![]() ![]() ![]() Open-source community-Airflow is free and has a large community of active users.Ease of use-you only need a little python knowledge for understanding.And also make sure each task gets the required resources. It will make sure that each task of your data pipeline will get executed in the correct order. It is a workflow engine that will easily schedule and run your complex data pipelines. What is Apache Airflow?Īpache Airflow is a robust scheduler for programmatically authoring, scheduling, and monitoring workflows. Apache Airflow is written in Python, which enables flexibility and robustness. For more, check out their documentation, or peruse this useful list of Airflow plugins to see if your data sources are represented.It is one of the most popular open-source workflow management platforms within data engineering to manage the automation of tasks and their workflows. This was just a taste of Airflow’s capabilities. If you need your workflows to kick off every time a file lands on an FTP server, or when a customer places an order, or other unpredictable triggers, you may want to consider an alternative orchestrator. While Airflow has “Sensors” that attempt to bring event-driven behavior to the table, these will still only trigger once per the scheduled time period of the workflow. Airflow Isn’t Event-DrivenĪirflow works best when its workflows are scheduled to execute at specific times. It’s also not recommended to share data between workflows, or even between tasks within a workflow - make sure you have a landing zone for your data in mind, like AWS S3 or Azure Blob, so it can be passed around as necessary. Airflow is designed to boss around other services, not do the heavy lifting itself. You can certainly use Airflow to schedule big data processes in systems like BigQuery or Spark, but don’t try to process large amounts of data within your Python code. You can probably hack a dynamic workflow into working, but you may encounter odd UI bugs, or more difficulty getting it to work than you expected. Despite being a fairly common use case, Airflow isn’t really built for a job like this it expects linear, straightforward, pre-defined workflows, with no surprises at runtime. What do I mean by “dynamic”? Let’s say you want to retrieve data from a source, and based on how much data you find, start a dynamically-generated number of new tasks to process that data. There’s even an interface for running ad-hoc queries against any database Airflow is connected to… including its own internals. Dependency trees, gantt charts, and execution logs are just a couple clicks away. Intuitive User InterfaceĪ browser-based, color-coded dashboard gives you instant insight into the status of all your workflows. Need to connect to Google Analytics? Redshift? Salesforce? Chances are, someone else did too, and has shared a plugin to do it. Community Drivenīecause of its popularity and open-source pedigree, Airflow has a large, active community of users and contributors, and just as importantly, loads of free plugins. What Airflow Is Built Around Workflows-As-CodeĪll Airflow workflows are written in Python, giving you a wealth of advantages over config file or pure GUI-based orchestrators: version control, flexibility, and Python’s massive catalog of data science libraries. But just because you can, does that mean you should? In this article I’ll summarize what makes Airflow useful… and where it falls short. It has become especially popular for automating ETL and other data analytics pipelines, but it can be used for almost any kind of programmatic task. Apache Airflow is a platform originally developed by Airbnb for authoring, scheduling, and monitoring workflows. ![]()
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