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Version: 0.16.16

How to instantiate an Ephemeral Data Context

An Ephemeral Data Context is a temporary, in-memory Data Context. They are ideal for doing data exploration and initial analysis when you do not want to save anything to an existing project, or for when you need to work in a hosted environment such as an EMR Spark Cluster.

Prerequisites​

Steps​

1. Import necessary classes for instantiating an Ephemeral Data Context​

To create our Data Context, we will create a configuration that uses in-memory Metadata Stores. This will require two classes from the Great Expectations module: the DataContextConfig class and the InMemoryStoreBackendDefaults class. These can be imported with the code: 

from great_expectations.data_context.types.base import (
    DataContextConfig,
    InMemoryStoreBackendDefaults,
)

We will also need to import the EphemeralDataContext class that we will be creating an instance of: 

from great_expectations.data_context import EphemeralDataContext

2. Create the Data Context configuration​

To create a Data Context configuration that specifies the use of in-memory Metadata Stores we will pass in an instance of the InMemoryStoreBackendDefaults class as a parameter when initializing an instance of the DataContextConfig class: 

project_config = DataContextConfig(
    store_backend_defaults=InMemoryStoreBackendDefaults()
)

3. Instantiate an Ephemeral Data Context​

To create our Ephemeral Data Context instance, we initialize the EphemeralDataContext class while passing in the DataContextConfig instance we previously created as the value of the project_config parameter. 

context = EphemeralDataContext(project_config=project_config)

We now have an Ephemeral Data Context to use for the rest of this Python session.

Saving the contents of an Ephemeral Data Context for future use

An Ephemeral Data Context is an in-memory Data Context that is not intended to persist beyond the current Python session. However, if you decide that you would like to save its contents for future use you can do so by converting it to a Filesystem Data Context: 

context = context.convert_to_file_context()

This method will initialize a Filesystem Data Context in the current working directory of the Python process that contains the Ephemeral Data Context. For more detailed explanation of this method, please see our guide on how to convert an ephemeral data context to a filesystem data context

Next steps​

Connecting GX to source data systems​

Now that you have an Ephemeral Data Context you will want to connect GX to your data. For this, please see the appropriate guides from the following:

Connecting GX to filesystem source data​

Connecting GX to in-memory source data​

Connecting GX to SQL source data​

Preserving the contents of an Ephemeral Data Context​

An Ephemeral Data Context is a temporary, in-memory object. It will not persist beyond the current Python session. If you decide that you would like to keep the contents of your Ephemeral Data Context for future use, please see: