Table of Contents

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  1. Preface
  2. Introduction to Informatica Data Engineering Integration
  3. Mappings
  4. Mapping Optimization
  5. Sources
  6. Targets
  7. Transformations
  8. Python Transformation
  9. Data Preview
  10. Cluster Workflows
  11. Profiles
  12. Monitoring
  13. Hierarchical Data Processing
  14. Hierarchical Data Processing Configuration
  15. Hierarchical Data Processing with Schema Changes
  16. Intelligent Structure Models
  17. Blockchain
  18. Stateful Computing
  19. Appendix A: Connections Reference
  20. Appendix B: Data Type Reference
  21. Appendix C: Function Reference

Google BigQuery Connection Properties

Google BigQuery Connection Properties

When you set up a Google BigQuery connection, you must configure the connection properties.
The order of the connection properties might vary depending on the tool where you view them.
The following table describes the Google BigQuery connection properties:
Property
Description
Service Account ID
Specifies the client_email value present in the JSON file that you download after you create a service account in Google BigQuery.
Service Account Key
Specifies the private_key value present in the JSON file that you download after you create a service account in Google BigQuery.
Connection mode
The mode that you want to use to read data from or write data to Google BigQuery.
Select one of the following connection modes:
  • Simple. Flattens each field within the Record data type field as a separate field in the mapping.
  • Hybrid. Displays all the top-level fields in the Google BigQuery table including Record data type fields. PowerExchange for Google BigQuery displays the top-level Record data type field as a single field of the String data type in the mapping.
  • Complex. Displays all the columns in the Google BigQuery table as a single field of the String data type in the mapping.
Default is Simple.
Schema Definition File Path
Specifies a directory on the client machine where the
Data Integration Service
must create a JSON file with the sample schema of the Google BigQuery table. The JSON file name is the same as the Google BigQuery table name.
Alternatively, you can specify a storage path in Google Cloud Storage where the
Data Integration Service
must create a JSON file with the sample schema of the Google BigQuery table. You can download the JSON file from the specified storage path in Google Cloud Storage to a local machine.
Project ID
Specifies the project_id value present in the JSON file that you download after you create a service account in Google BigQuery.
If you have created multiple projects with the same service account, enter the ID of the project that contains the dataset that you want to connect to.
Storage Path
This property applies when you read or write large volumes of data.
Path in Google Cloud Storage where the
Data Integration Service
creates a local stage file to store the data temporarily.
You can either enter the bucket name or the bucket name and folder name.
For example, enter
gs://<bucket_name>
or
gs://<bucket_name>/<folder_name>
Dataset ID
Not applicable for PowerExchange for Google BigQuery.
Use Legacy SQL For Custom Query
Not applicable for PowerExchange for Google BigQuery.
Dataset Name for Custom Query
Not applicable for PowerExchange for Google BigQuery.
Region ID
The region name where the Google BigQuery dataset resides.
For example, if you want to connect to a Google BigQuery dataset that resides in Las Vegas region, specify
us-west4
as the
Region ID
.
In the
Storage Path
connection property, ensure that you specify a bucket name or the bucket name and folder name that resides in the same region as the dataset in Google BigQuery.
For more information about the regions supported by Google BigQuery, see the following Google BigQuery documentation:https://cloud.google.com/bigquery/docs/locations


Updated September 28, 2020