Connect WhatsAndro and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for WhatsAndro
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of WhatsAndro and TimescaleDB
Enable Integrations or automations with these events of WhatsAndro and TimescaleDB
Actions
Send WhatsApp Message
Sends a new message to a specified Contact on WhatsApp.
Create Contact
Creates a Contact.
Update Contact
Update an Existing Contact.
Send Media message
Send Media Message to a Cotnact.
Send Template Message
Send a Template message to a Contact.
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Frequently Asked Questions
To start, connect both your WhatsAndro and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in WhatsAndro triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how WhatsAndro 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 WhatsAndro 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 WhatsAndro 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 WhatsAndro
WhatsAndro is a platform dedicated to providing resources, tools, and insights for Android enthusiasts and developers. It offers tutorials, app reviews, and the latest news in the Android ecosystem, helping users stay updated with the ever-evolving world of Android technology.
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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