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‎docs/docs.json‎

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}
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]
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},
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{
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"group": "Use cases",
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"pages": [
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"guides/use-cases/data-processing-etl",
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"guides/use-cases/media-generation",
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"guides/use-cases/media-processing",
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"guides/use-cases/marketing"
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]
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},
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{
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"group": "Example projects",
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"pages": [

‎docs/guides/introduction.mdx‎

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<Card title="SvelteKit" img="/images/logo-svelte.png" href="/guides/community/sveltekit" />
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</CardGroup>
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## Use cases
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Explore comprehensive guides for common use cases and workflows:
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<CardGroup cols={2}>
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<Card
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title="Data Processing & ETL"
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href="/guides/use-cases/data-processing-etl"
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>
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Transform and move data reliably at any scale. Whether streaming real-time analytics, enriching
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customer records, or synchronizing large datasets.
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</Card>
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<Card
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title="Content generation"
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href="/guides/use-cases/content-generation"
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>
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Create scalable content workflows from single assets to thousands. Generate PDFs, videos, or
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social media content with progress tracking and approval steps.
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</Card>
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<Card
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title="Media processing"
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href="/guides/use-cases/media-processing"
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>
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Process audio, video, and images efficiently with adaptive workflows. From real-time
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transcription to batch optimization.
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</Card>
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<Card
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title="Marketing"
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href="/guides/use-cases/marketing"
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>
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Orchestrate campaigns that respond dynamically to user behavior. Build drip sequences,
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personalization engines, and analytics workflows.
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</Card>
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</CardGroup>
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## Guides
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Get set up fast using our detailed walk-through guides.

‎docs/guides/use-cases/data-processing-etl.mdx‎

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---
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title: "Data Processing & ETL"
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description: "Build reliable data processing and ETL pipelines with automatic retries, progress tracking, and no timeout limits using Trigger.dev"
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---
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import UseCasesCards from "/snippets/use-cases-cards.mdx";
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## Overview
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Data processing and ETL (Extract, Transform, Load) workflows require handling large datasets, complex transformations, and reliable data movement between systems. Build robust data pipelines in TypeScript with automatic retries, progress tracking, and no timeout limits; perfect for web scraping, database synchronization, real-time analytics, and large-scale data transformation.
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## Basic data processing and ETL workflow implementation
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A typical ETL pipeline:
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1. **Extract**: Pull from APIs, databases, S3, or web scraping
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2. **Transform**: Clean, validate, enrich data
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3. **Load**: Write to warehouse, database, or storage
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4. **Monitor**: Track progress, handle failures
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Each step is durable and retryable—if transformation fails, Trigger.dev automatically retries without re-extracting source data thanks to [checkpoint-resume](/how-it-works#the-checkpoint-resume-system) and [idempotency keys](/idempotency).
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Trigger.dev is ideal for ETL pipelines because there are no [timeout limits](/runs/max-duration) (process datasets for hours or days), [batchTriggerAndWait()](/triggering#yourtask-batchtriggerandwait) parallelizes across thousands of records with [queue.concurrencyLimit](/queue-concurrency) to respect API rate limits, [metadata](/runs/metadata) + [realtime](/realtime) stream row-by-row progress to dashboards, and [schedules.task()](/tasks/scheduled) handles recurring jobs with cron syntax.
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## Data processing workflow examples
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<CardGroup cols={2}>
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<Card
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title="Realtime CSV importer"
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icon="book"
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href="/guides/example-projects/realtime-csv-importer"
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>
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Import CSV files with progress tracking streamed to the frontend.
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</Card>
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<Card title="Web scraper with BrowserBase" icon="book" href="/guides/examples/scrape-hacker-news">
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Scrape Hacker News using BrowserBase and Puppeteer, summarize with ChatGPT.
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</Card>
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<Card title="Firecrawl" icon="book" href="/guides/examples/firecrawl-url-crawl">
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Crawl URLs and return LLM-ready markdown using Firecrawl.
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</Card>
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<Card
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title="Supabase database operations"
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icon="book"
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href="/guides/examples/supabase-database-operations"
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>
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Run CRUD operations on a Supabase database table.
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</Card>
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<Card title="Sequin database triggers" icon="book" href="/guides/frameworks/sequin">
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Trigger tasks from database changes using Sequin's CDC platform.
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</Card>
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<Card
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title="Sync Vercel environment variables"
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icon="book"
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href="/guides/examples/vercel-sync-env-vars"
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>
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Automatically sync environment variables from Vercel projects.
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</Card>
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</CardGroup>
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## Production use cases
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<Card title="Papermark customer story" href="https://trigger.dev/customers/papermark-customer-story">
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Read how Papermark processes thousands of documents per month using Trigger.dev.
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</Card>
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## Common data processing patterns
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### Scheduled Data Syncs
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Run ETL jobs on a schedule to keep systems in sync:
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- Daily database exports and backups
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- Hourly API data pulls and transformations
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- Real-time webhook processing and routing
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- Periodic data warehouse updates
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### Event-Driven Processing
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Respond to data events with automated workflows:
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- Process new database records as they're created
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- Transform uploaded files immediately
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- React to webhook events from external systems
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- Handle real-time data streams
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### Batch Processing
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Process large datasets efficiently:
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- Import CSV files with thousands of rows
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- Bulk update records across systems
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- Process queued data in parallel batches
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- Generate reports from aggregated data
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### Pipeline Orchestration
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Chain multiple processing steps together:
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- Extract from API → Transform → Load to database
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- Web scraping → Data cleaning → Analysis → Storage
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- File upload → Validation → Processing → Notification
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- Multi-source data aggregation and enrichment
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<UseCasesCards />

