Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
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AWS Glue
Score 8.6 out of 10
N/A
AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.
$0.44
billed per second, 1 minute minimum
Pricing
Apache Airflow
AWS Glue
Editions & Modules
No answers on this topic
per DPU-Hour
$0.44
billed per second, 1 minute minimum
Offerings
Pricing Offerings
Apache Airflow
AWS Glue
Free Trial
No
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Community Pulse
Apache Airflow
AWS Glue
Considered Both Products
Apache Airflow
No answer on this topic
AWS Glue
Verified User
Vice-President
Chose AWS Glue
AWS Glue is a managed service. It was easier for us to integrate it into our stack since we are already an AWS shop. It saved us the headache of managing a 3rd part service.
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.
Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.
It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.
While easy to set up and manage monitoring for large datasets, its complexity can be a barrier for new users. Integration with AWS Ecosystem, Managed Monitoring, Dashboards and monitoring tools for AWS Glue are generally easy to set up and maintain, Automated Data Pipelines. Automates data pipeline creation, making it efficient for certain data integration
Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing
