Connect Webtiger and TimescaleDB to Build Intelligent Automations
Enable Integrations or automations with these events of Webtiger and TimescaleDB
Enable Integrations or automations with these events of Webtiger and TimescaleDB
Triggers
New Lead
Triggers when a new lead is created
New Contract
Triggers when a new contact is created
New Customer
Triggers when a new customer is added
New Estimate
Triggers when a new estimate is created
New Invoice
Triggers when a new invoice is cretaed
New Proposal
Triggers when a new proposal is created
Actions
Add Lead
Creates a new lead
Add Contract
Creates a new contract
Add Customer
Creates a new customer in the system
Add Estimate
Creates a new estimate in the system
Add Project
Triggers when a new project is created
Add Proposal
Creates a new proposal in the system
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Frequently Asked Questions
To start, connect both your Webtiger and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Webtiger triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Webtiger 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 Webtiger 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 Webtiger 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 Webtiger
Webtiger is a comprehensive platform offering web development and digital marketing solutions tailored to enhance online presence and drive business growth.
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About 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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