Set up and connect Snowflake

Introduction

For the Snowflake project in the getting started guide, you'll learn how to set up Snowflake and connect it to dbt Cloud.

This guide will walk you through:

• Setting up a Snowflake trial account
• Loading training data into your Snowflake account
• Creating a dbt Cloud account, either through Partner Connect or through the account flow
• Connecting dbt Cloud and Snowflake
• Setting up the dbt Cloud IDE, querying data, and doing your first dbt run

Prerequisites

The only prerequisites for this guide are to have access to an email account for signing up for Snowflake and dbt Cloud.

Setting up

You can start by signing up for a free trial on Snowflake:

1. Sign up for a free trial by following this link and completing the sign-up form.

2. Select the Enterprise edition, choose a cloud provider and region, and agree to the terms of service.

   You should consider organizational questions when choosing a cloud provider for a full implementation. For more information, see Introduction to Cloud Platforms in the Snowflake docs. For the purposes of this setup, all cloud providers and regions will work so choose whichever you’d like.

3. Click GET STARTED.

   Snowflake Account Configuration

4. After submitting the sign-up form, you should receive an email asking you to activate your account. Click the link in the email and a new tab will open up where you’ll create your username and password. Complete the form and click Get started.

   Snowflake Account Signup

5. Congrats! Your workspace is ready for some data. Feel free to check out any of the getting started tooltips that Snowflake provides in the UI to familiarize yourself before moving on to the next section.

   Snowflake Workspace

Loading data

Now we’re ready for some sample data. The data used here is stored as CSV files in a public S3 bucket and the following steps will guide you through how to prepare your Snowflake account for that data and upload it.

1. If using the new Snowflake UI, create a new worksheet by clicking the "+ Worksheet" button in the upper right hand corner of the screen.

Snowflake New UI - Create New Worksheet

2. Run the following commands to create a new virtual warehouse, two new databases (one for raw data, the other for future dbt development), and two new schemas (one for jaffle_shop data, the other for 'stripe' data). If you're curious to learn more about the naming conventions used, check out this article. Feel free to copy/paste from below:

create warehouse transforming;
create database raw;
create database analytics;
create schema raw.jaffle_shop;
create schema raw.stripe;

Snowflake - Create Warehouse, Database, and Schema Commands

3. Our next step will focus on creating three raw tables in the raw database and jaffle_shop and stripe schemas. Execute the tabbed code snippets below to create the customers, orders, and payment table and load the respective data.

   customers

   ​​create table raw.jaffle_shop.customers 
   ( id integer,
     first_name varchar,
     last_name varchar
   );
   
   copy into raw.jaffle_shop.customers (id, first_name, last_name)
   from 's3://dbt-tutorial-public/jaffle_shop_customers.csv'
   file_format = (
     type = 'CSV'
     field_delimiter = ','
     skip_header = 1
     );

   orders

   create table raw.jaffle_shop.orders
   ( id integer,
     user_id integer,
     order_date date,
     status varchar,
     _etl_loaded_at timestamp default current_timestamp
   );
   
   copy into raw.jaffle_shop.orders (id, user_id, order_date, status)
   from 's3://dbt-tutorial-public/jaffle_shop_orders.csv'
   file_format = (
     type = 'CSV'
     field_delimiter = ','
     skip_header = 1
     );

   payment

   create table raw.stripe.payment 
   ( id integer,
     orderid integer,
     paymentmethod varchar,
     status varchar,
     amount integer,
     created date,
     _batched_at timestamp default current_timestamp
   );
   
   copy into raw.stripe.payment (id, orderid, paymentmethod, status, amount, created)
   from 's3://dbt-tutorial-public/stripe_payments.csv'
   file_format = (
     type = 'CSV'
     field_delimiter = ','
     skip_header = 1
     );

Snowflake - Create Customers Table

Snowflake - Create Orders Table

Snowflake - Create Payments Table

6. Great! Your data is loaded and ready to go. Just to make sure, run the following commands to query your data and confirm that you see an output for each one.

select * from raw.jaffle_shop.customers;
select * from raw.jaffle_shop.orders;
select * from raw.stripe.payment;

Now we’re ready to set up dbt Cloud!

