Connect Doubletick and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Doubletick
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Doubletick and TimescaleDB
Enable Integrations or automations with these events of Doubletick and TimescaleDB
Actions
Send WhatsApp Message
Send a WhatsApp Message to a Contact
Send WhatsApp Media Message
Send WhatsApp Media Message
Send WhatsApp Template Message
Send a pre-approved whatsapp message template to a contact
Create a new group
Create a new group by group name
Add a member to a group
Add a new member to a group
Delete a group
Delete an existing group by group ID
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Frequently Asked Questions
To start, connect both your Doubletick and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Doubletick triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Doubletick 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 Doubletick 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 Doubletick 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 Doubletick
Doubletick is an email tracking tool that allows users to see when their emails have been read. It integrates seamlessly with popular email clients to enhance communication tracking.
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.
Learn More
