The Pros and Cons of Airflow Against AWS GlueAdmin
AWS Glue is offered as a code-based, non-server reliant ETL option in contrast to typical drag-and-drop platforms, mainly implemented by Amazon ecosystem users. Seeing as Glue comes with brand new features as compared to orthodox tooling it is no surprise that it is often set in competition with Apache Airflow, especially by members with a preference for code based ETL tools that are flexible.
However, everything considered there are some core distinctions that separate the two and have been outlined below under four criteria. Let’s take a closer look at how the two compare with each other.
The first thing that comes to mind with AWS Glue is how it is serverless by default and won’t need the user to allocate resources at any point in time. With the help of crawlers, Glue goes through your AWS data stores and fills the AWS Glue Data Catalogue by searching for a specific data scheme.
ETL jobs are directed from this Data Catalogue while Glue uses this as a data source for jobs. In order to make sure that crawlers are working correctly the data has to be in a particular format. With this incapacitation in place it is not uncommon to make use of Apache Airflow to structure data so that Glue can accurately crawl it.
The best feature with Airflow is its in-built options of three executors, every one of them designed to oversee operations for varying use cases. The executors are efficient in that their design allows them to optimize resources utilization as per your workflows and also backs up serverless task execution through Kubernetes Executor for instance.
Apache Spark is the engine that AWS Glue uses to get data processing underway. A number of reasons exist as to why it is not the same as vanilla Spark. The most notable is the implementation of a “dynamic frame” as opposed to a “data frame” in Spark that attaches a variety of extra Glue means such as Relationalize, ApplyMapping and ResolveChoice.
Airflow manages to oversee activities using Python code and no other dependencies. In turn this can be tacked onto other services like Apache Spark with the help of formally backed up community contributed operators. Such operators make it possible for elasticity in terms of third-party APIs, infrastructure layers and data systems.
Data processing is especially easy with AWS Glue that was created in such a way. Especially because of Glue’s reliance on the AWS ecosystem many users opt to make use of both through Airflow in order to manage data pipelines using information from outside AWS (e.g. extracting records from an API and storing it in S3) since AWS Glue is not equipped to handle those tasks.
There are no limits on how you’d like to use Airflow since it was designed keeping in mind the enablement of workflow elasticity. ETL may be the most common use case however Airflow is also commonly utilized to power features inside an app using a number of other APIs, train ML models and examine the state of different systems and send out notifications via email/slack.
This is a completely managed ETL service created for compatibility in terms of extended AWS services which does not allow implementation on-premise or other cloud environments. AWS Glue users can implement Glue to make ready and load data needed for analytics. Glue records this data and allows ETL jobs to make use of it. A good choice for ETL is to have ample data stored in AWS.
As an open-source workflow coordination tool which is cloud independent Airflow comes with three different execution methods each with their own use case strengths. Airflow can extend itself to encompass much more complicated workflows and dependencies while also working as a basic ETL tool. With its expanded community and large availability of supported use cases there are several pre-constructed tools across cloud providers.
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