Get Started with Hasura DDN and Snowflake

Overview

This tutorial takes about twenty minutes to complete. You'll learn how to:

• Set up a new Hasura DDN project
• Connect it to a Snowflake instance
• Generate Hasura metadata
• Create a build
• Run your first query

Additionally, we'll familiarize you with the steps and workflows necessary to iterate on your API.

This tutorial assumes you're starting from scratch, but you can easily follow the steps if you already have data seeded; Hasura will never modify your source schema.

Prerequisites

Install the DDN CLI

macOS and Linux

Simply run the installer script in your terminal:

curl -L https://graphql-engine-cdn.hasura.io/ddn/cli/v4/get.sh | bash

ARM-based Linux Machines

Currently, the CLI does not support installation on ARM-based Linux systems.

Windows

• Download the latest DDN CLI installer for Windows.
• Run the DDN_CLI_Setup.exe installer file and follow the instructions. This will only take a minute.
• By default, the DDN CLI is installed under C:\Users\{Username}\AppData\Local\Programs\DDN_CLI
• The DDN CLI is added to your %PATH% environment variable so that you can use the ddn command from your terminal.

Install Docker

The Docker based workflow helps you iterate and develop locally without deploying any changes to Hasura DDN, making the development experience faster and your feedback loops shorter. You'll need Docker Compose v2.20 or later.

Validate the installation

You can verify that the DDN CLI is installed correctly by running:

ddn doctor

Tutorial

Step 1. Authenticate your CLI

Before you can create a new Hasura DDN project, you need to authenticate your CLI:

ddn auth login

This will launch a browser window prompting you to log in or sign up for Hasura DDN. After you log in, the CLI will acknowledge your login, giving you access to Hasura Cloud resources.

Step 2. Scaffold out a new local project

Next, create a new local project:

ddn supergraph init my-project && cd my-project

Once you move into this directory, you'll see your project scaffolded out for you. You can view the structure by either running ls in your terminal, or by opening the directory in your preferred editor.

Step 3. Initialize your Snowflake connector

In your project directory, run:

ddn connector init my_snowflake -i

Select hasura/snowflake from the list of connectors.

The CLI will ask you for a connection string in the JDBC URL format, e.g.:

jdbc:snowflake://<account-identifier.<region>.snowflakecomputing.com?user=YOUR_USERNAME&password=YOUR_PASSWORD&db=YOUR_DATABASE&warehouse=YOUR_WAREHOUSE&schema=YOUR_SCHEMA&role=YOUR_ROLE

Generating Snowflake JDBC URLs

From Snowflake's UI, you can click on the account menu in the Snowflake control panel and then select Connect a tool to Snowflake to get a generated JDBC string from the Connector/Drivers tab.

To learn more about Snowflake's conventions for JDBC URLs, see their docs.

Step 4. Create and seed a new Snowflake database

In a warehouse, create a new database called DOCS. Then, on the PUBLIC schema for the DOCS database, create a new table using the standard format via the Snowflake UI:

CREATE TABLE users (
  id INT AUTOINCREMENT START 1 INCREMENT 1 PRIMARY KEY,
  name STRING NOT NULL,
  age INT NOT NULL
);

Next, open a new worksheet on your database's PUBLIC schema and insert some seed data:

INSERT INTO users (name, age) VALUES ('Alice', 25);
INSERT INTO users (name, age) VALUES ('Bob', 30);
INSERT INTO users (name, age) VALUES ('Charlie', 35);

You can verify this worked by using the worksheet to query all records from the users table:

SELECT * FROM users;

Step 5. Introspect your Snowflake database

Next, use the CLI to introspect your Snowflake database:

ddn connector introspect my_snowflake

After running this, you should see a representation of your database's schema in the app/connector/my_snowflake/configuration.json file; you can view this using cat or open the file in your editor.

Additionally, you can check which resources are available — and their status — at any point using the CLI:

ddn connector show-resources my_snowflake

Step 6. Add your model

Now, track the table from your Snowflake database as a model in your DDN metadata:

ddn model add my_snowflake DOCS.PUBLIC.USERS

Open the app/metadata directory and you'll find a newly-generated file: DocsPublicUsers.hml. The DDN CLI will use this Hasura Metadata Language file to represent the users table from Snowflake in your API as a model.

