Everything about Apache Airflow
🎬S3:E10 Something's in the Air
Hey, TechFlixers!
In episode 8, we talked about Apache Flink, a popular framework used for processing data streams (basically, data ingestion jobs).
In episode 9, we explored how OLAP databases like Apache Druid and StarRocks can help build fast analytics platforms.
In today’s episode, we explore another important piece of a data platform—Apache Airflow.
Apache Flink handles the data processing, performing complex computations on the fly, while StarRocks (an OLAP DB) efficiently manages and queries the stored data. In this ecosystem, Apache Airflow plays a crucial role as an orchestrator (a tool that coordinates the execution of various tasks and processes).
🚀 Power Up
The Basics of Airflow
With the surge in data-driven processes across industries, there’s a growing trend of migrating to Apache Airflow. But what exactly is it, and why is it becoming so popular?
Apache Airflow is an open-source tool designed for workflow automation. Think of it as a scheduler and organizer for tasks that must be carried out in a specific order, especially when dealing with data. It’s particularly useful for managing complex data pipelines, where you need to ensure that tasks happen in a specific sequence and nothing falls through the cracks.
For instance, it’s commonly used to automate ETL (Extract, Transform, Load) processes—where data is extracted from one system, transformed into a useful format, and loaded into another system for analysis or storage.
In Airflow, a task is the smallest unit of work that it manages. Each task represents a single operation, such as running a script, executing a data processing job, or sending an email.
The real power of Airflow comes from its use of Directed Acyclic Graphs (DAGs). A DAG is like a flowchart that maps out each step in a process and ensures that tasks are executed in the correct order. With Airflow, developers can easily define these workflows, manage dependencies between tasks, handle retries if something fails, and set up notifications to keep everyone informed.
In short, a DAG is something that organizes tasks together and determines how they should be run.
Let’s put this into context with an example. Imagine you’re running a service that delivers daily weather updates to users. Here’s how Airflow might handle the workflow:
Fetch Weather Data: Airflow might use a Python script or an API call to gather the data from various weather sources.
Process and Clean the Data: Airflow could trigger an Apache Flink job (or another tool like Spark or a custom Python script) to process and clean the data. Airflow ensures that this job runs when it’s supposed to and monitors it for success or failure.
Analyze Trends: This might involve another Flink job, a machine learning model, or a different analysis tool that Airflow triggers and manages.
Send Forecasts to Users: Airflow could execute another script or send messages through a service like AWS Lambda, which handles the distribution of the forecasts.
So, Airflow's role is to manage the execution of these tasks, making sure they run in the correct order, handling retries if a task fails, and sending notifications if something goes wrong. The actual work—like processing the data or analyzing trends—uses other tools or custom code that Airflow integrates with.
Learning with an Example
I have built an actual ETL pipeline using Airflow to demonstrate its use. Check out this article on Medium (It’s a friend link, so it's free for you to read even if you are not a Medium member!) to explore it further. We build a unique anime-themed ETL pipeline and run it using Airflow.
🔦 Spotlight
Within this context, let’s look at a new tool released by Google to help migrate seamlessly to Airflow from older scheduler systems.
📢 Google Open Source: DAGify: Accelerate Your Journey from Control-M to Apache Airflow
DAGify is a free tool that helps organizations switch from their old enterprise scheduler, Control-M, to the more popular and open-source Apache Airflow.
This transition can be complicated because of all the job definitions that need to be converted.
DAGify makes this process faster and less error-prone by automatically converting Control-M XML files into Airflow's native format.
It uses templates to adapt to different configurations and requirements, ensuring that the original workflow is preserved.
DAGify also integrates with Google Cloud Composer, which provides a managed Airflow experience, making it even easier to migrate workflows to a cloud-native environment.
That’s it for this episode. We have covered the trilogy of technologies often used in building data platforms—Flink, OLAP DBs, and Airflow. With this foundation, you are all set to dive deeper into the world of data engineering.
Until next time!