Connect Teamtailor to BigQuery
Send Teamtailor users, candidates, custom fields and 15 more data types to BigQuery on a schedule. Refresh as often as every 15 minutes.
BigQuery
dataset teamtailor
userscandidatescustom_fieldsdepartmentsjobs- 13 more data types
How to connect Teamtailor and BigQuery
4 steps. You can preview the Teamtailor data in Coupler.io before anything reaches BigQuery.
Step 1 of 4
Connect Teamtailor
Enter your Teamtailor API version and API key. You only do this once per account.
Step 2 of 4
Choose what to export
Pick the Teamtailor data you want in BigQuery.
Show 10 more data types Show fewer data types
Step 3 of 4
Connect BigQuery
Upload the JSON key file from your Google Cloud service account, then enter the dataset and table for the Teamtailor data.
Step 4 of 4
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 users with fresh Teamtailor data.
Coupler.io made it easy to create dashboards and reports from different data sources. The sync is reliable and the user experience is amazing.
Take your data analytics to the next level
Sign Up with Google
Sign Up with MICROSOFT
SIGN UP WITH EMAIL
By signing up to Coupler.io, you agree to our Privacy Policy and Terms of Use.
Build your Teamtailor integrations with these destinations
Get the most of Teamtailor to BigQuery integration
Gather data
Understand data
Automate data
Act on data
18 data types, including Users, Candidates, Custom fields, Departments and Jobs.
With your Teamtailor API version and API key, entered once when you add the connection.
Yes. If the table doesn't exist in the dataset you choose, Coupler.io creates it on the first run and fills it with Teamtailor 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 100ms or Amazon Seller Central, and join or append them before the data reaches BigQuery.
