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Version: 0.15.50

How to connect to a Redshift database

This guide will help you connect to data in a Redshift database. This will allow you to ValidateThe act of applying an Expectation Suite to a Batch. and explore your data.

Prerequisites: This how-to guide assumes you have:
  • Completed the Getting Started Tutorial
  • A working installation of Great Expectations
  • Have access to data in a Redshift database

Steps​

1. Choose how to run the code in this guide​

Get an environment to run the code in this guide. Please choose an option below.

If you use the Great Expectations CLICommand Line Interface, run this command to automatically generate a pre-configured Jupyter Notebook. Then you can follow along in the YAML-based workflow below:

great_expectations datasource new

2. Install required dependencies​

To use connect to your Redshift database, Great Expectations will require the installation of additional dependencies. Fortunately, it is simple to install the necessary dependencies for Redshift by using pip and running the following from your terminal:

pip install sqlalchemy sqlalchemy-redshift psycopg2

# or if on macOS:
pip install sqlalchemy sqlalchemy-redshift psycopg2-binary

3. Add credentials​

Great Expectations provides multiple methods of using credentials for accessing databases. Options include using a file not checked into source control, environment variables, and using a cloud secret manager. Please read the article How to Configure Credentials for instructions on alternatives.

For this guide we will use a connection_string like this:

postgresql+psycopg2://<USER_NAME>:<PASSWORD>@<HOST>:<PORT>/<DATABASE>?sslmode=<SSLMODE>

Note: Depending on your Redshift cluster configuration, you may or may not need the sslmode parameter. For more details, please refer to Amazon's documentation for configuring security options on Amazon Redshift.

4. Instantiate your project's DataContext​

Import these necessary packages and modules.

from ruamel import yaml

import great_expectations as gx
from great_expectations.core.batch import BatchRequest, RuntimeBatchRequest

Load your DataContext into memory using the get_context() method.

context = gx.get_context()

5. Configure your Datasource​

Put your connection string in this template:

name: my_redshift_datasource
class_name: Datasource
execution_engine:
  class_name: SqlAlchemyExecutionEngine
  connection_string: postgresql+psycopg2://<user_name>:<password>@<host>:<port>/<database>?sslmode=<sslmode>
data_connectors:
   default_runtime_data_connector_name:
       class_name: RuntimeDataConnector
       batch_identifiers:
           - default_identifier_name
   default_inferred_data_connector_name:
       class_name: InferredAssetSqlDataConnector
       include_schema_name: true

Run this code to test your configuration.

context.test_yaml_config(datasource_yaml)

You will see your database tables listed as Available data_asset_names in the output of test_yaml_config().

Feel free to adjust your configuration and re-run test_yaml_config as needed.

6. Save the Datasource configuration to your DataContext​

Save the configuration into your DataContext by using the add_datasource() function.

datasource_yaml = f"""
name: my_redshift_datasource
class_name: Datasource
execution_engine:
  class_name: SqlAlchemyExecutionEngine
  connection_string: postgresql+psycopg2://<user_name>:<password>@<host>:<port>/<database>?sslmode=<sslmode>
data_connectors:
   default_runtime_data_connector_name:
       class_name: RuntimeDataConnector
       batch_identifiers:
           - default_identifier_name
   default_inferred_data_connector_name:
       class_name: InferredAssetSqlDataConnector
       include_schema_name: true
"""

7. Test your new Datasource​

Verify your new DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. by loading data from it into a ValidatorUsed to run an Expectation Suite against data. using a BatchRequest.

Here is an example of loading data by specifying a SQL query.

batch_request = RuntimeBatchRequest(
    datasource_name="version-0.15.50 my_redshift_datasource",
    data_connector_name="version-0.15.50 default_runtime_data_connector_name",
    data_asset_name="version-0.15.50 default_name",  # this can be anything that identifies this data
    runtime_parameters={"query": "SELECT * from taxi_data LIMIT 10"},
    batch_identifiers={"default_identifier_name": "default_identifier"},
)

context.add_or_update_expectation_suite(expectation_suite_name="version-0.15.50 test_suite")
validator = context.get_validator(
    batch_request=batch_request, expectation_suite_name="version-0.15.50 test_suite"
)
print(validator.head())

🚀🚀 Congratulations! 🚀🚀 You successfully connected Great Expectations with your data.

Additional Notes​

To view the full scripts used in this page, see them on GitHub:

Next Steps​

Now that you've connected to your data, you'll want to work on these core skills: