Migrating data from Salesforce Data Cloud to Google BigQuery using CData SSIS Components.

Google BigQuery is a serverless, highly scalable, and cost-effective data warehouse designed to help organizations turn big data into actionable insights.

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 BigQuery and walk through how to migrate Salesforce Data Cloud data to Google BigQuery using the CData SSIS Components for Salesforce Data Cloud and BigQuery.

Data Type Mapping

STRUCT and ARRAY Types

Google BigQuery supports two kinds of types for storing compound values in a single row, STRUCT and ARRAY. In some places within Google BigQuery, these are also known as RECORD and REPEATED types.

A STRUCT is a fixed-size group of values that are accessed by name and can have different types. The component flattens structs so their fields can be accessed using dotted names. Note that these dotted names must be quoted.

An ARRAY is a group of values with the same type that can have any size. The component treats the array as a single compound value and reports it as a JSON aggregate. These types may be combined such that a STRUCT type contains an ARRAY field, or an ARRAY field is a list of STRUCT values.

Special Considerations

• Google BigQuery has both DATETIME (no timezone) and TIMESTAMP (with timezone) data types that the CData SSIS Components map to datetime based on the timezone of your local machine.
• In Google BigQuery, the NUMERIC type supports 38 digits of precision and up to 9 digits after the decimal point, while the BIGNUMERIC type supports 76 digits of precision and up to 38 digits after the decimal point. The CData SSIS Components for Google BigQuery automatically detects the precision/scale, but with the Destination Component users can manually map any high-precision columns.
• INTERVAL data types:
  • The component represents INTERVAL types as strings. Whenever a query requires an INTERVAL type, it must specify the INTERVAL using the BigQuery SQL INTERVAL format:

    YEAR-MONTH DAY HOUR:MINUTE:SECOND.FRACTION

  • For example, the value "5 years and 11 months, minus 10 days and 3 hours and 2.5 seconds" in the correct format is:

    5-11 -10 -3:0:0.2.5

Prerequisites

• Visual Studio 2022
• SQL Server Integration Services Projects extension for Visual Studio 2022
• CData SSIS Components for Google BigQuery
• CData SSIS Components for Salesforce Data Cloud

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 Salesforce Data Cloud Source control and a CData GoogleBigQuery Destination control to the data flow task.

Configure the Salesforce Data Cloud source

Follow the steps below to specify properties required to connect to Salesforce Data Cloud.

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

   Salesforce Data Cloud supports authentication via the OAuth standard.

   OAuth

   Set AuthScheme to OAuth.

   Desktop Applications

   CData provides an embedded OAuth application that simplifies authentication at the desktop.

   You can also authenticate from the desktop via a custom OAuth application, which you configure and register at the Salesforce Data Cloud console. For further information, see Creating a Custom OAuth App in the Help documentation.

   Before you connect, set these properties:

   • InitiateOAuth: GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
   • OAuthClientId (custom applications only): The Client ID assigned when you registered your custom OAuth application.
   • OAuthClientSecret (custom applications only): The Client Secret assigned when you registered your custom OAuth application.

   When you connect, the driver opens Salesforce Data Cloud's OAuth endpoint in your default browser. Log in and grant permissions to the application.

   The driver then completes the OAuth process as follows:

   • Extracts the access token from the callback URL.
   • Obtains a new access token when the old one expires.
   • Saves OAuth values in OAuthSettingsLocation so that they persist across connections.

   For other OAuth methods, including Web Applications and Headless Machines, refer to the Help documentation.

   
3. After saving the connection, select "Table or view" and select the table or view to export into Google BigQuery, then close the CData Salesforce Data Cloud Source Editor.

Configure the Google BigQuery destination

With the Salesforce Data Cloud Source configured, we can configure the Google BigQuery connection and map the columns.

1. Double-click the CData Google BigQuery Destination to open the destination component editor and add a new connection.
2. In the CData GoogleBigQuery Connection Manager, configure the connection properties, then test and save the connection.
   • Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app. OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values. In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

   Helpful connection properties

   • QueryPassthrough: When this is set to True, queries are passed through directly to Google BigQuery.
   • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
   • FlattenObjects: By default the component reports each field in a STRUCT column as its own column while the STRUCT column itself is hidden. When this is set to False, the top-level STRUCT is not expanded and is left as its own column. The value of this column is reported as a JSON aggregate.
   • SupportCaseSensitiveTables: When this property is set to true, tables with the same name but different casing will be renamed so they are all reported in the metadata. By default, the provider treats table names as case-insensitive, so if multiple tables have the same name but different casing, only one will be reported in the metadata.
   
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.