# Migrating from a Monolithic Orchestrator to Apache Airflow

DevFeed: [Migrating from a Monolithic Orchestrator to Apache Airflow](<https://devfeed.tech/articles/migrating-from-a-monolithic-orchestrator-to-apache-airflow-30518.md>)

Original publisher: [Read original article](<https://medium.com/helpshift-engineering/migrating-from-a-monolithic-orchestrator-to-apache-airflow-30fde94bcdc0?source=rss----3229f31ca4f4---4>)

Author: Poorva Patil

Published: 2026-06-03T09:20:17Z

Content type: article

Language: en

Sources: [Helpshift](<https://devfeed.tech/sources/helpshift.md>)

Topics: [airflow](<https://devfeed.tech/topics/airflow.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Clojure](<https://devfeed.tech/topics/clojure.md>), [Python](<https://devfeed.tech/topics/python.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [apache-airflow](<https://devfeed.tech/tags/apache-airflow.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [aws](<https://devfeed.tech/tags/aws.md>), [clojure](<https://devfeed.tech/tags/clojure.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [etl](<https://devfeed.tech/tags/etl.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [monolithic-architecture](<https://devfeed.tech/tags/monolithic-architecture.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [python](<https://devfeed.tech/tags/python.md>), [software-architecture](<https://devfeed.tech/tags/software-architecture.md>)

## AI overview

The article describes a migration from a monolithic Clojure-based scheduler to Apache Airflow. The legacy system coupled event pipelines, dependencies, and an EMR step, making workflows difficult to understand, maintain, monitor, and debug. The authors report that Airflow's Python-based DAGs made dependencies, retries, and scheduling more readable and helped improve onboarding and maintainability.

## Source excerpt

Photo by Corinne Kutz on UnsplashBefore we knew better Our orchestration system started as a simple internal solution to manage event pipelines and trigger downstream jobs. Over time, as more workflows and dependencies were added, it gradually evolved into a tightly coupled monolithic scheduler that became increasingly difficult to understand and maintain. Understanding how a workflow executed often meant looking through multiple files, configurations and database tables. For newer team members, onboarding into the system took time because much of the workflow context was distributed across different parts of the codebase. Even relatively small changes required careful coordination to ensure existing pipelines continued to work as expected. Similarly, debugging typically involved manually tracing logs and rerunning jobs to better understand execution behavior. Limitations of our legacy design We had a monolithic architecture written in Clojure that bundled all our event pipelines together, added dependencies between them and triggered a Lambda function. Legacy Workflow This Lambda function added a single monolithic step to the EMR cluster. If there was an issue in any one of the pipelines, the entire flow would fail due to the single step on the cluster. We did not have step-wise monitoring in the old design, so during on-call situations it became very difficult to identify which part of the pipeline was causing the issue. Photo by Tim Gouw on Unsplash There was no single place to answer basic questions like: What runs first? What happens if this step fails? How do I re-run just one part safely? The scheduler worked, but it was hard to understand, hard to maintain and even harder to explain. That's when we realized we needed a better way. What we actually needed Our aim was less about fancy scheduling features and more about making our daily work easier and more reliable. Simpler onboarding, less mental overhead Our existing step scheduler was built in Clojure and c