
When this happens...
New or Updated File in specific folder

Automatically do this!
Enable Integrations or automations with these events of Google Drive and TimescaleDB
Return files that were created or modified in a specified Google Drive folder within the configured time window.
Create a new Google Docs document, Sheet, or Slide in Google Drive and optionally save it to a chosen folder.
Create a new folder in Google Drive. Optionally specify a parent folder to place it inside.
Grant a specific user or anyone access to a Google Drive file as Viewer, Commenter, or Editor.
Make a copy of a Google Drive file into a selected folder, optionally giving it a new name.
Get files from Drive or folder
Make a file accessible via a shareable link and set access level (Viewer, Commenter, Editor).

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To start, connect both your Google Drive and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Google Drive triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Google Drive 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 Google Drive 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 Google Drive 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.
Google Drive is Google's file sync app that lets you store all of your files online alongside your Google Docs documents, and keep them synced with all of your devices.
Learn MoreTimescaleDB 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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