Connecting to dbt Cloud

There are two ways to connect dbt Cloud and Snowflake. The first option is Partner Connect, which provides a streamlined setup to create your dbt Cloud account from within your new Snowflake trial account. The second option is to create your dbt Cloud account separately and build the Snowflake connection yourself. If you are looking to get started quickly, we recommend option 1. If you are looking to customize your setup from the very beginning and gain familiarity with the dbt Cloud setup flow, we recommend option 2.

Option 1: Connect dbt Cloud and Snowflake with partner connect

1. With your Snowflake account up and running with data, we’re ready to connect it with dbt Cloud. We’re going to use Snowflake Partner Connect to set up your dbt Cloud account and project. Using Partner Connect will allow you to create a complete dbt account with your Snowflake connection, a managed repository, environments, and credentials.
2. There’s a couple of ways you can access the Partner Connect page depending on if you’re navigating in the classic Snowflake UI or the new UI.

• Snowflake Classic UI: If you’re using the classic version of the Snowflake UI, you can click the Partner Connect button in the top bar of your account. From there, click on the dbt tile to open up the connect box.

Snowflake Classic UI - Partner Connect

• Snowflake New UI: If you’re using the new web interface, click on the home icon in the upper left-hand corner and then click on Admin title, and then Partner Connect. You can scroll down to find the dbt tile, or search for dbt in the search bar and it will float to the top. Click on the tile to open up the connect box.

Snowflake Partner Connect Box

3. Once you’ve clicked on the tile, a connection box will appear that will look slightly different depending on the route you took above, but will contain the same Optional Grant box towards the bottom. In both cases, you’ll want to type in or select the RAW and ANALYTICS databases. This will grant access for your new dbt user role to each database.

Snowflake Classic UI - Connection Box

Snowflake New UI - Connection Box

4. After you’ve entered the database names using either option above, click "Connect". You should see a pop up window similar to the one of the options below. Click Activate.

Snowflake Classic UI - Actviation Window

Snowflake New UI - Activation Window

5. A new tab will be created that will take you to the dbt Cloud website. Here you’ll be asked to create an account name with password, as well as agree to the Terms of Service. Once that’s done, click Complete Registration.

dbt Cloud - Account Info

6. We have one slight tweak to make to the dbt Cloud interface to account for the analytics database and transforming warehouse created earlier. Click the gear icon in the upper right and select Account Settings. Choose the "Partner Connection Trial" project and select snowflake in the overview table. Select edit and update the fields database and warehouse to be analytics and transforming, respectively.

dbt Cloud - Snowflake Project Overview

dbt Cloud - Update Database and Warehouse

7. Great! Your dbt Cloud account is now completely setup and connected to your Snowflake trial account with a managed repository. You can skip to the Initialize your repo and start development section to get started in the IDE.

Option 2: Connect dbt Cloud and Snowflake manually

Create a dbt Cloud account

Let's start this section by creating a dbt Cloud account if you haven't already.

1. Navigate to dbt Cloud.
2. If you don't have a dbt Cloud account, create a new one, and verify your account via email.
3. If you already have a dbt Cloud account, you can create a new project from your existing account:
   1. Click the gear icon in the top-right, then click Projects.
   2. Click + New Project.
4. You've arrived at the "Setup a New Project" page.
5. Type "Analytics" in the dbt Project Name field. You will be able to rename this project later.
6. Click Continue.

Connect dbt Cloud to Snowflake

Now let's formally set up the connection between dbt Cloud and Snowflake.

