How to Seamlessly Import Azure Data Lake Storage 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 Azure Data Lake Storage data data to SPSS Modeler via the CData ODBC Driver for Azure Data Lake Storage, 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 Azure Data Lake Storage data, enabling seamless data import, preparation, and analysis. With the CData ODBC Driver for Azure Data Lake Storage, you can unlock the full potential of your Azure Data Lake Storage data data within IBM SPSS Modeler for actionable insights.

Overview

Here is an overview of the steps:

1. CONFIGURE THE ODBC DRIVER: Set up a connection to Azure Data Lake Storage data in the CData ODBC Driver for Azure Data Lake Storage 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 Azure Data Lake Storage data data into SPSS Modeler, then review, filter, transform, and prepare the data for predictive analytics and statistical modeling.

Configure the Azure Data Lake Storage DSN Using the CData ODBC Driver

To start, configure the DSN (Data Source Name) for Azure Data Lake Storage 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 Azure Data Lake Storage data Source and enter the required values to establish a connection:

Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Azure AD for authentication.

For this, an Active Directory web application is required. You can create one as follows:

1. Sign in to your Azure Account through the .
2. Select "Azure Active Directory".
3. Select "App registrations".
4. Select "New application registration".
5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen1.
• Account: Set this to the name of the account.
• OAuthClientId: Set this to the application Id of the app you created.
• OAuthClientSecret: Set this to the key generated for the app you created.
• TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

• Schema: Set this to ADLSGen2.
• Account: Set this to the name of the account.
• FileSystem: Set this to the file system which will be used for this account.
• AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
• Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

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 Azure Data Lake Storage 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 Azure Data Lake Storage 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 Azure Data Lake Storage Data with CData

With the CData ODBC Driver for Azure Data Lake Storage, connecting Azure Data Lake Storage 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.