Connect BigMailer to PostgreSQL
Send BigMailer contacts, lists, users and 7 more data types to PostgreSQL on a schedule. Refresh as often as every 15 minutes.
PostgreSQL
schema bigmailer
contactslistsusersbrandsfields- 5 more data types
How to connect BigMailer and PostgreSQL
4 steps. You can preview the BigMailer data in Coupler.io before anything reaches PostgreSQL.
Step 1 of 4
Connect BigMailer
Enter your BigMailer API key. You only do this once per account.
Step 2 of 4
Choose what to export
Pick the BigMailer data you want in PostgreSQL.
Show 2 more data types Show fewer data types
Step 3 of 4
Connect PostgreSQL
Enter your PostgreSQL connection details, then the schema and table for the BigMailer 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 contacts and loads fresh BigMailer 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 BigMailer integrations with these destinations
Get the most of BigMailer to PostgreSQL integration
Gather data
Understand data
Automate data
Act on data
10 data types, including Contacts, Lists, Users, Brands and Fields.
With your BigMailer API key, entered once when you add the connection.
Yes, if the database user has CREATE privileges. Coupler.io creates the bigmailer schema and the contacts table on the first run.
Yes. Append adds new rows to contacts on each run, while Replace swaps in a fresh copy. With Append, add new BigMailer 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 Calendly or Klaviyo, and join or append them before the data reaches PostgreSQL.
