AWS Glue vs. Apache Airflow: A Comparative Outlook

Integrating data scattered across different databases or cloud services is the first step towards getting the data ready to derive business value. Data integration is not a single-step process and often involves a complex sequence of activities. Designing, implementing, and orchestrating extract, transform, and load (ETL) workflows is a tedious multiple-step part of this work.

Many tools and frameworks exist to implement these workflows, with more emerging daily.  Some offer overlapping functionalities, and it is often difficult to track when to use what. AWS Glue and Apache Airflow are two such tools that offer some overlapping functionalities. Yet both are designed to solve entirely different problems. This article compares the two and explores why organizations choose one over the other.  

Five Differences Between AWS Glue and Apache Airflow

Despite some overlapping features, AWS Glue and Apache Airflow are very different under the hood. So choosing between them depends a great deal on the specific use case. Before delving deeper, let’s review some key differences between these two tools that help depict their suitability for different use cases. We will reference the following throughout the article. 

When to use: AWS Glue vs. Apache Airflow

AWS Glue is a fully managed data integration service from Amazon. It helps data engineers discover and extract data from various sources, combine them, transform them, and load them into data warehouses or data lakes. Think of it as an all-in-one ETL or ELT tool. If your ETL jobs do not have complex dependencies and there’s a sole desire for an end-to-end data transformation and migration solution, consider using AWS Glue.  

Consider using Apache Airflow if your organization has complex data pipelines with many workflow dependencies. It’s a great tool to schedule and orchestrate batch data jobs running on various technologies into end-to-end data pipelines. Airflow provides out-of-the-box operators to interact with popular ETL tools and allows developers to write custom code to trigger any tool Python interacts with. 

1. What is your purpose? 

Workflow Orchestration 

Airflow is a workflow orchestration tool that helps developers automate a complex sequence of tasks and visualize it through an intuitive user interface. Unlike most schedulers, it chains complicated ETL workflow dependencies into directed acyclic graphs (DAGs) comprised of tasks to simplify creating, running, and monitoring end-to-end data pipelines. This enables a user to rerun batch ETL pipelines that may have failed. Such drives its flexibility to integrate and deploy single or multiple data sources and processing frameworks to larger workflows. For example, only a specific job will run if an upstream job fails; else, if all upstream jobs succeed, a different set of tasks will run.

ETL Framework

Even though Airflow can act as the backbone of a data integration system, the actual data processing is implemented by external services like Spark and Snowflake. Airflow just orchestrates tasks that are implemented based on third-party data processing frameworks. Hence, an organization working with multiple data processing frameworks with complicated routing logic should consider using AWS Glue to orchestrate its workflows. Glue relies on Apache Spark for all its data processing requirements. Additionally, propelling AWS Glue as the preferred choice for developers that require a completely managed data processing solution because they can use custom scripts using PySpark. 

AWS Glue’s all-in-one ETL framework includes data discovery, transformation, and workflow management. It has its own processing framework, metadata management system, and workflow management system. Glue’s workflow management is not as generic as Airflow’s and intends to be used only with Glue processing functions such as Glue Data Catalog, Glue Studio, and Glue DataBrew. So if you are not particular about the open-source nature of the frameworks in your architecture – consider using AWS Glue. 

2. What is your preferred infrastructure?

Server-Based 

Airflow installs on on-premises servers or cloud virtual machines. The servers are visible to the end users and require some effort to maintain the installation. Yet, most cloud providers offer completely managed services based on Airflow: Amazon Managed Workflows for Apache Airflow (MWAA) and Astronomer are examples of this. 

Serverless Platform

AWS Glue is a serverless ETL platform. There is no installation sequence involved, and maintaining Glue does not require infrastructure knowledge. However, engineers still must define the network and security policies to keep the system secure.

3. What is your preferred licensing model?

Open Source 

Airflow is entirely open source and free to use. Anyone can download Airflow, deploy it on their servers, sell it as a service, or modify it as they wish. Such makes it ideal for organizations that desire greater control of everything in their data platform.

