Connect Landbot and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Landbot
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Landbot and TimescaleDB
Enable Integrations or automations with these events of Landbot and TimescaleDB
Actions
Get a Channel
Get an existing channel by its channel ID
List Channels
List all existing channels
Delete a Channel
Delete an existing Channel by its channel ID
List Customers
List all existing Customers
Get a Customer
Get an existing Customer by Customer ID
Delete a Customer
Delete an existing Customer by Customer Id
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Frequently Asked Questions
To start, connect both your Landbot and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Landbot triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Landbot 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 Landbot 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 Landbot 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 Landbot
Landbot is a no-code chatbot platform that enables users to create conversational chatbots designed to improve customer engagement and collect valuable user insights. It allows the deployment of chatbots on websites, WhatsApp, or Facebook Messenger without any coding knowledge.
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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