Build a video processing pipeline with Prisma Pulse and Trigger.dev

The Benefits of a Decoupled Event-Driven Architecture

An event-driven, decoupled architecture offers several key benefits:

• Scalability: Systems can scale more easily since components are independent, allowing individual scaling based on demand.
• Flexibility: Decoupled components can be modified or updated without affecting the entire system, enabling faster innovation and adaptability.
• Resilience: A failure in one service doesn’t necessarily affect the entire system because components are loosely connected, improving reliability.
• Asynchronous Processing: Events are processed asynchronously, allowing tasks to run in the background, improving performance.
• Fault Isolation: Decoupled services isolate issues, reducing the risk of cascading failures across the system.
• Maintainability: Clear separation of concerns allows teams to develop and maintain different parts of the system independently, making the architecture more maintainable.

These benefits make decoupled, event-driven architectures an ideal choice for modern, scalable applications, especially those involving complex workflows, such as video transcription.

Building a video transcribing workflow with Pulse and Trigger.dev

To demonstrate how you can apply this architecture, let's build a video transcribing workflow using Pulse and Trigger.dev. We'll create a system that transcribes a video from a URL and stores the transcription in a database.

Defining the data model

First, consider the following Prisma model, which defines how we store the video URL and its transcribed data:

This model will store both the URL of the video and its corresponding transcription, once processed.

Implementing the video transcription task

Next, we'll set up a transcription task using Trigger.dev. This script will take a video URL, extract its audio, and transcribe it using the Deepgram API:

In this task, we first download the video data and extract its audio using ffmpeg. Then, we pass the audio to Deepgram’s transcription service, which processes the audio and returns the transcription. The final transcription is stored in the database using Prisma.

When a new video is uploaded, we use Prisma ORM to save the video URL in the database:

Triggering the workflow

Once the video is uploaded, we can trigger the transcription workflow. Using Prisma Pulse, we listen for new video records and trigger the transcription task accordingly:

Reacting to completed transcriptions

After the transcription is complete, we can react to this event by notifying a client or triggering additional processes. This can also be done using Prisma Pulse:

This demonstrates how efficiently you can build an end-to-end workflow using these tools, taking advantage of decoupled, event-driven architectures.

Try it out for yourself

Now, it's your turn to implement this approach and see how easily you can integrate decoupled workflows in your own projects. Experiment with these tools and see how they can simplify your development processes. Whether you're transcribing videos or building more complex applications, Pulse and Trigger.dev provide the scalability and flexibility to handle a wide range of tasks.

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