Create a Data Context

A Data Context defines the storage location for metadata, such as your configurations for Data Sources, Expectation Suites, Checkpoints, and Data Docs. It also contains your Validation Results and the metrics associated with them, and it provides access to those objects in Python, along with other helper functions.

All scripts that utilize GX Core should start with the creation of a Data Context.

The following are the available Data Context types:

• File Data Context: A persistent Data Context that stores metadata and configuration information as YAML files within a file system. File Data Contexts allow you to re-use previously configured Expectation Suites, Data Sources, and Checkpoints.

• Ephemeral Data Context: A temporary Data Context that stores metadata and configuration information in memory. This Data Context will not persist beyond the current Python session. Ephemeral Data Contexts are useful when you don’t have write permissions to a file system or if you are going to engage in data exploration without needing to save your results.

Quick Start

Prerequisites

• Python version 3.9 to 3.12
• An installation of GX Core

Request an available Data Context

Instructions

1. Run the following code to request a Data Context:

   Python input

   import great_expectations as gx
   
   context = gx.get_context()

   If you don't specify parameters with the get_context() method, GX checks your project environment and returns the first Data Context using the following criteria:

   • If a File Data Context is found in the folder hierarchy of your current working directory, get_context() will instantiate and return it.
   • Otherwise, get_context() instantiates and returns an Ephemeral Data Context.

2. Optional. Run the following code to verify the type of Data Context you received:

   Python input

   print(type(context).__name__)

   The name of the Data Context class is displayed.

Sample code

Python input

# Import great_expectations and request a Data Context.
import great_expectations as gx

context = gx.get_context()

# Optional. Check the type of Data Context that was returned.
print(type(context).__name__)

File

Prerequisites

• Python version 3.9 to 3.12
• An installation of GX Core

Create a File Data Context

Instructions

1. Run the following code to request a File Data Context:

   Python input

   import great_expectations as gx
   
   context = gx.get_context(mode="file")

   When you specify mode="file", the get_context() method instantiates and returns the first File Data Context it finds in the folder hierarchy of your current working directory.

   If a File Data Context configuration is not found, get_context(mode="file") creates a new File Data Context in your current working directory and then instantiates and returns the newly created File Data Context.

   Alternatively, you can request a specific File Data Context by providing a folder path with the project_root_dir parameter. If a File Data Context exists in the specified folder it will be instantiated and returned. If a File Data Context is not found in the specified folder, a new File Data Context will be created.

   Python input

   context = gx.get_context(mode="file", project_root_dir="./new_context_folder")

2. Optional. Run the following code to review the File Data Context configuration:

   Python input

   print(context)

   The Data Context configuration, formatted as a Python dictionary, is displayed.

Sample code

Python input

# Import great_expectations and request a Data Context.
import great_expectations as gx

context = gx.get_context(mode="file")

# Optional. Request a File Data Context from a specific folder.
context = gx.get_context(mode="file", project_root_dir="./new_context_folder")

# Optional. Review the configuration of the returned File Data Context.
print(context)

Ephemeral

Prerequisites

• Python version 3.9 to 3.12
• An installation of GX Core

Create an Ephemeral Data Context

Instructions

1. Run the following code to request an Ephemeral Data Context:

   Python input

   import great_expectations as gx
   
   context = gx.get_context(mode="ephemeral")

   Ephemeral Data Contexts are temporary and get_context(mode="ephemeral") always instantiates and returns a new Ephemeral Data Context.

2. Optional. Run the following code to review the Ephemeral Data Context configuration:

   Python input

   print(context)

   The Data Context configuration, formatted as a Python dictionary, is displayed.

Sample code

Python input

# Import great_expectations and request a Data Context.
import great_expectations as gx

context = gx.get_context(mode="ephemeral")

# Optional. Review the configuration of the returned Ephemeral Data Context.
print(context)