How to Build an ETL App for Google Translate Data in Python with CData

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python and the petl framework, you can build Google Translate-connected applications and pipelines for extracting, transforming, and loading Google Translate data. This article shows how to connect to Google Translate with the CData Python Connector and use petl and pandas to extract, transform, and load Google Translate data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Google Translate data in Python. When you issue complex SQL queries from 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).

Connecting to Google Translate Data

Connecting to Google Translate data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

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

After installing the CData Google Translate Connector, follow the procedure below to install the other required modules and start accessing Google Translate through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install petl
pip install pandas

Build an ETL App for Google Translate Data in Python

Once the required modules and frameworks are installed, we are ready to build our ETL app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import petl as etl
import pandas as pd
import cdata.api as mod

You can now connect with a connection string. Use the connect function for the CData Google Translate Connector to create a connection for working with Google Translate data.

cnxn = mod.connect("Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Create a SQL Statement to Query Google Translate

Use SQL to create a statement for querying Google Translate. In this article, we read data from the SupportedLanguages entity.

sql = "SELECT LanguageCode, DisplayName FROM SupportedLanguages WHERE ProjectId = 'my-project-12345'"

Extract, Transform, and Load the Google Translate Data

With the query results stored in a DataFrame, we can use petl to extract, transform, and load the Google Translate data. In this example, we extract Google Translate data, sort the data by the DisplayName column, and load the data into a CSV file.

Loading Google Translate Data into a CSV File

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'DisplayName')

etl.tocsv(table2,'supportedlanguages_data.csv')

With the CData API Driver for Python, you can work with Google Translate data just like you would with any database, including direct access to data in ETL packages like petl.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Google Translate data. Reach out to our Support Team if you have any questions.

Full Source Code

import petl as etl
import pandas as pd
import cdata.api as mod

cnxn = mod.connect("Profile=C:\profiles\GoogleTranslate.apip;AuthScheme=OAuth;InitiateOAuth=GETANDREFRESH;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

sql = "SELECT LanguageCode, DisplayName FROM SupportedLanguages WHERE ProjectId = 'my-project-12345'"

table1 = etl.fromdb(cnxn,sql)

table2 = etl.sort(table1,'DisplayName')

etl.tocsv(table2,'supportedlanguages_data.csv')