How to Seamlessly Import Databricks Data into IBM SPSS Modeler

IBM SPSS Modeler is a powerful data mining and predictive analytics platform that enables organizations to extract valuable insights from their data. By connecting Databricks data data to SPSS Modeler via the CData ODBC Driver for Databricks, you can leverage real-time access for advanced data mining, predictive modeling, and statistical analysis.

This guide takes you through the steps of connecting IBM SPSS Modeler to Databricks data, enabling seamless data import, preparation, and analysis. With the CData ODBC Driver for Databricks, you can unlock the full potential of your Databricks data data within IBM SPSS Modeler for actionable insights.

Getting Started

---

Overview

Here is an overview of the steps:

1. CONFIGURE THE ODBC DRIVER: Set up a connection to Databricks data in the CData ODBC Driver for Databricks by entering the required connection properties.
2. SET UP ODBC CONNECTION IN SPSS MODELER: Establish the ODBC connection within IBM SPSS Modeler by selecting the configured DSN.
3. IMPORT AND PROCESS DATA: Import the Databricks data data into SPSS Modeler, then review, filter, transform, and prepare the data for predictive analytics and statistical modeling.

Configure the Databricks DSN Using the CData ODBC Driver

To start, configure the DSN (Data Source Name) for Databricks data in your system using the CData ODBC Driver. Download and install a 30-day free trial with all the features from here.

Once installed, launch the ODBC Data Source Administrator:

• On Windows: Search for ODBC Data Source Administrator in the Start menu and open the application.
• On Mac: Open Applications, go to Utilities, and select ODBC Manager.
• On Linux: Use the command line to launch ODBC Data Source Administrator or use unixODBC if installed.

Once launched, double-click on the CData Databricks data Source and enter the required values to establish a connection:

To connect to a Databricks cluster, set the properties as described below.

Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

• Server: Set to the Server Hostname of your Databricks cluster.
• HTTPPath: Set to the HTTP Path of your Databricks cluster.
• Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

Setup an ODBC Connection in IBM SPSS Modeler

After configuring the DSN, it's time to connect to it in IBM SPSS Modeler:

• Launch IBM SPSS Modeler, log in, and create a new stream.
• From the Sources palette, locate the Database node, and drag it onto the canvas.
• Double-click the Database node to open the configuration dialog. Select , browse to select the configured DSN, then click OK.
• In the Database dialog, browse to select the table(s) you’d like to import, preview the data, and click OK to finalize.

You are now ready to process and analyze the Databricks data data in IBM SPSS Modeler.

Process Data: Filter, Categories, and Model

Once the tables are imported, you can refine, filter, categorize, and model your Databricks data data in SPSS Modeler:

• Filtering: Double-click your Database connection, go to the Filter section, and select/deselect fields to focus on relevant data. This improves processing speed and model accuracy.
• Set Data Types and Roles: Categorize your fields and assign roles to each data type by navigating to the Types section.
• Perform a Basic Analysis: Drag and drop the Analysis node next to your Database node, connect them, and click the Play button to run the stream and analyze the data.

You have now performed a simple analysis, enabling SPSS Modeler to process and display insights from your database.

Unlock the Potential of Your Databricks Data with CData

With the CData ODBC Driver for Databricks, connecting Databricks data data to IBM SPSS Modeler is seamless. Start your free trial today and unlock the full potential of your real-time data for advanced analytics and decision-making.