This documentation describes a release under development. Documentation for the latest release, 3.6.2, can be found here.

Snowflake

Snowflake can either be used as a data source or a data destination in Mitto.

Mitto and Snowflake

Source plugin example: Query

Destination plugin examples: CSV, Salesforce, SQL

Snowflake as a data destination

  • Mitto automatically creates the Snowflake database schema if it doesn’t exist

  • Mitto automatically creates the Snowflake database tables if they don’t exist

  • Mitto automatically determines data types for Snowflake columns

  • Mitto automatically adds new columns to Snowflake tables based on new fields in source systems

  • Mitto automatically adjusts Snowflake tables based on changes in source data

Snowflake specific setup

Below is the database url structure for connecting to a Snowflake database:

snowflake://<username>:<password>@<account_name>/<database>?warehouse=<warehouse>

Examples of Snowflake account_name based on cloud platform/region:

  • AWS - US West (Oregon) - xy12345

  • AWS - US East (Ohio) - xy12345.us-east-2.aws

  • GCP - US Central1 (Iowa) - xy12345-us-central1.gcp

  • Azure - West US 2 (Washington) - xy12345.west-us-2.azure

Learn more about Your Snowflake Account Name .

Here’s an example of using a Snowflake database as a destination in a CSV job:

CSV JOB

Note

When outputting to a Snowflake database, leaving the “Schema” blank will create a table in the public schema.

SQL

Mitto can send SQL statements to a Snowflake database. Use Snowflake syntax in these Mitto SQL jobs.

Cheat Sheet

The Zuar team has ample experience working with Snowflake. The team has created this cheat sheet to help you get started: Snowflake Cheat Sheet