Connect sinao and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for sinao
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of sinao and TimescaleDB
Enable Integrations or automations with these events of sinao and TimescaleDB
Actions
Create an Organization
Create an organization.
Create or Edit an Invoice
Create or Edit an invoice with an amount in TTC/HT.
Create a Product/Service
Createa product/service.
Create a Client
Create a Client.
Update a Client
Update a Client.
Update a Product/Service
Update a product/service.
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Automate Expense Report Approvals via Slack & QuickBooks
- Send for Approval acceptCreate Entry of expensesAdd Step
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Automate Social Media Post Creation and Scheduling Across Multiple Platforms
- InstagramInstagram CaptionGenerate ImagePublish Photo with TaggingAdd Step
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- TwitterTwitter TweetPost a TweetAdd Step
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Frequently Asked Questions
To start, connect both your sinao and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in sinao triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how sinao 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 sinao 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 sinao 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 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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