Process & Analyze Google Translate Data in Databricks (AWS)

Databricks is a cloud-based service that provides data processing capabilities through Apache Spark. When paired with the CData JDBC Driver, customers can use Databricks to perform data engineering and data science on live Google Translate data. This article explains how to host the CData JDBC Driver in AWS, as well as connect to and process live Google Translate data in Databricks.

With built-in optimized data processing, the CData JDBC Driver offers unmatched performance for interacting with live Google Translate data. When you issue complex SQL queries to Google Translate, the driver pushes supported SQL operations, like filters and aggregations, directly to Google Translate and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations). Its built-in dynamic metadata querying allows you to work with and analyze Google Translate data using native data types.

Install the CData JDBC Driver in Databricks

To work with live Google Translate data in Databricks, install the driver on your Databricks cluster.

1. Navigate to your Databricks administration screen and select the target cluster.
2. On the Libraries tab, click "Install New."
3. Select "Upload" as the Library Source and "Jar" as the Library Type.
4. Upload the JDBC JAR file (cdata.jdbc.api.jar) from the installation location (typically C:\Program Files\CData[product_name]\lib).

Access Google Translate Data in your Notebook: Python

With the JAR file installed, we are ready to work with live Google Translate data in Databricks. Start by creating a new notebook in your workspace. Name the notebook, select Python as the language (though Scala is available as well), and choose the cluster where you installed the JDBC driver. When the notebook launches, we can configure the connection, query Google Translate, and create a basic report.

Configure the Connection to Google Translate

Connect to Google Translate by referencing the JDBC Driver class and constructing a connection string to use in the JDBC URL. Additionally, you will need to set the RTK property in the JDBC URL (unless you are using a Beta driver). You can view the licensing file included in the installation for information on how to set this property.

Step 1: Connection Information

driver = "cdata.jdbc.api.APIDriver"
url = "jdbc:api:RTK=5246...;Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;"

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the Google Translate JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.api.jar

Fill in the connection properties and copy the connection string to the clipboard.

Authentication

Google Cloud Translation API requires OAuth 2.0 authentication to ensure secure access to translation services, datasets, glossaries, and adaptive MT resources. This authentication method allows you to securely connect to your Google Cloud project and manage translation resources with proper authorization.

OAuth 2.0 Setup and Configuration

Step 1: Create Google Cloud Project and Enable API

To set up OAuth authentication:

1. Visit the Google Cloud Console
2. Create a new project or select an existing project
3. Note down your Project ID (required for all API calls)
4. Navigate to "APIs & Services" > "Library"
5. Search for and enable the "Cloud Translation API"
6. Go to "APIs & Services" > "Credentials"
7. Click "Create Credentials" and select "OAuth Client ID"
8. Configure the OAuth consent screen if prompted
9. Select "Desktop application" or "Web application" as appropriate
10. Set the authorized redirect URI (CallbackURL)
11. Copy the Client ID and Client Secret for use in your connection

Required Connection Properties

• AuthScheme: Set this to OAuth (required)
• OAuthClientId: Client ID from Google Cloud Console (required)
• OAuthClientSecret: Client secret from Google Cloud Console (required)
• CallbackURL: Redirect URI specified in your OAuth application (required)
• InitiateOAuth: Set to GETANDREFRESH for automatic token management (recommended)
• ProjectId: Your Google Cloud project ID or project number (required for queries)

Required OAuth Scopes

The Google Cloud Translation API Profile requires the following OAuth scope:

• https://www.googleapis.com/auth/cloud-translation - Full access to Cloud Translation API resources including translation, datasets, glossaries, and adaptive MT

Load Google Translate Data

Once you configure the connection, you can load Google Translate data as a dataframe using the CData JDBC Driver and the connection information.

Step 2: Reading the data

remote_table = spark.read.format ( "jdbc" ) \
	.option ( "driver" , driver) \
	.option ( "url" , url) \
	.option ( "dbtable" , "SupportedLanguages") \
	.load ()

Display Google Translate Data

Check the loaded Google Translate data by calling the display function.

Step 3: Checking the result

display (remote_table.select ("LanguageCode"))

Analyze Google Translate Data in Databricks

If you want to process data with Databricks SparkSQL, register the loaded data as a Temp View.

Step 4: Create a view or table

remote_table.createOrReplaceTempView ( "SAMPLE_VIEW" )

With the Temp View created, you can use SparkSQL to retrieve the Google Translate data for reporting, visualization, and analysis.

% sql

SELECT LanguageCode, DisplayName FROM SAMPLE_VIEW ORDER BY DisplayName DESC LIMIT 5

The data from Google Translate is only available in the target notebook. If you want to use it with other users, save it as a table.

remote_table.write.format ( "parquet" ) .saveAsTable ( "SAMPLE_TABLE" )

Download a free, 30-day trial of the CData API Driver for JDBC and start working with your live Google Translate data in Databricks. Reach out to our Support Team if you have any questions.