1. Choose Snowflake to setup your connection.

   dbt Cloud - Choose Snowflake Connection

2. For the name, write Snowflake or another simple title.

3. Enter the following information under Snowflake settings.

   • Account: Find your account by using the Snowflake trial account URL and removing snowflakecomputing.com. The order of your account information will vary by Snowflake version. For example, Snowflake's Classic console URL might look like: oq65696.west-us-2.azure.snowflakecomputing.com. The AppUI or Snowsight URL might look more like: snowflakecomputing.com/west-us-2.azure/oq65696. In both examples, your account will be: oq65696.west-us-2.azure. For more information, see "Account Identifiers" in the Snowflake documentation.

       ✅ db5261993 or db5261993.east-us-2.azure
       ❌ db5261993.eu-central-1.snowflakecomputing.com

     
     
   • Role: Leave blank for now. You can update this to a default Snowflake role in the future.
   • Database: analytics. This tells dbt to create new models in the analytics database.
   • Warehouse: transforming. This tells dbt to use the transforming warehouse we created earlier.
     dbt Cloud - Snowflake Account Settings

4. Enter the following information under Development credentials.

   • Username: The username you created for Snowflake. Note: The username is not your email address and is usually your first and last name together in one word.
   • Password: The password you set when creating your Snowflake account
   • Schema: You’ll notice that the schema name has been auto created for you. By convention, this is dbt_<first-initial><last-name>. This is the schema connected directly to your development environment, and it's where your models will be built when running dbt within the Cloud IDE.
   • Target name: leave as default
   • Threads: Leave as 4. This is the number of simultaneous connects that dbt Cloud will make to build models concurrently.
     dbt Cloud - Snowflake Development Credentials

5. Click Test Connection at the bottom. This verifies that dbt Cloud can access your Snowflake account.

6. If the connection test succeeds, click Next. If it fails, you may need to check your Snowflake settings and credentials.

Initialize your repository and start development

When you develop in dbt Cloud, you can leverage Git to version control your code.

To connect to a repository, you can either set up a dbt Cloud-hosted managed repository or directly connect to a supported git provider. Managed repositories are a great way to trial dbt without needing to create a new repository. In the long run, it's better to connect to a supported git provider to use features like automation and continuous integration.

If you used Partner Connect, you can skip over to initializing your dbt project as the Partner Connect sets you up with a managed repository already. If not, you will need to create your repository connection.

Setting up a managed repository

To set up a managed repository:

1. Under "Add repository from", select Managed.
2. Type a name for your repo such as bbaggins-dbt-quickstart
3. Click Create. It will take a few seconds for your repository to be created and imported.
4. Once you see the "Successfully imported repository," click Continue.

Initialize your dbt project

Now that you have a repository configured, you can initialize your project and start development in dbt Cloud:

1. Click Develop from the upper left. It might take a few minutes for your project to spin up for the first time as it establishes your git connection, clones your repo, and tests the connection to the warehouse.
2. Above the file tree to the left, click Initialize your project. This builds out your folder structure with example models.
3. Make your initial commit by clicking Commit. Use the commit message initial commit. This creates the first commit to your managed repo and allows you to open a branch where you can add new dbt code.
4. Now you should be able to directly query data from your warehouse and execute dbt run. Paste your following warehouse-specific code in the IDE:

BigQuery

select * from `dbt-tutorial.jaffle_shop.customers`

Databricks

select * from default.jaffle_shop_customers

Redshift

select * from jaffle_shop.customers

Snowflake

select * from raw.jaffle_shop.customers

• In the command line bar at the bottom, type dbt run and click Enter. We will explore what happens in the next section of the tutorial.

Congratulations! You have successfully completed the following:

• Set up a new Snowflake instance
• Loaded training data into your Snowflake account
• Connected dbt Cloud and Snowflake

Next steps

You can continue the tutorial by building your first project. You will learn and apply the fundamentals of dbt with models, tests, docs, and deploying your first job!