Data processing & ETL workflows

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

Realtime CSV importer

Import CSV files with progress streamed live to frontend.

Web scraper with BrowserBase

Scrape websites using BrowserBase and Puppeteer.

Supabase database webhooks

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

MagicSchool AI customer story

Read how MagicSchool AI uses Trigger.dev to generate insights from millions of student interactions.

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.

Midday customer story

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

Example workflow patterns

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.

Featured use cases