Data Extraction and ETL Developer with Apache Airflow Salary in 2024

Where is Apache Airflow used?

Alert-O-Matic for Fickle Servers

• Imagine a digital babysitter that pokes your servers when they nap on the job. Airflow sends out alerts faster than a caffeinated squirrel when systems doze off.

Auto-Pilot for Data Jugglers

• For data wranglers tired of the circus act, Airflow automates complex data workflows so they can chill and watch the code do backflips.

The Scheduler That's Never Late

• No more late-night coding! This stickler for punctuality schedules tasks with the precision of a Swiss watch made by robots.

Insightful ETL Alchemist

• Likes turning data-potatoes into golden insights. Performing Extract-Transform-Load (ETL) spells without breaking a sweat is its party trick!

Apache Airflow Alternatives

Luigi by Spotify

Luigi is a Python-centric workflow management system which handles dependency resolution, workflow management, and visualization. It's primarily used for batch job orchestration.

import luigi

class MyTask(luigi.Task):
    def requires(self):
        return AnotherTask()

    def output(self):
        return luigi.LocalTarget('my_output.txt')

    def run(self):
        with self.output().open('w') as f:
            f.write('Hello World')

• Pros:
  
• Simpler to debug with Python stack traces.


• Better for Python deep integration, less overhead than Airflow.


• Static visualization of task dependencies.

Cons:

• Lacks a user-friendly UI out of the box.


• Less scalable compared to Airflow.


• Community and ecosystem not as large as Airflow's.

Argo Workflows

Argo is a Kubernetes-native workflow engine for complex job orchestration, including parallel and sequential workflows. It is cloud-native and supports DAGs as well.

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: hello-world-
spec:
  entrypoint: whalesay
  templates:
  - name: whalesay
    container:
      image: docker/whalesay:latest
      command: [cowsay]
      args: ["hello world"]

• Pros:
  
• Deep Kubernetes integration, great for microservices architectures.


• Powerful cloud-native features and scalability.


• Strong CI/CD and GitOps patterns support.

Cons:

• Steep learning curve for non-Kubernetes users.


• More complex setup than Airflow for simpler workflows.


• Primarily container-based, which might not fit all use cases.

Prefect

Prefect is a workflow management system with a focus on simplicity and ease-of-use. It's Python-based and offers robust scheduling and orchestration of complex dataflows.

from prefect import task, Flow

@task
def say_hello():
    print("Hello, world!")

with Flow("My first flow!") as flow:
    say_hello()

flow.run()

• Pros:
  
• User-friendly API with a focus on simplicity.


• Hybrid execution model allows for local or cloud task runs.


• Intelligent state handling and automatic retries for failed tasks.

Cons:

• Younger project with a less mature ecosystem.


• Cloud version is not free, which might be a limitation for some teams.


• May not be as extensible as Airflow for unique or complex tasks.

Quick Facts about Apache Airflow

Airflow's Inception: Code, Coffee, and a Bored Engineer

Originating from the depths of Airbnb in 2014, the brainchild of Maxime Beauchemin, Apache Airflow swooped into the tech scene. Designed to orchestrate complex computational workflows with the elegance of a maestro, it quickly became the tool of choice for simplifying the convoluted symphonies of data-pipeline tasks, saving the sanity of data engineers everywhere.

It's Pythonic! Airflow's Snakes on a Workflow

What's Python got to do with it? Everything! Airflow is enamored with Python, allowing you to define your system's bone structure (aka the workflow) in pure, sweet Python code. This "Pythonic" approach trounced rigid configuration files, beckoning a new era where coding your pipeline was as delightful as binge-watching cat videos.

# Example of a simple Airflow DAG
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from datetime import datetime

default_args = {
    'start_date': datetime(2021, 1, 1),
}
with DAG('my_cool_dag', default_args=default_args, schedule_interval='@daily') as dag:
    task1 = DummyOperator(task_id='task_1')
    task2 = DummyOperator(task_id='task_2')

    task1 >> task2  # Defines tasks sequence

From Zero to Hero: Airflow's Version Saga

Like a startup hero's journey, Airflow has had its share of versions. Kicking off as open-source in 2015, an initial v1.0 landed in the public arena. But it didn't stop there; each iteration brought its bucket full of features until the Airflow 2.0 release in December 2020, taking a gigantic leap with the full might of a Scheduler Overhaul and overpowering features like smart Sensors!

