Connect and Query Live API Data in Databricks with CData Connect Cloud

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 Cloud gives organizations live, real-time access to API 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 API using CData Connect Cloud. Once configured, you'll be able to access API data directly from Databricks notebooks using standard SQL—enabling unified, real-time analytics across your data ecosystem.

Overview

Here is an overview of the simple steps:

1. Step 1 — Connect and Configure: In CData Connect Cloud, create a connection to your API 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 API data.

Prerequisites

Before you begin, make sure you have the following:

1. An active API account.
2. A CData Connect Cloud 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 API Connection in CData Connect Cloud

1.1 Add a Connection to your API

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

1. Log into Connect Cloud, click Sources on the left, and then click Add Connection in the top-right.

2. Select "API" from the Add Connection panel.

3. Enter the necessary authentication properties to connect to your API.

   To connect to your API, configure the following properties on the Global Settings page:

   • In Authentication, select the Type and fill in the required properties
   • In Headers, add the required HTTP headers for your API
   • In Pagination, select the Type and fill in the required properties

   After the configuring the global settings, navigate to the Tables to add tables. For each table you wish to add:

   1. Click "+ Add"
   2. Set the Name for the table
   3. Set Request URL to the API endpoint you wish to work with
   4. (Optional) In Parameters, add the required URL Parameters for your API endpoint
   5. (Optional) In Headers, add the required HTTP headers for the API endpoint
   6. In Table Data click " Configure"
   7. Review the response from the API and click "Next"
   8. Select which element to use as the Repeated Elements and which elements to use as Columns and click "Next"
   9. Preview the tabular model of the API response and click "Confirm"
4. Click Save & Test in the top-right.
5. Navigate to the Permissions tab on the your API Connection page and update the user-based permissions based on your preferences.

1.2 Generate a Personal Access Token (PAT)

When connecting to Connect Cloud 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 Cloud. 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 Cloud 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.
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 your API Data in Databricks

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

2.1 Install the CData JDBC Driver for Connect Cloud

1. In CData Connect Cloud, 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 Cloud\lib\cdata.jdbc.connect.jar

   • Mac/Linux:

     /Applications/CData/CData JDBC Driver for Connect Cloud/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.
2. Click on the running cluster, go to the Libraries tab, and click Install New at the top right.
3. In the Install Library dialog, select DBFS, and drag and drop the cdata.jdbc.connect.jar file. Click Install.

2.3 Query your API 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;"

2. Replace:
   • your_username - With your CData Connect Cloud username
   • your_pat - With your CData Connect Cloud Personal Access Token (PAT)
   • Your_Connection_Name - With the name of your Connect Cloud data source, from the Sources page
3. Run the script.

Notebook Script 2 — Load DataFrame from API 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()

3. Replace:
   • your_username - With your CData Connect Cloud username
   • your_pat - With your CData Connect Cloud Personal Access Token (PAT)
   • Your_Connection_Name - With the name of your Connect Cloud data source, from the Sources page
   • YOUR_SCHEMA.YOUR_TABLE - With your schema and table, for example, API.posts
4. 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"))

3. Replace ColumnName1 and ColumnName2 with the actual columns from your your API structure (e.g. title, body, etc.).
4. Run the script.

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

Try CData Connect Cloud Free for 14 Days

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

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