In dit Engelstalige artikel maken we een vergelijking tussen Cloud Composer en MWAA. Dit zal je helpen de overeenkomsten, verschillen en factoren te begrijpen die je moet overwegen bij de keuze voor een cloudoplossing. Weet dat er andere goede opties zijn als het gaat om het hosten van een beheerde airflow-implementatie, zoals Microsoft Azure. De twee die in dit artikel worden vergeleken, zijn gekozen vanwege mijn praktijkervaring met beide beheerde diensten en hun respectievelijke ecosystemen.
In recent years, Apache Airflow has emerged as a highly popular platform for efficiently managing complex workflows and data pipelines. When I first encountered Airflow in 2019, integrating it with existing cloud infrastructure posed a significant challenge. As we wrestled with a shoddy implementation on an EC2 instance, a colleague of mine predicted the potential value of perfecting this integration and offering "Airflow as a Service".
Fast forward a couple of years, and we now have Amazon's Managed Workflows for Apache Airflow (MWAA), followed closely by Google Cloud Composer, providing robust solutions in this domain.
Ease of Deployment and Management
AWS MWAA and GCP Cloud Composer both aim to simplify the deployment and management of Apache Airflow. Both services provide web-based UIs (User Interface) for configuring and monitoring workflows, as well as CLI (Command Line Interface) and API (Application Programming Interface) access for automation and integration.
If you plan to deploy your project with an infrastructure as code solution such as Terraform, it is important to note that as Cloud Composer manages most of these resources fully, your code runner may not be able to automatically find or manage all the underlying resources. If this is important for your project, it may be a factor that pushes you towards MWAA.
Scaling and Performance
Both AWS MWAA and GCP Cloud Composer offer autoscaling capabilities for scaling Airflow. When configuring either of these managed service providers, you have the option to set the minimum and maximum number of workers for your environment. Under heavy load, additional resources will be allocated to horizontally scale out the workflow. Conversely, when the workload decreases and there are idle workers, they will be removed to minimize costs.
When it comes to performance, both AWS MWAA and GCP Cloud Composer strive to enhance the efficiency of Airflow workflows. They offer managed environments with preconfigured settings and optimisations, ensuring seamless execution and minimal latency.
In my experience, both service providers offer comparable capabilities and configuration options. If performance is a critical consideration for your project, it is advisable to evaluate the required integrations and consider selecting the cloud provider that provides the lowest latency connection.
For instance, suppose your airflow implementation heavily relies on AWS data sources or direct interactions with AWS services. In that case, MWAA might offer lower latency due to its integration with the AWS ecosystem. Conversely, if your workflow primarily involves Google Cloud data sources or interactions with GCP services, Cloud Composer could potentially provide lower latency connections.
For third party integrations it is important to look at where that data source/service is located and the routing information available, you can then look at the AWS availability zones and GCP regions to compare how this could affect performance in one over the other.
By assessing the specific needs and connectivity requirements, you can make an informed decision to optimise performance.
Integration with Cloud Services
One of the advantages of using managed services is the seamless integration with other cloud offerings. MWAA integrates well with various AWS services, such as S3, Glue, Lambda, and Redshift, enabling you to build end-to-end data pipelines within the AWS ecosystem. GCP Cloud Composer, similarly, integrates with GCP services like BigQuery, Cloud Storage, and Pub/Sub, allowing you to harness the full potential of Google Cloud.
AWS offers a wide range of third-party integrations and partnerships that allow users to seamlessly connect their cloud infrastructure with popular tools and services. Companies can easily integrate their AWS environment with tools like Splunk for log management and Tableau for data visualization and analytics.
Whilst Google Cloud Platform also provides some third-party integrations, its selection is comparatively narrower instead focusing on its own suite of services. This limitation could impact the flexibility and extensibility of workflows and data pipelines, particularly for organisations that heavily rely on specific tools for their data processing and analytics needs.
A project’s integration requirements and the tech ecosystem used within your organisation should be a key factor for consideration when it comes to making the decision of which managed service to use.
Pricing and Cost Optimisation
Pricing structures for AWS MWAA and GCP Cloud Composer differ, and understanding the cost implications is crucial for optimising your cloud spend.
Both managed services charge for usage of the underlying compute resources, costs will depend on the instance types/sizes, quantity, and usage time for worker nodes utilised by your Airflow project. Both providers also offer discounted compute rates for committed usage by locking into a minimum usage rate for 1-3 years.
GCP will charge storage and data transfer costs under Google Cloud Storage for DAGs and other artifacts, AWS will be similar except for storage will be utilising Amazon S3 instead.
Both providers may apply data transfer costs for ingress and egress traffic even within their respective ecosystem.
As the pricing model for both providers are ever-changing, it is recommended that you get in contact with sales representatives from each platform for a to-date quotation or use the pricing calculators provided by both platforms to make an informed decision on how each provider will affect your organisation financially.
The table below shows a brief overview of the comparison points discussed in the previous sections.
As both platforms excel in terms of ease of deployment and management, deep integration within their respective ecosystems, and robust computational capabilities. Ultimately, the decision boils down to evaluating your specific requirements, considering the strengths and capabilities of each platform, and understanding how they align with your organisation's goals and existing technology stack.
Conducting thorough assessments, considering the integration possibilities, and factoring in familiarity with the respective cloud provider will help you make an optimal choice between AWS MWAA and GCP Cloud Composer for Apache Airflow.
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This article was written by Jordan Holliday
Jordan is a Data Engineer with a passion for computer science. Transitioning from Full Stack Web Application Development to Technical Web Analysis and now ultimately to Data Engineering, he has a wide range of technical and analytical capabilities uniquely positioning him to assist clients in their diverse data needs.
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