‎docs/guides/use-cases/marketing.mdx‎

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---
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title: "Marketing"
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description: "Orchestrate campaigns that respond dynamically to user behavior. Build drip sequences, personalization engines, and analytics workflows with Trigger.dev"
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---
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import UseCasesCards from "/snippets/use-cases-cards.mdx";
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## Overview
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Marketing automation workflows require orchestrating complex campaigns that respond to user behavior, send personalized content, and track engagement across multiple channels. Build reliable marketing pipelines in TypeScript with automatic retries, progress tracking, and no timeout limits; perfect for email sequences, customer journeys, personalization engines, and analytics workflows.
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## Basic marketing workflow implementation
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A typical marketing automation workflow:
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1. **Trigger**: User event, [webhook](/triggering), or [scheduled task](/tasks/scheduled)
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2. **Segment**: Query database for targeting criteria
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3. **Generate**: Create personalized content (AI-generated copy, dynamic images)
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4. **Send**: Deliver via email/SMS APIs with [retry logic](/errors-retrying)
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5. **Track**: Store engagement data, [trigger follow-ups](/triggering#yourtask-trigger)
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Each step is durable and retryable—if email sending fails, Trigger.dev automatically retries without regenerating personalized content.
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Trigger.dev is ideal for marketing automation because [wait.for()](/wait-for) and [wait.until()](/wait-until) let you build drip campaigns with precise delays (hours to weeks) without paying for idle time, [batchTriggerAndWait()](/triggering#yourtask-batchtriggerandwait) sends to thousands in parallel with configurable concurrency, and [idempotency keys](/idempotency) ensure users never receive duplicate messages even when tasks retry.
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## Common marketing workflow patterns
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### Drip Campaigns
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Use scheduled tasks and wait conditions to create sophisticated email sequences:
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- Welcome series for new subscribers
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- Onboarding sequences for new users
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- Re-engagement campaigns for inactive users
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- Product education sequences
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### Behavioral Triggers
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Respond to user actions with personalized campaigns:
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- Cart abandonment recovery
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- Post-purchase follow-up
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- Feature usage-based messaging
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- Milestone celebration campaigns
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### Personalization at Scale
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Generate personalized content for thousands of users:
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- Dynamic email content based on user preferences
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- Personalized product recommendations
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- Custom landing pages for different segments
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- Individualized promotional offers
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### Analytics and Reporting
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Track campaign performance and optimize:
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- A/B test different message variants
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- Monitor engagement metrics across channels
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- Generate automated performance reports
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- Trigger campaigns based on performance thresholds
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## Marketing workflow examples
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<CardGroup cols={2}>
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<Card
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title="Email sequences with Resend"
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icon="book"
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href="/guides/examples/resend-email-sequence"
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>
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Send a sequence of emails over several days using Resend.
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</Card>
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<Card title="React Email" icon="book" href="/guides/examples/react-email">
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Send emails using React Email with JSX email templates.
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</Card>
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<Card title="Stripe webhooks" icon="book" href="/guides/examples/stripe-webhook">
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Trigger campaigns from Stripe payment events and subscription changes.
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</Card>
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<Card
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title="Generate and translate marketing copy"
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icon="book"
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href="/guides/ai-agents/generate-translate-copy"
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>
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Chain prompts to generate and translate copy in multiple languages.
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</Card>
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<Card
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title="Product image generator"
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icon="book"
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href="/guides/example-projects/product-image-generator"
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>
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Transform basic product photos into professional marketing images.
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</Card>
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<Card
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title="Human-in-the-loop workflow"
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icon="book"
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href="/guides/example-projects/human-in-the-loop-workflow"
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>
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Add human approval steps to workflows using ReactFlow and waitpoint tokens.
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</Card>
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</CardGroup>
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<UseCasesCards />

