Transform Data

dbt and Snowflake with dbt Power User and SQLFluff

Last Updated:

June 13, 2023

June 13, 2023

Author:

Noel Gomez

Using dbt with Snowflake is one of the most popular and powerful data stacks today. Are you facing one of these situations:

- You just joined a data team using dbt Core with Snowflake, and you want to set up your local dbt development environment

- Your company is already using Snowflake and wants to try out dbt.

- You are using dbt with another data warehouse and you want to try dbt with Snowflake before migrating.

If you are facing any of these, this guide will help you set up your dbt environment for Snowflake using VS Code as your dbt development environment. We will also cover useful python libraries and VS Code extensions that will make you more productive. Want to become a dbt on Snowflake ninja, keep reading.

Pre-requisites for dbt development on Snowflake

To get started, you need to set up your dbt development environment. This includes Python, dbt, and VS Code. You will also need to set up your Snowflake account properly so dbt can work its magic.

While dbt supports versions of Python greater than 3.7, some other tools like Snowpark require Python 3.8, therefore we recommend you stick with that version. You can find the installer for your particular Operating System on this page. Finding the right Windows Installer can be confusing, so look for a link titled Windows installer (64-bit). If you are on a Mac, you can use the Universal Installer macOS 64-bit universal2 installer.

When using dbt, you will also need Git. To setup git, follow this handy guide.

The preferred IDE for dbt is VS Code, you can find it on the official Microsoft site. click the big Download button and install like any other app.

Installing dbt is done using pip. You can find more information on this page.

For dbt Snowflake, simply run:

pip install dbt-snowflake

This will install the latest version of dbt core along with the dbt adapter for Snowflake. If you need to use an older version of dbt, you will need to specify it when you run pip. However, the version of the dbt adapter may not match the version of dbt core.

For example, as of this writing, the last version of dbt core 1.3.x is 1.3.4. However, dbt-snowflake for 1.3.x is 1.3.2.  So, if you want to install the latest dbt 1.3.x for snowflake, you would run

pip install dbt-snowflake version

This will install dbt-snowflake 1.3.2 along with dbt-core 1.3.4

Configure Snowflake permissions

The final pre-requisite you will need to do is set up a Snowflake user that has been granted a role with the right access to a database where dbt will create views and tables. The role should also have access to a Snowflake Warehouse for compute. Here is a handy guide that gives you the basics. We would recommend a more comprehensive setup for a production deployment, but this will get you going for now.

The key items that are not covered in that guide is that you should create a role ANALYST and a database ANALYTICS_DEV and grant OWNERSHIP of that database to the ANALYST role. The ANALYST role should also be granted USAGE on the TRANSFORMING warehouse.  You also need to grant the ANALYST role to your specific user. Don’t run as ACCOUNTADMIN.  

This is all needed because when dbt runs it will create a schema for your user in the ANALYTICS_DEV database and you will use the TRANSFORMING warehouse when compute is needed like when dbt creates tables.

If all of this seems confusing or tedious, you should consider a managed dbt solution like dbt Cloud or Datacoves.

Now with all the pre-requisites out of the way, let’s configure dbt to connect with Snowflake.

Configure your dbt Snowflake profile

To initialize your dbt project in Snowflake, dbt has a handy command dbt init. You can configure your dbt Snowflake profile using the dbt init command both for a new and a existing dbt project. First you will need to clone your repo using git. Then, simply run the dbt-init command and go through the prompts.  

Once you get your project set up, consider adding a profile_template.yml to your project. As stated on that page, using a profiles template will simplify the dbt init process for users on your team.

dbt init

To make sure dbt can connect to Snowflake, run dbt debug. If dbt can connect to your Snowflake account, you should see “All checks passed!” If you have problems, then join the dbt Community search the forums or ask a question in the #db-snowflake channel.

dbt debug used to check dbt to Snowflake connection

Even though dbt performed the setup of your profile.yml to connect to Snowflake with your credentials, it only provides the basic setup. This page provides additional parameters. that you can configure for the Snowflake connection in your profiles.yml file.  

If you want to configure those parameters, you will need to open and edit the profiles.yml file. The profiles.yml file created by dbt init will be in your user’s home directory in a subdirectory called .dbt.