Step 7. Create a new build

To create a local build, run:

ddn supergraph build local

The build is stored as a set of JSON files in engine/build.

Step 8. Start your local services

Start your local Hasura DDN Engine and Snowflake connector:

ddn run docker-start

Your terminal will be taken over by logs for the different services.

Step 9. Run your first query

In a new terminal tab, open your local console:

ddn console --local

In the GraphiQL explorer of the console, write this query:

query {
  docsPublicUsers {
    id
    name
    age
  }
}

You'll get the following response:

{
  "data": {
    "docsPublicUsers": [
      {
        "id": 1,
        "name": "Alice",
        "age": 25
      },
      {
        "id": 2,
        "name": "Bob",
        "age": 30
      },
      {
        "id": 3,
        "name": "Charlie",
        "age": 35
      }
    ]
  }
}

Step 10. Iterate on your Snowflake schema

Add a new table for posts:

CREATE TABLE posts (
  id INT AUTOINCREMENT PRIMARY KEY,
  user_id INT NOT NULL REFERENCES users(id) ON DELETE CASCADE,
  title TEXT NOT NULL,
  content TEXT NOT NULL,
  created_at TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()
);

INSERT INTO posts (user_id, title, content) VALUES
  (1, 'My First Post', 'This is Alice''s first post.'),
  (1, 'Another Post', 'Alice writes again!'),
  (2, 'Bob''s Post', 'Bob shares his thoughts.'),
  (3, 'Hello World', 'Charlie joins the conversation.');

Using the worksheet, query the joined data:

SELECT
  posts.id AS post_id,
  posts.title,
  posts.content,
  posts.created_at,
  users.name AS author
FROM
  posts
JOIN
  users ON posts.user_id = users.id;

You should see a list of posts returned with the author's information joined from the users table

Step 11. Refresh your metadata and rebuild your project

tip

The following steps are necessary each time you make changes to your source schema. This includes, adding, modifying, or dropping tables.

Step 11.1. Re-introspect your data source

Run the introspection command again:

ddn connector introspect my_snowflake

In app/connector/my_snowflake/configuration.json, you'll see schema updated to include operations for the posts table. In app/metadata/my_snowflake.hml, you'll see DOCS.PUBLIC.POSTS present in the metadata as well.

Step 11.2. Update your metadata

Add the posts model:

ddn model add my_snowflake "DOCS.PUBLIC.POSTS"

Step 11.3. Create a new build

Next, create a new build:

ddn supergraph build local

Step 11.4. Restart your services

Bring down the services by pressing CTRL+C and start them back up:

ddn run docker-start

Step 12. Query your new build

Head back to your console and query the posts model:

query GetPosts {
  docsPublicPosts {
    id
    title
    content
  }
}

You'll get a response like this:

{
  "data": {
    "docsPublicPosts": [
      {
        "id": 1,
        "title": "My First Post",
        "content": "This is Alice's first post."
      },
      {
        "id": 2,
        "title": "Another Post",
        "content": "Alice writes again!"
      },
      {
        "id": 3,
        "title": "Bob's Post",
        "content": "Bob shares his thoughts."
      },
      {
        "id": 4,
        "title": "Hello World",
        "content": "Charlie joins the conversation."
      }
    ]
  }
}

Next steps

Congratulations on completing your first Hasura DDN project with Snowflake! 🎉

Here's what you just accomplished:

• You started with a fresh project and connected it to a local Snowflake database.
• You set up metadata to represent your tables, which acts as the blueprint for your API.
• Then, you created a build — essentially compiling everything into a ready-to-use API — and successfully ran your first GraphQL queries to fetch data.
• Along the way, you learned how to iterate on your schema and refresh your metadata to reflect changes.

Now, you're equipped to connect and expose your data, empowering you to iterate and scale with confidence. Great work!

Take a look at our Snowflake docs to learn more about how to use Hasura DDN with Snowflake. Or, if you're ready, get started with adding permissions to control access to your API.