Data Build Tool (DBT) Integration With Snowflake

  • Converting tables to views — It is sufficient to change the materialization in a single config file to change a table to a view.
  • Stored Procedure — The stored procedures created in dbt are shown in the models, which could be accessed and modified.
  • Combining transformation logic — DBT groups similar transformation logic together using dbt tags.
    Version Control — dbt supports version control by integrating with GitHub.
  • Open-source community — Could enhance the development by sharing experiences from fellow developers rather than starting from scratch.
  • Account — The Snowflake account to connect to. If the URL for the Snowflake account is like abc12345.east-us-2.azure.snowflakecomputing.com, then the account name should be abc12345.east-us-2.azure.
  • Role — Role to be used after connecting to Snowflake. (Optional field)
  • Database — Establish a connection with this logical database in the data warehouse to run queries.
  • Warehouse — The virtual warehouse to use for running queries.
  • Auth method –
  1. Username / Password — Enter the Snowflake username (specifically, the login_name) and the corresponding user’s Snowflake password, which will be used to authenticate dbt Cloud to run queries in Snowflake on behalf of a Snowflake user.
  2. Key Pair — The Keypair auth method is based on Snowflake’s Key Pair Authentication to authenticate the credentials when accessed from a dbt Cloud project. After generating an encrypted key pair, rsa_public_key should be set for the Snowflake user for the authentication process.

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