Migrating data from Google Translate to Databricks using CData SSIS Components.

Cameron Leblanc
Cameron Leblanc
Senior Technology Evangelist
Easily push Google Translate data to Databricks using the CData SSIS Tasks for Google Translate and Databricks.

Databricks is a unified data analytics platform that allows organizations to easily process, analyze, and visualize large amounts of data. It combines data engineering, data science, and machine learning capabilities in a single platform, making it easier for teams to collaborate and derive insights from their data.

The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.

In this article, we explore the data type mapping considerations when exporting to Databricks and walk through how to migrate Google Translate data to Databricks using the CData SSIS Components for Google Translate and Databricks.

Data Type Mapping

Databricks Schema CData Schema

int, integer, int32

int

smallint, short, int16

smallint

double, float, real

float

date

date

datetime, timestamp

datetime

time, timespan

time

string, varchar

If length > 4000: nvarchar(max), Otherwise: nvarchar(length)

long, int64, bigint

bigint

boolean, bool

tinyint

decimal, numeric

decimal

uuid

nvarchar(length)

binary, varbinary, longvarbinary

binary(1000) or varbinary(max) after SQL Server 2000

Special Considerations

  • String/VARCHAR: String columns from Databricks can map to different data types depending on the length of the column. If the column length exceeds 4000, then the column is mapped to nvarchar (max). Otherwise, the column is mapped to nvarchar (length).
  • DECIMAL Databricks supports DECIMAL types up to 38 digits of precision, but any source column beyond that can cause load errors.

Prerequisites

Create the project and add components

  1. Open Visual Studio and create a new Integration Services Project.
  2. Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
  3. Add a CData Google Translate Source control and a CData Databricks Destination control to the data flow task.

Configure the Google Translate source

Follow the steps below to specify properties required to connect to Google Translate.

  1. Double-click the CData Google Translate Source to open the source component editor and add a new connection.
  2. In the CData Google Translate Connection Manager, configure the connection properties, then test and save the connection.

    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

  3. After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Google Translate Source Editor.

Configure the Databricks destination

With the Google Translate Source configured, we can configure the Databricks connection and map the columns.

  1. Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
  2. In the CData Databricks Connection Manager, configure the connection properties, then test and save the 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, 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).

    Other helpful connection properties

    • QueryPassthrough: When this is set to True, queries are passed through directly to Databricks.
    • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
    • UseUploadApi: Setting this property to true will improve performance if there is a large amount of data in a Bulk INSERT operation.
    • UseCloudFetch: This option specifies whether to use CloudFetch to improve query efficiency when the table contains over one million entries.
  3. After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
  4. On the Column Mappings tab, configure the mappings from the input columns to the destination columns.

Run the project

You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.

Ready to get started?

Connect to live data from Google Translate with the API Driver

Connect to Google Translate

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