Option 1: Setup Big Query as Data Source for Local Product Inventory

Video Walkthrough

Description  :

Google BigQuery is a scalable, fully managed, serverless cloud data warehouse that supports lightning-fast SQL queries over large datasets.
When used as a Datahash source for Local Product Inventory (LPI), BigQuery enables you to send real-time or batch updates of store-level product availability to ad platforms — powering local inventory ads and improving local commerce performance.

Prerequisites  

•  Google Cloud Project with BigQuery API enabled
• Service Account with:
  • BigQuery User role
  • BigQuery Data Editor role
•  Service Account JSON Key for authentication
• Local Product Inventory dataset prepared in Datahash-required schema

Getting Started:  

• Go to https://studio.datahash.com/login
• Enter your credentials

Select BigQuery Source  

• In the left navigation, go to Sources → Warehouse
• Click the BigQuery connector tile

Choose File Data Type  

• On the File Data Type selection screen, choose Local Product Inventory
• Provide a Source Name for your connection
• Click Next

Choose Integration Method  

You can integrate via:

• Table Path — Pull directly from a defined table in BigQuery
• Query Path — Pull data via a custom SQL query

• Click Validate Credentials

• If validation succeeds, click Finish to complete setup

Option B: Query Path  

• Provide the following credentials:
  • JSON Key → Service Account JSON key file
  • Project ID → GCP Project hosting the dataset
  • Dataset ID → Dataset name in BigQuery

• Click Validate Credentials

• On the “Configure” screen, enter your SQL Query to fetch Local Product Inventory data

• Click Preview Results to verify output
• If correct, click Finish to complete setup

• If everything looks good, click Finish to complete the setup. The source connector set-up will be marked ‘Completed’ as below. If the set-up is exited before finishing the set-up, the connector will remain in pending status and still be completed any time later by clicking the Connection Name in the below step.

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Where to find the Service Account Credentials JSON Key, Project ID and Dataset ID in the Google Cloud Platform:

• Go to Google Cloud Console
• Once you are logged in, select the relevant project.

• Navigate to IAM & Admin → Service Accounts
• Create or select an existing service account with BigQuery User + BigQuery Data Editor roles

• Go to Keys → Add Key → Create New Key → JSON
• Save the JSON file

Project ID:

Project ID  

• Found in your project list in GCP dashboard

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Dataset ID:

1. Log in to your Google cloud console https://console.cloud.google.com
2.  
3. In BigQuery Studio, click the dataset to view Dataset ID in the info panel

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Table Name  

Expand the dataset in BigQuery Explorer and copy the table name

How to create a BigQuery Service Account:

• Sign in to the Google Cloud management console.
• Make sure that you have API enabled on your BigQuery API page. If you don’t see API Enabled, choose Enable.

• Once done, you will get the “API Enabled” badge.

• On the Service accounts page, choose your BigQuery project, and then choose Create service account.

• On the Service account details page, enter a descriptive value for Service account name. Choose Create and continue. The Grant this service account access to the project page opens.
• For Select a role, choose BigQuery USer, and then choose BigQuery Data Editor.

• Choose Continue, and then choose Done.

• On the Service accounts page, choose the service account that you created.

• Choose Keys, Add key, Create new key.

• Choose JSON, and then choose Create. Choose the folder to save your private key or check the default folder for downloads in your browser.