apache airflow

1. why apache airflow was invented

airflow was created because managing complex data workflows manually is hard. in small projects, cron jobs + scripts might work. but in real-world data engineering, you often need to:

• run hundreds of tasks in a specific order
• retry tasks if they fail
• schedule tasks at different frequencies
• track what has run, what failed, and why
• alert the team automatically on failures
• share the workflow with others in a clear, visual way

doing all this manually with cron and scripts becomes messy, error-prone, and unscalable. airflow provides a framework for this, so you don’t reinvent the wheel.

2. what problem it solves (vs cron/scripts)

in short: airflow makes data workflows maintainable, scalable, and observable.

3. dags (directed acyclic graphs)

a dag is just a fancy way to say: a collection of tasks with dependencies, without loops.

• “directed” = tasks have a clear order
• “acyclic” = tasks cannot depend on themselves indirectly (no infinite loops)

example:

• task a: extract data from api
• task b: clean the data
• task c: load into database

the dag would be: a → b → c airflow will automatically run them in the correct order, retry failed tasks, and alert you if something breaks.

4. orchestration

orchestration means: managing how and when tasks run, and in what order.

real-world analogy: imagine an orchestra:

• musicians = tasks
• conductor = airflow
• sheet music = dag
• performance = the workflow execution

without a conductor, everyone might play at the wrong time (like scripts with cron). airflow ensures everything happens in the right order and handles errors.

5. simple real-world example

say a company wants a daily sales report:

1. extract sales data from crm api (task a)
2. clean missing fields and convert currencies (task b)
3. aggregate daily totals per region (task c)
4. load final report into data warehouse (task d)
5. send email to managers (task e)

dag: a → b → c → d → e

with airflow:

• all tasks are visualized
• if task b fails, airflow retries it automatically
• if email fails, it alerts the team
• you can run this workflow daily at 6am automatically

if you used cron + scripts: you’d need separate cron jobs for each step, custom error handling, custom logging, and custom alerts. it quickly becomes messy.

before airflow (and other modern orchestrators), companies mostly used a mix of cron, bash/python scripts, and homegrown tools. here’s how it looked historically

1. cron + scripts

• cron schedules tasks at fixed times
• bash/python scripts did the actual work (extract/transform/load)
• logging was usually manual (writing to files)
• error handling was custom: sometimes emails on failure, sometimes nothing

problems:

• hard to see dependencies between tasks
• retrying failed tasks was manual
• scaling to hundreds of jobs across many servers was messy
• tracking history was hard

2. custom workflow managers

some companies built internal tools to manage workflows:

• scheduler + logging + retry logic
• some could visualize dependencies
• usually specific to that company’s stack

problems:

• expensive to build and maintain
• tied to internal tech (hard to share externally)
• harder to extend

3. enterprise ETL tools

big companies sometimes used commercial ETL/orchestration tools like:

• informatica
• talend
• pentaho

these handled scheduling, retries, logging, and visual workflows, but:

• expensive licenses
• heavy setups
• less flexible than writing code