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URL: https://www.cdata.com/kb/tech/snowflake-cloud-databricks.rst

⇱ Connect and Query Live Snowflake Data in Databricks with CData Connect AI

Connect and Query Live Snowflake Data in Databricks with CData Connect AI

👁 Mohsin Turki
Mohsin Turki
Technical Marketing Engineer
Use CData Connect AI to integrate live Snowflake data into Databricks and enable direct, live querying and analysis without replication.

Databricks is a leading AI cloud-native platform that unifies data engineering, machine learning, and analytics at scale. Its powerful data lakehouse architecture combines the performance of data warehouses with the flexibility of data lakes. Integrating Databricks with CData Connect AI gives organizations live, real-time access to Snowflake data without the need for complex ETL pipelines or data duplication—streamlining operations and reducing time-to-insights.

In this article, we'll walk through how to configure a secure, live connection from Databricks to Snowflake using CData Connect AI. Once configured, you'll be able to access Snowflake data directly from Databricks notebooks using standard SQL—enabling unified, real-time analytics across your data ecosystem.

About Snowflake Data Integration

CData simplifies access and integration of live Snowflake data. Our customers leverage CData connectivity to:

  • Reads and write Snowflake data quickly and efficiently.
  • Dynamically obtain metadata for the specified Warehouse, Database, and Schema.
  • Authenticate in a variety of ways, including OAuth, OKTA, Azure AD, Azure Managed Service Identity, PingFederate, private key, and more.

Many CData users use CData solutions to access Snowflake from their preferred tools and applications, and replicate data from their disparate systems into Snowflake for comprehensive warehousing and analytics.

For more information on integrating Snowflake with CData solutions, refer to our blog: https://www.cdata.com/blog/snowflake-integrations.


Getting Started


Overview

Here is an overview of the simple steps:

  1. Step 1 — Connect and Configure: In CData Connect AI, create a connection to your Snowflake source, configure user permissions, and generate a Personal Access Token (PAT).
  2. Step 2 — Query from Databricks: Install the CData JDBC driver in Databricks, configure your notebook with the connection details, and run SQL queries to access live Snowflake data.

Prerequisites

Before you begin, make sure you have the following:

  1. An active Snowflake account.
  2. A CData Connect AI account. You can log in or sign up for a free trial here.
  3. A Databricks account. Sign up or log in here.

Step 1: Connect and Configure a Snowflake Connection in CData Connect AI

1.1 Add a Connection to Snowflake

CData Connect AI uses a straightforward, point-and-click interface to connect to available data sources.

  1. Log into Connect AI, click Sources on the left, and then click Add Connection in the top-right.
    2. 👁 Adding a Connection in CData Connect AI
  2. Select "Snowflake" from the Add Connection panel.
    3. 👁 Selecting a data source
  3. Enter the necessary authentication properties to connect to Snowflake.

    To connect to Snowflake:

    1. Set User and Password to your Snowflake credentials and set the AuthScheme property to PASSWORD or OKTA.
    2. Set URL to the URL of the Snowflake instance (i.e.: https://myaccount.snowflakecomputing.com).
    3. Set Warehouse to the Snowflake warehouse.
    4. (Optional) Set Account to your Snowflake account if your URL does not conform to the format above.
    5. (Optional) Set Database and Schema to restrict the tables and views exposed.
    6. (Optional) If MFA is enabled on your Snowflake account (via Duo Security), set MFACode to the passcode generated by your Duo authenticator app.

    See the Getting Started guide in the CData driver documentation for more information.

    👁 Configuring a connection (Salesforce is shown)
  4. Click Save & Test in the top-right.
  5. Navigate to the Permissions tab on the Snowflake Connection page and update the user-based permissions based on your preferences. 👁 Updating permissions

1.2 Generate a Personal Access Token (PAT)

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. PAT functions as an alternative to your login credentials for secure, token-based authentication. It is a best practice to create a separate PAT for each service to maintain granularity of access.