Proprietary 

In contrast, AWS Glue is a proprietary service by Amazon. The source is closed, and it is not free to use. No modifications to the base framework are possible. So, if you want to leverage the benefits that cloud infrastructure provides – such as pay-as-you-go, scale, availability, security, etc. – consider using AWS Glue. 

4. What degree of flexibility do you need?

Process jobs outside of AWS ecosystem

Since Apache Airflow is just a facilitator of any job (i.e., Spark, Hive, API calls, or even custom applications), it offers more flexibility than Glue in extraction and transformation jobs. In addition to Apache Spark, Airflow can orchestrate jobs based on many tools, such as Presto. Airflow can also work with managed services like Google Dataflow. In short, Airflow does not lock one into the AWS Ecosystem.

Process jobs within the AWS ecosystem only

On the other hand, Glue uses Apache Spark for all its data processing requirements. It cannot use services from different cloud providers. Thus, if you are happy with the AWS ecosystem and do not object to being locked to one cloud provider, Glue is the better option. It can pull data from all AWS-managed services (i.e., S3, RDS, Redshift) and external sources that support Java Database Connectivity (JDBC) – alleviating all the complexities of connecting different data sources and providing a unified method for dealing with all data from a single platform. 

Apache Airflow and AWS Glue architectures

5. Monitoring and Logging

Apache Airflow

Airflow visualizes which ETL jobs succeeded, failed, and are currently running much better than a tool like Glue, where users can only view one job run at a time. A user can rerun failed jobs much more easily using Airflow than Glue via its intuitive UI. 

Logs output by the Airflow webserver, scheduler, and workers are written to the local filesystem by default. These logs can be pushed to cloud services like S3 and Google Cloud Storage using community-written handlers. A log aggregator like Fluentd can help collect these logs and help monitor workloads in production. Hence, Airflow is the better debugger. 

Guide to Metadata-Driven Integration

FREE DOWNLOAD

Learn how to overcome constraints in the evolving data integration landscape

Shift data architecture fundamentals to a metadata-driven design

Implement metadata in your data flows to deliver data at time-of-use

AWS Glue

Glue inherits AWS Cloudwatch’s comprehensive application and infrastructure monitoring abilities and allows real-time viewing of logs. Since Apache Spark is the foundation of Glue, most log entries are from Spark executors and drivers; Cloudwatch collects these logs every five (5) seconds. There is no need for a separate log aggregation framework in the case of Glue.

Is it possible to use both?

Organizations that value the serverless data transformation capabilities of Glue but do not want to be limited only to it prefer to use both – AWS Glue and Apache Airflow – simultaneously. Airflow provides Glue operators, hooks, and sensors – enabling airflow tasks to execute Glue processes. 

For instance, one may leverage Glue’s Crawlers that automatically scan defined data locations, generate information about the columns and fields wherever possible, and then upload it to Glue’s Data Catalog – where the metadata is maintained. The content of the catalog can be accessed via an Airflow hook and used as needed within a larger sequence of tasks.

With Nexla’s Data Operations Platform, you can automate such workflows with ease. Using no-code, their powerful and intuitive UI enables you to create, integrate, prepare, validate, and enrich your data and then provides data to any partner company within your ecosystem. 

Empowering Data Engineering Teams

Free Strategy
Session

Platform	Data Extraction	Data Warehousing	No-Code Automation	Auto-Generated Connectors	Metadata-driven	Multi-Speed Data Integration
Informatica	✔	✔
Fivetran	✔	✔	✔
Nexla	✔	✔	✔	✔	✔	✔

Conclusion

AWS Glue and Apache Airflow are both frameworks that can help developers design and facilitate data transformation pipelines. 

While Airflow adopts a flexible approach emphasizing workflow management, Glue packs all the features required to build an ETL pipeline into a single service. Airflow’s flexibility makes it popular for use cases that require complex job sequences and execution frameworks other than Spark. On the other hand, Glue provides everything needed for quickly setting up a data platform. 

So if you want a tool to build pipelines for an AWS ecosystem very quickly, Glue is a great choice. However,  if you want a tool to handle complex workflow dependencies and job scheduling, Airflow is the tool for you.