Glossary of Terms
The Great Expectations CLI is no longer the preferred method for implementing and configuring Great Expectations. This topic will be updated soon to reflect this change. For more information, see A fond farewell to the CLI.
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 Assistant: A utility that asks questions about your data, gathering information to describe what is observed, and then presents Metrics and proposes Expectations based on the answers.
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.