Connect Pinterest Organic & Shopping to PostgreSQL
Send Pinterest Organic & Shopping boards, catalogs, catalogs feeds and 5 more data types to PostgreSQL on a schedule. Filter by date. Refresh as often as every 15 minutes.
pinterest
boardscatalogscatalogs_feedscatalogs_product_groupsboard_sections- 3 more data types
How to connect Pinterest Organic & Shopping and PostgreSQL
5 steps. You can preview the Pinterest Organic & Shopping data in Coupler.io before anything reaches PostgreSQL.
Step 1 of 5
Connect Pinterest Organic & Shopping
Sign in to Pinterest Organic & Shopping and authorize Coupler.io. There's no API key to copy.
Step 2 of 5
Choose what to export
Pick the Pinterest Organic & Shopping data you want in PostgreSQL.
Step 3 of 5
Fine-tune the export
Optional settings for this Pinterest Organic & Shopping export.
Step 4 of 5
Connect PostgreSQL
Enter your PostgreSQL connection details, then the schema and table for the Pinterest Organic & Shopping data.
Show 3 more fields Show fewer fields
Step 5 of 5
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 boards and loads fresh Pinterest Organic & Shopping data.
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8 data types, including Boards, Catalogs, Catalogs feeds, Catalogs product groups and Board sections.
You sign in to Pinterest Organic & Shopping and authorize Coupler.io. There's no API key to copy.
Yes. Set the start date to cover the period you need.
Yes, if the database user has CREATE privileges. Coupler.io creates the pinterest schema and the boards table on the first run.
Yes. Append adds new rows to boards on each run, while Replace swaps in a fresh copy. With Append, add new Pinterest Organic & Shopping 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 Google Ads or Facebook Ads, and join or append them before the data reaches PostgreSQL.