> ## Documentation Index
> Fetch the complete documentation index at: https://trigger.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Data processing & ETL workflows

> Learn how to use Trigger.dev for data processing and ETL (Extract, Transform, Load), including web scraping, database synchronization, batch enrichment and more.

## Overview

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.

## Featured examples

> [card] [Realtime CSV importer](https://trigger.dev/docs/guides/example-projects/realtime-csv-importer.md)
>
> Import CSV files with progress streamed live to frontend.

> [card] [Web scraper with BrowserBase](https://trigger.dev/docs/guides/examples/scrape-hacker-news.md)
>
> Scrape websites using BrowserBase and Puppeteer.

> [card] [Supabase database webhooks](https://trigger.dev/docs/guides/frameworks/supabase-edge-functions-database-webhooks.md)
>
> Trigger tasks from Supabase database webhooks.

## Benefits of using Trigger.dev for data processing & ETL workflows

**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.

## Production use cases

> [card] [MagicSchool AI customer story](https://trigger.dev/customers/magicschool-ai-customer-story)
>
> Read how MagicSchool AI uses Trigger.dev to generate insights from millions of student interactions.

> [card] [Comp AI customer story](https://trigger.dev/customers/comp-ai-customer-story)
>
> Read how Comp AI uses Trigger.dev to automate evidence collection at scale, powering their open source, AI-driven compliance platform.

> [card] [Midday customer story](https://trigger.dev/customers/midday-customer-story)
>
> Read how Midday use Trigger.dev to sync large volumes of bank transactions in their financial management platform.

## Example workflow patterns

> [tab] CSV file import
>
> Simple CSV import pipeline. Receives file upload, parses CSV rows, validates data, imports to database with progress tracking.
>
> ```mermaid theme={"theme":"css-variables"}
> graph TB
>     A[importCSV] --> B[parseCSVFile]
>     B --> C[validateRows]
>     C --> D[bulkInsertToDB]
>     D --> E[notifyCompletion]
> ```

> [tab] Multi-source ETL pipeline
>
> **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.
>
> ```mermaid theme={"theme":"css-variables"}
> graph TB
>     A[runETLPipeline] --> B[coordinateExtraction]
>     B --> C[batchTriggerAndWait]
>
>     C --> D[extractFromAPI]
>     C --> E[extractFromDatabase]
>     C --> F[extractFromS3]
>
>     D --> G[transformData]
>     E --> G
>     F --> G
>
>     G --> H[validateData]
>     H --> I[loadToWarehouse]
> ```

> [tab] Parallel web scraping
>
> **Coordinator pattern with browser automation**. Launches headless browsers in parallel to scrape multiple pages, extracts structured data, cleans and normalizes content, stores in database.
>
> ```mermaid theme={"theme":"css-variables"}
> graph TB
>     A[scrapeSite] --> B[coordinateScraping]
>     B --> C[batchTriggerAndWait]
>
>     C --> D[scrapePage1]
>     C --> E[scrapePage2]
>     C --> F[scrapePageN]
>
>     D --> G[cleanData]
>     E --> G
>     F --> G
>
>     G --> H[normalizeData]
>     H --> I[storeInDatabase]
> ```

> [tab] Batch data enrichment
>
> **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.
>
> ```mermaid theme={"theme":"css-variables"}
> graph TB
>     A[enrichRecords] --> B[fetchRecordsToEnrich]
>     B --> C[coordinateEnrichment]
>     C --> D[batchTriggerAndWait]
>
>     D --> E[enrichRecord1]
>     D --> F[enrichRecord2]
>     D --> G[enrichRecordN]
>
>     E --> H[validateEnrichedData]
>     F --> H
>     G --> H
>
>     H --> I[updateDatabase]
> ```

## Featured use cases

> [card] [Data processing & ETL workflows](https://trigger.dev/docs/guides/use-cases/data-processing-etl.md)
>
> Build complex data pipelines that process large datasets without timeouts.

> [card] [Media processing workflows](https://trigger.dev/docs/guides/use-cases/media-processing.md)
>
> Batch process videos, images, audio, and documents with no execution time limits.

> [card] [AI media generation workflows](https://trigger.dev/docs/guides/use-cases/media-generation.md)
>
> Generate images, videos, audio, documents and other media using AI models.

> [card] [Marketing workflows](https://trigger.dev/docs/guides/use-cases/marketing.md)
>
> Build drip campaigns, create marketing content, and orchestrate multi-channel campaigns.


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