What is the difference between Junior, Middle, Senior and Expert Apache Airflow developer?

Top 10 Apache Airflow Related Tech

1. 
   

   Python

   
   

   Like peanut butter to jelly, Python is to Airflow – they're a match made in data pipeline heaven! This dynamic and flexible language is where you scribble down your workflow orchestration masterpieces. It's not just a snake folks, it's the very lifeblood of Airflow scripts, DAGs, and custom operators. It's what you'll need to whisper sweet nothings to your data as it prances from one task to another.

   
   

   
   def greet():
       print("Hello, Airflow!")
   

   
   



2. 
   

   Bash

   
   

   Get ready to bash it out! The Bash command-line is like the Swiss Army knife in your Airflow utility belt. Sometimes you just need to fire off a quick script to poke your data or give your server a gentle nudge. It's quick, it's dirty, and it gets the job done when Python feels like overkill.

   
   

   
   echo "Airflow loves Bash!"
   

   
   



3. 
   

   SQL & Databases

   
   

   SQ-HELL yeah! If data is the kingdom, SQL is the key to the treasure chest. When Airflow needs to interact with databases - MySQL, PostgreSQL, or a fancy NoSQL like MongoDB, you’ll need SQL prowess to query, insert, and wrangle this wild data. It's like playing whack-a-mole with your data, but with more structured queries and less mole-whacking.

   
   

   
   SELECT * FROM airflow_magic WHERE status = 'awesome';
   

   
   



4. 
   

   Docker

   
   

   Would you like some containerization with that data pipeline? Docker wraps up your Airflow environment in a neat little container package handy for shipping away to any port, known as production or staging. One might say it's the Tupperware of the software world – it keeps your Airflow fresh and ready-to-go!

   
   

   
   docker run -d -p 8080:8080 apache/airflow webserver
   

   
   



5. 
   

   Kubernetes

   
   

   Kubernetes (a.k.a. K8s) is where Airflow likes to play when scaling is the game. Spin up pods faster than you can say "autoscaling," manage your container fleet and let your data workflows bask in the glory of orchestrated harmony. Just don't get lost in the YAML; it's a jungle out there!

   
   

   
   kubectl apply -f airflow_cluster.yaml
   

   
   



6. 
   

   Amazon Web Services (AWS)

   
   

   The cloud's the limit with AWS! Airflow loves cloud platforms, and AWS is like an amusement park for your data ops. From S3 for storage puddle jumping to EC2 for compute carousel rides, you’ll use AWS for practically every data thrill ride imaginable. Buckle up; it's a wild cloud chase!

   
   

   
   aws s3 ls s3://your-airflow-bucket
   

   
   



7. 
   

   Apache Hadoop

   
   

   Giant yellow elephant in the room: Apache Hadoop. Used alongside Airflow for map-reducing your way through big data like a hot knife through butter. Pair it with Airflow, and you've got yourself a data processing tag-team ready to take on terabytes of teetering tasks.

   
   

   
   hadoop fs -ls /user/airflow/dags
   

   
   



8. 
   

   Git

   
   

   Remember folks, GIT is not just a British term of endearment. It's your version-controlled sanctuary for Airflow DAGs and scripts. Push, pull, and commit your way to collaboration nirvana, while never losing a decimal of your valuable code. It's like a diary for your development journey, but with less teenage angst and more code.

   
   

   
   git commit -m "Add new DAG for data wrangling"
   

   
   



9. 
   

   CI/CD Tools (Jenkins, GitHub Actions)

   
   

   CI/CD tools are like the pit crew for your Airflow race car - they get your workflow engines fine-tuned and onto the track smoothly. Be it Jenkins with its endless pipeline possibilities or GitHub Actions for that sweet sweet integrated experience, your path to continuous integration and deployment is clear. Race to production without a pit stop!

   
   

   
   pipeline {
     agent any
     stages {
       stage('Deploy Airflow') {
         steps {
           script {
             // Deploy Airflow steps
           }
         }
       }
     }
   }
   

   
   



10. 
    

    Apache Spark

    
    

    Spark is to data what lightsabers are to a Jedi. This fast, in-memory data processing tool can turn your Airflow DAGs into a symphony of speedy executions. It slices, it dices, and it handles big data like a dream. Plus, when paired with Airflow, you can schedule and monitor your Spark jobs with the grace of an orchestral conductor.

    
    

    
    spark-submit --class your.class.here --master local app.jar