store: A connector to store and retrieve information about computed attributes of data, such as the mean of a column.
Plugin: Extends Great Expectations' components and/or functionality.
Profiler: Generates Metrics and candidate Expectations from data.
Profiling: The act of generating Metrics and candidate Expectations from data.
Renderer: A method for converting Expectations, Validation Results, etc. into Data Docs or other output such as email notifications or slack messages.
Store: A connector to store and retrieve information about metadata in Great Expectations.
Supporting Resource: A resource external to the Great Expectations code base which Great Expectations utilizes.
Validation: The act of applying an Expectation Suite to a Batch.
Validation Result: Generated when data is Validated against an Expectation or Expectation Suite.
Validation Result Store: A connector to store and retrieve information about objects generated when data is Validated against an Expectation Suite.
Validator: Used to run an Expectation Suite against data.