Connect SandeshChat and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for SandeshChat
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of SandeshChat and TimescaleDB
Enable Integrations or automations with these events of SandeshChat and TimescaleDB
Actions
Send Template Message
Send a SandeshChat template message to a recipient's phone number.
Send Session Message
Send a chat message (text, image, audio, video, document, or location) to a recipient via SandeshChat.
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Frequently Asked Questions
To start, connect both your SandeshChat and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in SandeshChat triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how SandeshChat 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 SandeshChat 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 SandeshChat 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 SandeshChat
SandeshChat is a dynamic communication platform designed to enhance team collaboration and streamline messaging. Ideal for businesses and teams looking to improve their internal communication, SandeshChat offers a range of features including instant messaging, file sharing, and group chats.
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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