Airflow vs AWS Glue: What are the differences?
Developers describe Airflow as "A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb". Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. On the other hand, AWS Glue is detailed as "Fully managed extract, transform, and load (ETL) service". A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
Airflow can be classified as a tool in the "Workflow Manager" category, while AWS Glue is grouped under "Big Data Tools".
Some of the features offered by Airflow are:
- Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
- Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
- Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.
On the other hand, AWS Glue provides the following key features:
- Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.
- Integrated - AWS Glue is integrated across a wide range of AWS services.
- Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.
Airflow is an open source tool with 13K GitHub stars and 4.72K GitHub forks. Here's a link to Airflow's open source repository on GitHub.
According to the StackShare community, Airflow has a broader approval, being mentioned in 72 company stacks & 33 developers stacks; compared to AWS Glue, which is listed in 13 company stacks and 7 developer stacks.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Airflow?
What is AWS Glue?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions