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Version: 0.15.50

Tutorial, Step 2: Connect to data

SetupArrowConnect to DataArrowCreate ExpectationsArrowValidate Data

In Step 1: Setup, we created a Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components.. Now that we have that Data Context, you'll want to connect to your actual data. In Great Expectations, DatasourcesProvides a standard API for accessing and interacting with data from a wide variety of source systems. simplify these connections by managing and providing a consistent, cross-platform API for referencing data.

Create a Datasource with the CLI

Let's create and configure your first Datasource: a connection to the data directory we've provided in the repo. This could also be a database connection, but because our tutorial data consists of .CSV files we're just using a simple file store.

Start by using the CLICommand Line Interface to run the following command from your gx_tutorials directory:

great_expectations datasource new

You will then be presented with a choice that looks like this:

What data would you like Great Expectations to connect to?
1. Files on a filesystem (for processing with Pandas or Spark)
2. Relational database (SQL)

The only difference is that we've included a "1" after the colon and you haven't typed anything in answer to the prompt, yet.

As we've noted before, we're working with .CSV files. So you'll want to answer with 1 and hit enter.

The next prompt you see will look like this:

What are you processing your files with?
1. Pandas
2. PySpark

For this tutorial we will use Pandas to process our files, so again answer with 1 and press enter to continue.


When you select 1. Pandas from the above list, you are specifying your Datasource's Execution EngineA system capable of processing data to compute Metrics.. Although the tutorial uses Pandas, Spark and SqlAlchemy are also supported as Execution Engines.

We're almost done with the CLI! You'll be prompted once more, this time for the path of the directory where the data files are located. The prompt will look like:

Enter the path of the root directory where the data files are stored. If files are on local disk
enter a path relative to your current working directory or an absolute path.

The data that this tutorial uses is stored in gx_tutorials/data. Since we are working from the gx_tutorials directory, you only need to enter data and hit return to continue.

This will now open up a new Jupyter Notebook to complete the Datasource configuration. Your console will display a series of messages as the Jupyter Notebook is loaded, but you can disregard them. The rest of the Datasource setup takes place in the Jupyter Notebook and we won't return to the terminal until that is done.

The datasource new notebook

The Jupyter Notebook contains some boilerplate code to configure your new Datasource. You can run the entire notebook as-is, but we recommend changing at least the Datasource name to something more specific.

Edit the second code cell as follows:

datasource_name = "version-0.15.50 getting_started_datasource"

Then execute all cells in the notebook in order to save the new Datasource. If successful, the last cell will print a list of all Datasources, including the one you just created.

Before continuing, let’s stop and unpack what just happened.

Configuring Datasources

When you completed those last few steps, you told Great Expectations that:

  • You want to create a new Datasource called getting_started_datasource (or whatever custom name you chose above).
  • You want to use Pandas to read the data from CSV.

Based on that information, the CLI added the following entry into your great_expectations.yml file, under the datasources header:

datasource_yaml = f"""
name: getting_started_datasource
class_name: Datasource
class_name: PandasExecutionEngine
class_name: InferredAssetFilesystemDataConnector
base_directory: ../data/
- data_asset_name
pattern: (.*)
class_name: RuntimeDataConnector
- runtime_batch_identifier_name

Please note that due to how data is serialized, the entry in your great_expectations.yml file may not have these key/value pairs in the same order as the above example. However, they will all have been added.

What does the configuration contain?

ExecutionEngine : The Execution EngineA system capable of processing data to compute Metrics. provides backend-specific computing resources that are used to read-in and perform validation on data. For more information on ExecutionEngines, please refer to the following Core Concepts document on ExecutionEngines

DataConnectors : Data ConnectorsProvides the configuration details based on the source data system which are needed by a Datasource to define Data Assets. facilitate access to external data stores, such as filesystems, databases, and cloud storage. The current configuration contains both an InferredAssetFilesystemDataConnector, which allows you to retrieve a batch of data by naming a data asset (which is the filename in our case), and a RuntimeDataConnector, which allows you to retrieve a batch of data by defining a filepath. In this tutorial we will only be using the InferredAssetFilesystemDataConnector. For more information on DataConnectors, please refer to the Core Concepts document on Datasources.

This Datasource does not require any credentials. However, if you were to connect to a database that requires connection credentials, those would be stored in great_expectations/uncommitted/config_variables.yml.

In the future, you can modify or delete your configuration by editing your great_expectations.yml and config_variables.yml files directly.

For now, let’s move on to Step 3: Create Expectations.