Connect Facebook Custom Audience and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Facebook Custom Audience
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Facebook Custom Audience and TimescaleDB
Enable Integrations or automations with these events of Facebook Custom Audience and TimescaleDB
Actions
Add User to Custom Audience
Add user to Facebook audience
Create Empty Audience
Create an empty audience
Remove User From Custom Audience
Remove user from audience
Get Custom Audience Details
Retrieve a Facebook audience
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Frequently Asked Questions
To start, connect both your Facebook Custom Audience and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Facebook Custom Audience triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Facebook Custom Audience data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between Facebook Custom Audience and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Facebook Custom Audience and TimescaleDB. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Facebook Custom Audience
Tool to create and manage Facebook audience segments for advertising targeting.
Learn MoreAbout TimescaleDB
TimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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