Connect Cal.com to PostgreSQL
Send Cal.com event types, my profiles, schedules and 3 more data types to PostgreSQL on a schedule. Refresh as often as every 15 minutes.
PostgreSQL
schema cal_com
event_typesmy_profilesschedulescalendarsbookings- 1 more data type
How to connect Cal.com and PostgreSQL
4 steps. You can preview the Cal.com data in Coupler.io before anything reaches PostgreSQL.
Step 1 of 4
Connect Cal.com
Enter your Cal.com organization ID and API key. You only do this once per account.
Step 2 of 4
Choose what to export
Pick the Cal.com data you want in PostgreSQL.
Step 3 of 4
Connect PostgreSQL
Enter your PostgreSQL connection details, then the schema and table for the Cal.com data.
Show 3 more fields Show fewer fields
Step 4 of 4
Choose a write mode and schedule
Every run either replaces or appends the data in PostgreSQL. Run it as often as every 15 minutes.
Each run deletes the existing rows in event_types and loads fresh Cal.com 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 Cal.com integrations with these destinations
Get the most of Cal.com to PostgreSQL integration
Gather data
Understand data
Automate data
Act on data
6 data types, including Event types, My profiles, Schedules, Calendars and Bookings.
With your Cal.com organization ID and API key, entered once when you add the connection.
Yes, if the database user has CREATE privileges. Coupler.io creates the cal_com schema and the event_types table on the first run.
Yes. Append adds new rows to event_types on each run, while Replace swaps in a fresh copy. With Append, add new Cal.com columns to the table yourself before the next run.
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 Clockify or Harvest, and join or append them before the data reaches PostgreSQL.