One handy configuration parameter to change is reuse_connections. Also, if you use SSO authentication with external browser, you should consider setting up connection caching on Snowflake, otherwise you will be prompted to authenticate for every connection dbt opens to the database.

Now that you have set up your dbt connection to Snowflake, there are some other options you can configure when dbt runs against your Snowflake account. This includes overriding the default warehouse for a specific model, adding query tags, copying grants, etc. This handy page has a lot more information on these dbt snowflake advanced configurations.

Improve your dbt Snowflake experience with SQLFluff and other Python libraries

Now that you have dbt connecting to your database, let’s talk about some python libraries you should set up to improve how you work with dbt.

dbt-coves

dbt-coves is an open source library created by Datacoves to complement dbt development by simplifying tedious tasks like generating staging models. It is a must-have tool for any dbt practitioner who wants to improve their efficiency. dbt-coves will automatically create your source.yml and staging models as well as their corresponding yml(property) files. It also has utilities for backing up Airbyte and Fivetran configurations.

SQLFluff

SQLFluff is a Python library for linting SQL code. SQLFluff seamlessly integrates dbt using a templater and it is the only linter compatible with dbt. If you have not heard of code linting it helps you enforce rules on how your SQL code is formatted for example, should everyone use leading or trailing commas, should SQL keywords be upper or lower case. We recommend everyone use a linter as this will improve code readability and long term maintainability.  

pre-commit with dbt-checkpoint

dbt-checkpoint is a tool that allows you to make sure your dbt project complies with certain governance rules. For example, you can have a rule that validates whether every dbt model has a description. You can also ensure that every column is documented among many other rules. We also recommend the use of dbt-checkpoint as it will assure developers don’t add technical debt from the start of a project.

In addition to these Python libraries, at Datacoves we set up the development environment with other handy libraries like Snowpark and Streamlit. We believe that flexibility is important especially in enterprise environments. If you want to learn what to consider when selecting a managed dbt core platform, check out our guide.

Improve your dbt Snowflake experience with dbt power user and other VS Code extensions

In addition to Python libraries, you can improve your dbt workflow with Snowflake by installing these VS Code extensions.

Snowflake VS Code Extension

The official Snowflake dbt extension keeps you in the flow by bringing the Snowflake interface to VS Code. With it you can explore your database, run queries, and even upload and download files to Snowflake stages. It is a must-have for any Snowflake user.

dbt power user

dbt power user is a VS Code extension that improves the dbt development experience by adding handy shortcuts to run dbt models, tests, and even let’s you preview the result of a dbt model or a CTE within that model.  

SQLFluff VS Code extension

The SQLFluff VS Code extension is the companion to the SQLFluff python library. It improves the development experience by highlighting linting errors right in line with your SQL code. It even has a handy hover which describes the linting error and links to the SQLFluff documentation.

SQLFluff Linting Error hover on VS Code

There are many other great VS Code extensions and at Datacoves we are always improving the dbt developer’s experience by pre-installing them. One recent addition demonstrated on the video below is a ChatGPT extension that allows you to improve the dbt experience by writing documentation in addition to other functions.

Conclusion

Getting started with dbt and Snowflake is not hard and knowing how to streamline the development experience when working with dbt and Snowflake will maximize developer happiness.

Some users may run into issues configuring their development environment. If that happens, check out the #sqlfluff, #tools-dbt-libraries, and #tools-vscode channels on the dbt Slack community. There are many helpful people there always ready to help.

As you can see there are a lot of steps and potential gotchas to get a dbt environment properly configured. This gets more complicated as the number of dbt users increases. Upgrading everyone can also pose a challenge. These reasons and more are why we created the most flexible managed dbt-core environment. If you want your developers to be up and running in minutes with no installation required, reach out and we can show you how we can streamline your teams’ dbt experience with best practices from the start.

Noel Gomez
Linkedin

Datacoves Co-founder | 15+ Data Platform Expert.
Solving enterprise data challenges quickly with dbt & Airflow.

Looking for an enterprise data platform?

Datacoves offers managed dbt core and Airflow and can be deployed in your private cloud.

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