# Data processing & ETL workflows

> Source: https://trigger.dev/docs/guides/use-cases/data-processing-etl

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#overview)

Overview
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Build complex data pipelines that process large datasets without timeouts. Handle streaming analytics, batch enrichment, web scraping, database sync, and file processing with automatic retries and progress tracking.

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#featured-examples)

Featured examples
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Realtime CSV importer
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Import CSV files with progress streamed live to frontend.

Web scraper with BrowserBase
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Scrape websites using BrowserBase and Puppeteer.

Supabase database webhooks
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Trigger tasks from Supabase database webhooks.

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#benefits-of-using-trigger-dev-for-data-processing-&-etl-workflows)

Benefits of using Trigger.dev for data processing & ETL workflows
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**Process datasets for hours without timeouts:** Handle multi-hour transformations, large file processing, or complete database exports. No execution time limits. **Parallel processing with built-in rate limiting:** Process thousands of records simultaneously while respecting API rate limits. Scale efficiently without overwhelming downstream services. **Stream progress to your users in real-time:** Show row-by-row processing status updating live in your dashboard. Users see exactly where processing is and how long remains.

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#production-use-cases)

Production use cases
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MagicSchool AI customer story
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Read how MagicSchool AI uses Trigger.dev to generate insights from millions of student interactions.

Comp AI customer story
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Read how Comp AI uses Trigger.dev to automate evidence collection at scale, powering their open source, AI-driven compliance platform.

Midday customer story
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Read how Midday use Trigger.dev to sync large volumes of bank transactions in their financial management platform.

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#example-workflow-patterns)

Example workflow patterns
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*   CSV file import
    
*   Multi-source ETL pipeline
    
*   Parallel web scraping
    
*   Batch data enrichment
    

Simple CSV import pipeline. Receives file upload, parses CSV rows, validates data, imports to database with progress tracking.

**Coordinator pattern with parallel extraction**. Batch triggers parallel extraction from multiple sources (APIs, databases, S3), transforms and validates data, loads to data warehouse with monitoring.

**Coordinator pattern with browser automation**. Launches headless browsers in parallel to scrape multiple pages, extracts structured data, cleans and normalizes content, stores in database.

**Coordinator pattern with rate limiting**. Fetches records needing enrichment, batch triggers parallel API calls with configurable concurrency to respect rate limits, validates enriched data, updates database.

[​](https://trigger.dev/docs/guides/use-cases/data-processing-etl#featured-use-cases)

Featured use cases
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Data processing & ETL workflows
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Build complex data pipelines that process large datasets without timeouts.

Media processing workflows
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Batch process videos, images, audio, and documents with no execution time limits.

AI media generation workflows
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Generate images, videos, audio, documents and other media using AI models.

Marketing workflows
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Build drip campaigns, create marketing content, and orchestrate multi-channel campaigns.

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