Connect Hostfully and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Booking
Cancelled Booking
Updated Booking
New Booking Inquiry
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Hostfully and TimescaleDB
Enable Integrations or automations with these events of Hostfully and TimescaleDB
Triggers
New Booking
Triggers when a new booking arrives.
Cancelled Booking
Triggers when a booking is cancelled.
Updated Booking
Triggers when a booking is updated.
New Booking Inquiry
Triggers whenever a new inquiry is received.
Actions
Create Booking
Add a reservation
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Frequently Asked Questions
To start, connect both your Hostfully and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Hostfully triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Hostfully 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 Hostfully 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 Hostfully 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 Hostfully
Hostfully is a complete, end-to-end property management platform that helps vacation rental PMs boost revenues.
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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