‎docs/guides/use-cases/media-generation.mdx‎

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---
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title: "AI-Powered Media Generation"
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description: "Build reliable AI media generation workflows for images, videos, audio, and more with Trigger.dev. No timeouts, automatic retries, and real-time progress tracking"
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---
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import UseCasesCards from "/snippets/use-cases-cards.mdx";
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## Overview
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AI-powered media generation workflows require handling unpredictable API latencies, managing rate limits, and processing computationally intensive operations. Build reliable AI content generation pipelines in TypeScript with automatic retries, progress tracking, and no timeout limits; perfect for AI image generation, video synthesis, audio creation, and multi-modal content workflows.
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## Basic AI media generation workflow implementation
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A typical AI generation pipeline:
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1. **Input**: Receive prompts, parameters, reference images
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2. **Generate**: Call AI APIs (OpenAI, Replicate, Fal.ai, Stability, etc.)
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3. **Post-process**: Upscale, optimize, apply transformations
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4. **Review**: Human approval or automated quality checks
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5. **Deliver**: Upload to storage, update database
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Each step is durable and retryable—if an AI API call times out, Trigger.dev automatically retries without re-executing expensive prompt generation thanks to [checkpoint-resume](/how-it-works#the-checkpoint-resume-system) and [idempotency](/idempotency).
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Trigger.dev is ideal for AI media generation because [checkpoint-resume](/how-it-works#the-checkpoint-resume-system) pauses during AI API calls (you only pay for active compute, not inference time), no [timeout limits](/runs/max-duration) means generation can take minutes or hours, [batchTriggerAndWait()](/triggering#yourtask-batchtriggerandwait) generates hundreds in parallel with [queue.concurrencyLimit](/queue-concurrency) respecting API rate limits, [metadata.stream()](/runs/metadata#stream) + [realtime](/realtime) stream previews to your frontend, and [wait.for()](/wait-for-token) adds human approval gates for brand safety.
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## AI media generation workflow examples
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<CardGroup cols={2}>
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<Card
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title="Product image generator"
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icon="book"
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href="/guides/example-projects/product-image-generator"
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>
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Transform product photos into professional marketing images using Replicate.
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</Card>
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<Card title="DALL·E 3" icon="book" href="/guides/examples/dall-e3-generate-image">
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Generate images from text prompts using OpenAI's DALL·E 3.
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</Card>
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<Card title="Fal.ai realtime" icon="book" href="/guides/examples/fal-ai-realtime">
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Generate images with Fal.ai and stream progress updates in real-time.
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</Card>
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<Card
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title="Vercel AI SDK image generator"
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icon="book"
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href="/guides/example-projects/vercel-ai-sdk-image-generator"
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>
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Build image generation workflows using Vercel's AI SDK.
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</Card>
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<Card
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title="Meme generator (human-in-the-loop)"
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icon="book"
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href="/guides/example-projects/meme-generator-human-in-the-loop"
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>
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Generate memes with DALL·E 3 and add human approval steps.
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</Card>
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<Card
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title="Deep research agent"
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icon="book"
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href="/guides/example-projects/vercel-ai-sdk-deep-research"
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>
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Build a research agent that generates comprehensive reports with Vercel AI SDK.
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</Card>
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</CardGroup>
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## Production use cases
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<Card title="Icon customer story" href="https://trigger.dev/customers/icon-customer-story">
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Read how Icon uses Trigger.dev to process and generate thousands of AI-powered videos per month for their AI-driven video creation platform.
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</Card>
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## Common AI generation patterns
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### Single Media Generation
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Generate individual AI-powered content on demand:
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- Generate images from text prompts
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- Transform existing images with AI models
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- Create variations of source images
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- Apply AI filters and effects
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### Batch Generation
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Create content at scale for production workflows:
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- Generate hundreds of product images
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- Create personalized marketing assets at scale
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- Generate thumbnail variations for A/B testing
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- Bulk process images through AI models
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### Multi-step AI Pipelines
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Chain multiple AI operations for sophisticated workflows:
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- Prompt generation → Image generation → Upscaling
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- Image generation → Style transfer → Optimization
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- Audio generation → Transcription → Translation
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- Video generation → Thumbnail extraction → Metadata generation
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### Human-in-the-loop
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Add review and approval steps to AI workflows:
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- Generate content → Human review → Publish
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- AI moderation → Flagged content review → Decision
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- Batch generation → Sample review → Approve batch
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- Iterative refinement with human feedback
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<UseCasesCards />

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