Connect Reamaze and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
New Conversation Starts
New Message Created
New Contact Created
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Reamaze and TimescaleDB
Enable Integrations or automations with these events of Reamaze and TimescaleDB
Triggers
New Conversation Starts
Runs when new conversation starts
New Message Created
Runs when new message is created
New Contact Created
Runs when new contact is created
Actions
Create Contact
Create a contact in Reamaze
Create Message
Send a message in a conversation
Create Conversation
Create a new customer conversation.
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Frequently Asked Questions
To start, connect both your Reamaze and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Reamaze triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Reamaze 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 Reamaze 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 Reamaze 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 Reamaze
Reamaze is a web-based customer communication platform that integrates email, social media, and live chat functionalities to provide a comprehensive support solution for businesses.
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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