How to configure a Redshift Datasource

This guide shows how to connect to a Redshift Datasource.


Show Docs for Stable API (up to 0.12.x)

Prerequisites: This how-to guide assumes you have already:

To add a Redshift datasource, do the following:

  1. Install the required modules

    If you haven’t already, install these modules for connecting to Redshift.

    pip install sqlalchemy
    pip install psycopg2
    # or if on macOS:
    pip install psycopg2-binary
  2. Run datasource new

    From the command line, run:

    great_expectations datasource new
  3. Choose “Relational database (SQL)”

    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)
    : 2
  4. Choose Redshift

    Which database backend are you using?
        1. MySQL
        2. Postgres
        3. Redshift
        4. Snowflake
        5. BigQuery
        6. other - Do you have a working SQLAlchemy connection string?
    : 3
  5. Give your Datasource a name

    When prompted, provide a custom name for your Redshift data source, or hit Enter to accept the default.

    Give your new Datasource a short name.
  6. Provide credentials

    Next, you will be asked to supply the credentials for your Redshift instance:

    Next, we will configure database credentials and store them in the `my_redshift_db` section
    of this config file: great_expectations/uncommitted/config_variables.yml:
    What is the host for the Redshift connection? []:
    What is the port for the Redshift connection? [5439]:
    What is the username for the Redshift connection? []: myusername
    What is the password for the Redshift connection?:
    What is the database name for the Redshift connection? []: my_database
    What is sslmode name for the Redshift connection? [prefer]: prefer

    Great Expectations will store these secrets privately on your machine. They will not be committed to git.

  7. Wait to verify your connection

    You will then see the following message on your terminal screen:

    Attempting to connect to your database. This may take a moment...

    If all goes well, it will be followed by the message:

    Great Expectations connected to your database!

    If you run into an error, you will see something like:

    Cannot connect to the database.
      - Please check your environment and the configuration you provided.
      - Database Error: Cannot initialize datasource my_redshift_db, error: (psycopg2.OperationalError) could not connect to server: No such file or directory
        Is the server running locally and accepting
        connections on Unix domain socket "/tmp/.s.PGSQL.5439"?
    (Background on this error at:
    Enter the credentials again? [Y/n]: n

    In this case, please check your credentials, ports, firewall, etc. and try again.

  8. Save your new configuration

    Finally, you’ll be asked to confirm that you want to save your configuration:

    Great Expectations will now add a new Datasource 'my_redshift_db' to your deployment, by adding this entry to your great_expectations.yml:
        credentials: ${my_redshift_db}
          class_name: SqlAlchemyDataset
          module_name: great_expectations.dataset
        class_name: SqlAlchemyDatasource
    The credentials will be saved in uncommitted/config_variables.yml under the key 'my_redshift_db'
    Would you like to proceed? [Y/n]:

    After this confirmation, you can proceed with exploring the data sets in your new Redshift Datasource.

Show Docs for Experimental API (0.13)

Prerequisites: This how-to guide assumes you have already:

To add a Redshift datasource, do the following:

  1. Install the required modules.

    If you haven’t already, install these modules for connecting to Redshift.

    pip install sqlalchemy
    pip install psycopg2
    # or if on macOS:
    pip install psycopg2-binary
  2. Instantiate a DataContext.

    Create a new Jupyter Notebook and instantiate a DataContext by running the following lines:

    import great_expectations as ge
    context = ge.get_context()
  3. Create or copy a yaml config.

    Parameters can be set as strings, or passed in as environment variables. In the following example, a yaml config is configured for a SimpleSqlalchemyDatasource with associated credentials. Username, password, host, port, and database are set as strings.

    Additional examples of yaml configurations for various filesystems and databases can be found in the following document: How to configure DataContext components using test_yaml_config

    config = f"""
      class_name: SimpleSqlalchemyDatasource
        drivername: postgresql+psycopg2
        username: YOUR_REDSHIFT_USERNAME
        password: YOUR_REDSHIFT_PASSWORD
        port: 5439
        database: dev
          sslmode: prefer
        data_asset_name_suffix: __whole_table
  4. Run context.test_yaml_config.


    When executed, test_yaml_config will instantiate the component and run through a self_check procedure to verify that the component works as expected.

    The resulting output will look something like this:

    Attempting to instantiate class from config...
    Instantiating as a Datasource, since class_name is SimpleSqlalchemyDatasource
    Successfully instantiated SimpleSqlalchemyDatasource
    Execution engine: SqlAlchemyExecutionEngine
    Data connectors:
        whole_table : InferredAssetSqlDataConnector
    Available data_asset_names (1 of 1):
        imdb_100k_main__whole_table (1 of 1): [{}]
    Unmatched data_references (0 of 0): []
    Choosing an example data reference...
        Reference chosen: {}
        Fetching batch data...
        Showing 5 rows
       movieid                               title                                       genres
        0        1                    Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy
        1        3             Grumpier Old Men (1995)                               Comedy|Romance
        2        5  Father of the Bride Part II (1995)                                       Comedy
        3        7                      Sabrina (1995)                               Comedy|Romance
        4        9                 Sudden Death (1995)                                       Action

    If something about your configuration wasn’t set up correctly, test_yaml_config will raise an error. Whenever possible, test_yaml_config provides helpful warnings and error messages, like the example below. It can’t solve every problem, but it can solve many.

    psycopg2.OperationalError: FATAL:  password authentication failed for user "my_username"
    FATAL:  password authentication failed for user "my_username"
  5. Save the config.

    Once you are satisfied with the config of your new Datasource, you can make it a permanent part of your Great Expectations setup. First, create a new entry in the datasources section of your great_expectations/great_expectations.yml with the name of your Datasource (which is my_redshift_datasource in our example). Next, copy the yml snippet from Step 3 into the new entry.

    Note: Please make sure the yml is indented correctly. This will save you from much frustration.

Additional Notes

  1. Depending on your Redshift cluster configuration, you may or may not need the sslmode parameter.

  2. Should you need to modify your connection string, you can manually edit the great_expectations/uncommitted/config_variables.yml file.

  3. You can edit the great_expectations/uncommitted/config_variables.yml file to accomplish the connection configuration without using the CLI. The entry would have the following format:

        url: "postgresql+psycopg2://username:password@host:port/database_name?sslmode=require"