Connect Height to BigQuery
Send Height tasks, lists, activities and 5 more data types to BigQuery on a schedule. Filter by date. Refresh as often as every 15 minutes.
BigQuery
dataset height
taskslistsactivitiesusersworkspaces- 3 more data types
How to connect Height and BigQuery
5 steps. You can preview the Height data in Coupler.io before anything reaches BigQuery.
Step 1 of 5
Connect Height
Enter your Height API key. You only do this once per account.
Step 2 of 5
Choose what to export
Pick the Height data you want in BigQuery.
Step 3 of 5
Fine-tune the export
Optional settings for this Height export.
Step 4 of 5
Connect BigQuery
Upload the JSON key file from your Google Cloud service account, then enter the dataset and table for the Height data.
Step 5 of 5
Choose a write mode and schedule
Every run either replaces or appends the data in BigQuery. Run it as often as every 15 minutes.
Each run overwrites tasks with fresh Height data.
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Build your Height integrations with these destinations
Get the most of Height to BigQuery integration
Gather data
Understand data
Automate data
Act on data
8 data types, including Tasks, Lists, Activities, Users and Workspaces.
With your Height API key, entered once when you add the connection.
Yes. Set the start date to cover the period you need.
Yes. If the table doesn't exist in the dataset you choose, Coupler.io creates it on the first run and fills it with Height data.
Refresh the source field list in Coupler.io and select the new column. If you define the BigQuery table schema manually, add the column there too.
On a schedule you choose, from monthly down to every 15 minutes, depending on your Coupler.io plan.
Yes. Add more sources to the same data flow, for example ClickUp or Airtable, and join or append them before the data reaches BigQuery.
