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Snowflake is a data warehouse that offers fast storage, processing, and analytics solutions. Deepnote integrates with Snowflake seamlessly to bring the cloud data warehouse to your notebook.
What can you do with the Snowflake integration?
The Snowflake integration allows you to query databases and explore the structure of your warehouse. Think "SQL editor" but with all the modern productivity boosters that come with Deepnote's notebook. For example:
- Write native SQL and Python in the same notebook
- Search your entire warehouse instantly via the integrated schema explorer
- Get intelligent autocomplete for columns, tables, and databases
- Interactively explore data without writing any additional code
Available on Team and Enterprise plans
How to connect to Snowflake
Deepnote ensures that connections to your data are safe. Please see our security options for configuring access to your Snowflake data.
Using Snowflake with OAuth
Available on the Enterprise plan
With a Snowflake OAuth authentication, you can give every member of your Deepnote workspace their own set of credentials. You can ensure higher security by using short-lived tokens and enabling the use of multi-factor authentication. Follow the principle of least privilege and use granular access control for various Snowflake resources to ensure everyone can only access the data they need.
Deepnote supports three OAuth providers for Snowflake:
Using Snowflake in Deepnote
Now that you're connected to Snowflake, you can do the following:
Click the newly created integration in the Integrations section to open the schema browser. Click here to learn more about the schema browser.
Create an SQL block that points to your warehouse and query your data. Autocomplete for columns, tables, and databases will be at your fingertips as you type. Click here to learn more about SQL blocks.
Explore column distributions as well as sorting and filtering capabilities on the results set. All results are displayed as an interactive pandas DataFrame. Click here to learn about interactive DataFrame output.
Pipe the results of your query into a chart block for rapid interactive data visualization. Click here to learn more about chart blocks.
Deepnote also provides an integration with Snowpark that allows you to write code in the notebook but execute using the warehouse's compute resources.
Connecting to Snowflake via Python
If you prefer to connect to Snowflake with pure Python, you may use the Snowflake connector as shown in the code example below.
import snowflake.connector conn = snowflake.connector.connect( user='USER', password='PASSWORD', account='ACCOUNT', warehouse='WAREHOUSE', database='DATABASE', schema='SCHEMA' ) conn.cursor().execute('select * from products')