Build Pipelines with Live JSON Services in Google Cloud Data Fusion (via CData Connect AI)

Google Cloud Data Fusion simplifies building and managing data pipelines by offering a visual interface to connect, transform, and move data across various sources and destinations, streamlining data integration processes. When combined with CData Connect AI, it provides access to JSON services for building and managing ELT/ETL data pipelines. This article explains how to use CData Connect AI to create a live connection to JSON and how to connect and access live JSON services from the Cloud Data Fusion platform.

Configure JSON Connectivity for Cloud Data Fusion

Connectivity to JSON from Cloud Data Fusion is made possible through CData Connect AI. To work with JSON services from Cloud Data Fusion, we start by creating and configuring a JSON connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "JSON" from the Add Connection panel

3. Enter the necessary authentication properties to connect to JSON.

   See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models JSON APIs as bidirectional database tables and JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

   After setting the URI and providing any authentication values, set DataModel to more closely match the data representation to the structure of your data.

   The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

   • Document (default): Model a top-level, document view of your JSON data. The data provider returns nested elements as aggregates of data.
   • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
   • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

   See the Modeling JSON Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add JSON Connection page and update the User-based permissions.

Add a Personal Access Token

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. It is 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.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to JSON services from Cloud Data Fusion.

Connecting to JSON from Cloud Data Fusion

Follow these steps to establish a connection from Cloud Data Fusion to JSON through the CData Connect AI JDBC driver:

1. Download and install the CData Connect AI JDBC driver:
   1. Open the Integrations page of CData Connect AI.
   2. Search for and select JDBC.
   3. Download and run the setup file.
   4. When the installation is complete, copy the JAR file(cdata.jdbc.connect.jar) from the installation directory (e.g., C:\Program Files\CData\JDBC Driver for CData Connect\lib).
2. Log into Cloud Data Fusion.
3. Click the green "+" button at the top right to add an entity.
4. Under Driver, click Upload.
5. Now, upload the CData Connect AI JDBC driver (JAR file).
6. Enter the driver settings:
   • Name: Enter the name of the driver
   • Class name: Enter "cdata.jdbc.connect.ConnectDriver"
   • Version: Enter the driver version
   • Description (optional): Enter a description for the driver
7. Click on Finish.
8. Enter source configuration settings:
   • Label: Helps to identify the connection
   • JDBC driver name: Enter the JDBC driver name to identify the driver configured in Step 6.
   • Connection string: Enter the JDBC connection string, for example:

     jdbc:connect:AuthScheme=Basic;user=username;password=PAT;

   • User: Enter your CData Connect AI username, displayed in the top-right corner of the CData Connect AI interface. For example, "[email protected]"
   • Password: Enter the PAT you generated on the Settings page.
9. Click Validate in the top right corner.
10. If the connection is successful, you can manage the pipeline by editing it through the UI.
11. Run the pipepline created.

Troubleshooting

Please be aware that there is a known issue in Cloud Data Fusion where "int" types from source data are automatically cast as "long".

Live Access to JSON Services from Cloud Applications

Now you have a direct connection to live JSON services from from Google Cloud Data Fusion. You can create more connections to ensure a smooth movement of data across various sources and destinations, thereby streamlining data integration processes - all without replicating JSON services.

To get real-time data access to hundreds of SaaS, Big Data, and NoSQL sources (including JSON) directly from your cloud applications, explore the CData Connect AI.