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URL: https://www.cdata.com/kb/tech/snowflake-cloud-postgres.rst

โ‡ฑ Connect to Live Snowflake Data in PostGresSQL Interface through CData Connect AI


Connect to Live Snowflake Data in PostGresSQL Interface through CData Connect AI

๐Ÿ‘ Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Create a live connection to Snowflake in CData Connect AI and connect to your Snowflake data from PostgreSQL.

There are a vast number of PostgreSQL clients available on the Internet. PostgreSQL is a popular interface for data access. When you pair PostgreSQL with CData Connect AI, you gain database-like access to live Snowflake data from PostgreSQL. In this article, we walk through the process of connecting to Snowflake data in Connect AI and establishing a connection between Connect AI and PostgreSQL using a TDS foreign data wrapper (FDW).

CData Connect AI provides a pure SQL Server interface for Snowflake, allowing you to query data from Snowflake without replicating the data to a natively supported database. Using optimized data processing out of the box, CData Connect AI pushes all supported SQL operations (filters, JOINs, etc.) directly to Snowflake, leveraging server-side processing to return the requested Snowflake data quickly.

About Snowflake Data Integration

CData simplifies access and integration of live Snowflake data. Our customers leverage CData connectivity to:

  • Reads and write Snowflake data quickly and efficiently.
  • Dynamically obtain metadata for the specified Warehouse, Database, and Schema.
  • Authenticate in a variety of ways, including OAuth, OKTA, Azure AD, Azure Managed Service Identity, PingFederate, private key, and more.

Many CData users use CData solutions to access Snowflake from their preferred tools and applications, and replicate data from their disparate systems into Snowflake for comprehensive warehousing and analytics.

For more information on integrating Snowflake with CData solutions, refer to our blog: https://www.cdata.com/blog/snowflake-integrations.


Getting Started


Connect to Snowflake in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

  1. Log into Connect AI, click Sources, and then click Add Connection
  2. ๐Ÿ‘ Adding a Connection
  3. Select "Snowflake" from the Add Connection panel
  4. ๐Ÿ‘ Selecting a data source
  5. Enter the necessary authentication properties to connect to Snowflake.

    To connect to Snowflake:

    1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
    2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
    3. Set Warehouse to the Snowflake warehouse.
    4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
    5. (Optional) Set Database and Schema to restrict the tables and views exposed.
    6. (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.

    See the Getting Started guide in the CData driver documentation for more information.

    ๐Ÿ‘ Configuring a connection (Salesforce is shown)
  6. Click Save & Test
  7. Navigate to the Permissions tab in the Add Snowflake Connection page and update the User-based permissions. ๐Ÿ‘ Updating permissions

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

  1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create. ๐Ÿ‘ Creating a new PAT
  4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Snowflake data from PostgreSQL.

Build the TDS Foreign Data Wrapper

The Foreign Data Wrapper can be installed as an extension to PostgreSQL, without recompiling PostgreSQL. The tds_fdw extension is used as an example (https://github.com/tds-fdw/tds_fdw).

  1. You can clone and build the git repository via something like the following view source:
    sudo apt-get install git
    git clone https://github.com/tds-fdw/tds_fdw.git
    cd tds_fdw
    make USE_PGXS=1
    sudo make USE_PGXS=1 install
    
    Note: If you have several PostgreSQL versions and you do not want to build for the default one, first locate where the binary for pg_config is, take note of the full path, and then append PG_CONFIG=
  2. After you finish the installation, then start the server:
    sudo service postgresql start
    
  3. Then go inside the Postgres database
    psql -h localhost -U postgres -d postgres
    
    Note: Instead of localhost you can put the IP where your PostgreSQL is hosted.

Connect to Snowflake data as a PostgreSQL Database and query the data!

After you have installed the extension, follow the steps below to start executing queries to Snowflake data:

  1. Log into your database.
  2. Load the extension for the database:
    CREATE EXTENSION tds_fdw;
    
  3. Create a server object for Snowflake data:
    CREATE SERVER "Snowflake1" FOREIGN DATA WRAPPER tds_fdw OPTIONS (servername'tds.cdata.com', port '14333', database 'Snowflake1');
    
  4. Configure user mapping with your email and Personal Access Token from your Connect AI account:
    CREATE USER MAPPING for postgres SERVER "Snowflake1" OPTIONS (username '[email protected]', password 'your_personal_access_token' );
    
  5. Create the local schema:
    CREATE SCHEMA "Snowflake1";
    
  6. Create a foreign table in your local database:
    #Using a table_name definition:
    
    CREATE FOREIGN TABLE "Snowflake1".Products ( 
    id varchar, 
    ProductName varchar) 
    SERVER "Snowflake1"
    OPTIONS(table_name 'Snowflake.Products', row_estimate_method 'showplan_all');
    
    #Or using a schema_name and table_name definition:
    
    CREATE FOREIGN TABLE "Snowflake1".Products ( 
    id varchar, 
    ProductName varchar) 
    SERVER "Snowflake1"
    OPTIONS (schema_name 'Snowflake', table_name 'Products', row_estimate_method 'showplan_all');
    
    #Or using a query definition:
    
    CREATE FOREIGN TABLE "Snowflake1".Products (
    id varchar, 
    ProductName varchar) 
    SERVER "Snowflake1"
    OPTIONS (query 'SELECT * FROM Snowflake.Products', row_estimate_method 'showplan_all');
    
    #Or setting a remote column name:
    
    CREATE FOREIGN TABLE "Snowflake1".Products (
    id varchar,
    col2 varchar OPTIONS (column_name 'ProductName'))
    SERVER "Snowflake1"
    OPTIONS (schema_name 'Snowflake', table_name 'Products', row_estimate_method 'showplan_all');
    
  7. You can now execute read/write commands to Snowflake:
    SELECT id, ProductName
    FROM "Snowflake1".Products;
    

More Information & Free Trial

Now, you have created a simple query from live Snowflake data. For more information on connecting to Snowflake (and more than 200 other data sources), visit the Connect AI page. Sign up for a free trial and start working with live Snowflake data in PostgreSQL.