Connect DivvyHQ and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for DivvyHQ
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of DivvyHQ and TimescaleDB
Enable Integrations or automations with these events of DivvyHQ and TimescaleDB
Actions
Create Content Item
Creates a content item.
List Calendar
List allowed type per calendar.
List Base Calendars
List all base calendars.
List Calendarentries
List all calendarentries.
List Campaigns
List all campaigns.
List Contentitems
List all Contentitems.
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Frequently Asked Questions
To start, connect both your DivvyHQ and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in DivvyHQ triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how DivvyHQ 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 DivvyHQ 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 DivvyHQ 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 DivvyHQ
DivvyHQ is a content planning and production workflow platform designed to help marketing and content teams manage their content strategies more efficiently. It offers tools for content ideation, planning, collaboration, and publishing, making it easier to streamline content operations.
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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