Google BigQuery

Connecting DinMo to Google BigQuery: Step-by-Step Guide

Overview

The connection process involves generating a Google Cloud service account, and granting it with the following roles:

DinMo will automatically create the following datasets to store its technical data:

dinmo_segments: storing views of the query of the segments and entities created within DinMo
dinmo_stats: storing tables that contain statistics about your segments
dinmo_delta_storage: storing snapshots of a historical run of segments queries that enable DinMo to calculate the changes that occur on a segment and update, therefore, the destination
dinmo_predictions: storing AI attributes such as churn likelihood and expected lifetime value.

These datasets should not be altered in your BigQuery project.

Learn how to create a source in our step-by-step tutorial

In the connection process:

This information is displayed in the Google Cloud interface:

Note that the Google Cloud project id might sometimes differ from the Google Cloud project name.

You should fill in the internal project id field only if you want DinMo to store its technical data in a separate project. If left blank, DinMo will be storing these data in the project mentioned above.
The project region refers to where the tables are stored. DinMo only supports one region per BigQuery source, so please ensure you store your data in the same region. In case you decided to use two separate projects, please ensure that the location region remains the same for both projects.
The source name is only used as a display name in DinMo.
To generate a DinMo service account, simply click the corresponding button.

Depending if you chose to store data in a separate project or not, it will generate two or four blocks of codes.

Simply copy these blocks of code and run them in your BigQuery console, without changing their values.

Step 4: Hit Save & Test in the bottom right of the page. If the tests do not encounter any issue, your source will be connected!

Last updated 5 days ago