Connect Dolby and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Dolby
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Dolby and TimescaleDB
Enable Integrations or automations with these events of Dolby and TimescaleDB
Actions
Update Webhook
Update the previously registered webhook configuration.
Register Webhook
Register a webhook that is triggered when a job completes.
Start Enhancing
Start enhancing to improve your media.
Delete Webhook
Delete a previously registered webhook configuration.
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Frequently Asked Questions
To start, connect both your Dolby and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Dolby triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Dolby 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 Dolby 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 Dolby 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 Dolby
Dolby.io provides advanced audio and video processing solutions for developers, enabling them to integrate high-quality media experiences into their applications. With a focus on enhancing communication and media playback, Dolby.io offers APIs and SDKs for real-time audio and video streaming, media processing, and more.
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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