Pipeline Architecture
Every automation pipeline follows this five-stage pattern
What starts the pipeline
Cron schedule, webhook, database change, manual command
Gather input data
API calls, database queries, web scraping, MCP reads
Process and enrich
Filter, deduplicate, AI summarize, normalize formats
Execute the output
Write to DB, send messages, publish content, update sheets
Log and notify
Console logs, Slack alerts, dashboard updates, error handling
Individual automations are useful. Pipelines that chain multiple automations together are major. This module teaches you to build complete, end-to-end systems using everything you've learned.
A pipeline is a sequence of stages where the output of each stage feeds into the next. The Trigger-Fetch-Transform-Act-Report pattern provides a universal template. Every production automation, from job aggregation bots to content publishing systems to lead enrichment workflows, follows this five-stage architecture. The key engineering principle is that each stage should be independent and idempotent: if the Transform stage fails, you can re-run it without duplicating the Fetch results. This modularity is what separates hobby automations that break every week from production systems that run for months without intervention.
Pipeline Case Studies
Four production pipelines showing the Trigger-Fetch-Transform-Act-Report pattern