Renta automatically imports data from Salesforce to the data warehouse. Currently the following databases are supported:
- Google BigQuery.
- Microsoft SQL Server.
- Azure SQL database.
The data can then be used in different analytics systems like Power BI, Tableau, or in Excel.
Integration setup on the example of BigQuery
In this manual the example of Salesforce integration with Google BigQuery is presented.
Text transcript is provided below.
After a successful authentication in Renta you need to create an integration. To do that, click on the Add button, and after that you will see the list of all possible integrations.
Then you need to sign in using the account you need to get data from. You can do that in just a few clicks:
Click on the Add button, after that the Salesforce authentication window will open.
Enter your login and password.
After that the account is available for selection.
The third step is to select what objects you need to upload to BigQuery. Each element will be uploaded to a separate table.
The list of all available elements as well as their contents can be accessed in the Salesforce Help section.
As an example, we specify the following objects:
And as an update frequency we select each hour.
On the last stage we specify where to save the selected tables.
For that you need to add the source. Click on the Add button and select BigQuery as the database.
After that you will be redirected to the authentication in Google Cloud. Sign in using the same login and password you used to create the BigQuery project.
After a successful authentication you will see the project selection window (in case there are several projects within the added account).
As a final step, select the project from the provided list.
From that moment the integration will launch automatically and the data download will start.
Data can be updated every minute. Also you can expand the update time period up to 6 hours.
It is worth noticing that the Salesforce API allows tracking exact changes to each object. Therefore the table updates occur in particular rows, which eventually increases speed and saves resources needed for the data processing in Google BigQuery.