  1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
  2. On the Settings page, go to the Access Tokens section and click Create PAT.
  3. Give the PAT a name and click Create. 👁 Creating a new PAT
  4. Note: The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

Step 2: Connect and Query Snowflake Data in Databricks

Follow these steps to establish a connection from Databricks to Snowflake. You'll install the CData JDBC Driver for Connect AI, add the JAR file to your cluster, configure your notebooks, and run SQL queries to access live Snowflake data data.

2.1 Install the CData JDBC Driver for Connect AI

  1. In CData Connect AI, click the Integrations page on the left. Search for JDBC or Databricks, click Download, and select the installer for your operating system.
  2. Once downloaded, run the installer and follow the instructions:
    • For Windows: Run the setup file and follow the installation wizard.
    • For Mac/Linux: Unpack the archive and move the folder to /opt or /Applications. Make sure you have execute permissions.
  3. After installation, locate the JAR file in the installation directory:
    • Windows: 
      C:\Program Files\CData\CData JDBC Driver for Connect AI\lib\cdata.jdbc.connect.jar
    • Mac/Linux: 
      /Applications/CData/CData JDBC Driver for Connect AI/lib/cdata.jdbc.connect.jar

2.2 Install the JAR File on Databricks

  1. Log in to Databricks. In the navigation pane, click Compute on the left. Start or create a compute cluster. 👁 Launching a compute cluster in Databricks
  2. Click on the running cluster, go to the Libraries tab, and click Install New at the top right. 👁 Accessing the Libraries tab in Databricks
  3. In the Install Library dialog, select DBFS, and drag and drop the cdata.jdbc.connect.jar file. Click Install. 👁 Uploading the JDBC driver JAR to DBFS

2.3 Query Snowflake Data in a Databricks Notebook

Notebook Script 1 — Define JDBC Connection:

  1. Paste the following script into the notebook cell:
driver = "cdata.jdbc.connect.ConnectDriver"
url = "jdbc:connect:AuthScheme=Basic;User=your_username;Password=your_pat;URL=https://cloud.cdata.com/api/;DefaultCatalog=Your_Connection_Name;"
  1. Replace:
    • your_username - With your CData Connect AI username
    • your_pat - With your CData Connect AI Personal Access Token (PAT)
    • Your_Connection_Name - With the name of your Connect AI data source, from the Sources page
  2. Run the script.

Notebook Script 2 — Load DataFrame from Snowflake data:

  1. Add a new cell for this second script. From the menu on the right side of your notebook, click Add cell below.
  2. Paste the following script into the new cell:
remote_table = spark.read.format("jdbc") \
 .option("driver", "cdata.jdbc.connect.ConnectDriver") \
 .option("url", "jdbc:connect:AuthScheme=Basic;User=your_username;Password=your_pat;URL=https://cloud.cdata.com/api/;DefaultCatalog=Your_Connection_Name;") \
 .option("dbtable", "YOUR_SCHEMA.YOUR_TABLE") \
 .load()
  1. Replace:
    • your_username - With your CData Connect AI username
    • your_pat - With your CData Connect AI Personal Access Token (PAT)
    • Your_Connection_Name - With the name of your Connect AI data source, from the Sources page
    • YOUR_SCHEMA.YOUR_TABLE - With your schema and table, for example, Snowflake.Products
  2. Run the script.

Notebook Script 3 — Preview Columns:

  1. Similarly, add a new cell for this third script.
  2. Paste the following script into the new cell:
display(remote_table.select("ColumnName1", "ColumnName2"))
  1. Replace ColumnName1 and ColumnName2 with the actual columns from your Snowflake structure (e.g. Id, ProductName, etc.).
  2. Run the script.
👁 Previewing Snowflake data data in Databricks notebook

You can now explore, join, and analyze live Snowflake data directly within Databricks notebooks—without needing to know the complexities of the back-end API and without replicating Snowflake data.

Try CData Connect AI Free for 14 Days

Ready to simplify real-time access to Snowflake data? Start your free 14-day trial of CData Connect AI today and experience seamless, live connectivity from Databricks to Snowflake.

Low code, zero infrastructure, zero replication — just seamless, secure access to your most critical data and insights.