Connect Signaturely and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Signaturely
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Signaturely and TimescaleDB
Enable Integrations or automations with these events of Signaturely and TimescaleDB
Actions
Create Folder
This action creates a new folder.
Search Document
Search your documents.
Rename Document
This action rename the document.
Delete Document
This action deletes the document.
Create signing request
Create a signature request from a selected template with a title and optional message; supports test mode.
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Frequently Asked Questions
To start, connect both your Signaturely and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Signaturely triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Signaturely 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 Signaturely 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 Signaturely 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 Signaturely
Signaturely offers an integration that enables you to create documents from templates or trigger actions such as sending or completing documents automatically. Streamline your workflow and save time with Signaturely's efficient integration.
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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