How to configure a Redshift Datasource

This guide shows how to connect to a Redshift Datasource.

Steps

Show Docs for V2 (Batch Kwargs) API

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.
 [my_redshift_db]:

  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? []: my-datawarehouse-name.abcde1qrstuw.us-east-1.redshift.amazonaws.com
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: http://sqlalche.me/e/e3q8)
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:

  my_redshift_db:
    credentials: ${my_redshift_db}
    data_asset_type:
      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 V3 (Batch Request) API

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 --v3-api 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. You will be presented with a Jupyter Notebook which will guide you through the steps of creating a Datasource.

Redshift SimpleSqlalchemyDatasource Example.

Within this notebook, you will have the opportunity to create your own yaml Datasource configuration. The following text walks through an example.

  1. 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.

    datasource_name = "my_redshift_datasource"
config = f"""
name: {datasource_name}
class_name: SimpleSqlalchemyDatasource
credentials:
  drivername: postgresql+psycopg2
  username: YOUR_REDSHIFT_USERNAME
  password: YOUR_REDSHIFT_PASSWORD
  host: YOUR_REDSHIFT_HOSTNAME
  port: 5439
  database: YOUR_REDSHIFT_DATABASE
  query:
      sslmode: prefer
introspection:
  whole_table:
    data_asset_name_suffix: __whole_table
"""

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

  2. Run context.test_yaml_config.

    context.test_yaml_config(
    yaml_config=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): []

    This means all has gone well and you can proceed with configuring your new Datasource. If something about your configuration wasn’t set up correctly, test_yaml_config will raise an error.

  3. 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 configuration. The following method will save the new Datasource to your great_expectations.yml:

    sanitize_yaml_and_save_datasource(context, config, overwrite_existing=False)

    Note: This will output a warning if a Datasource with the same name already exists. Use overwrite_existing=True to force overwriting.

    Note: The credentials will be stored in uncommitted/config_variables.yml to prevent checking them into version control.

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:

    my_redshift_db:
    url: "postgresql+psycopg2://username:[email protected]:port/database_name?sslmode=require"

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