ECS is recommended for development teams that prefer AWS-managed solutions, organizations seeking to streamline container deployments, and companies looking for secure and scalable orchestration without the overhead of managing Kubernetes. It is also ideal for enterprises that require tight integration with other AWS services.
Apache Airflow is recommended for data engineers, data scientists, and IT professionals who need to automate and manage workflows. It is particularly suited for organizations handling large-scale data processing tasks, requiring integration with various systems, and those looking to deploy machine learning pipelines or ETL processes.
Apache Airflow might be a bit more popular than Amazon ECS. We know about 75 links to it since March 2021 and only 52 links to Amazon ECS. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Amazon's Elastic Container Service (ECS) and Sliplane both simplify deployment, management, and scaling of containerized applications. However, there are some key differences, and both platforms serve different users and use cases. Let's compare them side by side. - Source: dev.to / 4 months ago
AWS Elastic Container Service (ECS) and Elastic Kubernetes Service (EKS) provide managed container orchestration platforms integrated with AWS infrastructure. - Source: dev.to / 6 months ago
Most cloud platforms support Docker containers. Sliplane, Fly.io, AWS, Google Cloud, etc. This means that you can easily switch between cloud providers if you want to, without having to change your software. If you ever migrated from one cloud provider to another, you probably know how much work this can be. With Docker, you can just take your container image and run it on the new platform. - Source: dev.to / 6 months ago
In containerized environments like Kubernetes or Amazon ECS, configuration is often injected as environment variables or mounted as files. Your app starts up with fresh values every time—no rebuilds needed. - Source: dev.to / 6 months ago
The workers in this example are containers, running in Amazon Elastic Container Service (ECS) with an Amazon Fargate Capacity Provider . Though the workers could potentially run almost anywhere so long as they had access to poll the Step Functions Activity and report SUCCESS/FAILURE back to Step Functions. - Source: dev.to / 7 months ago
Is this really true? Something that can be supported by clear evidence? I’ve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 3 months ago
Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools. - Source: dev.to / 4 months ago
Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / 5 months ago
Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production. - Source: dev.to / 5 months ago
This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / 6 months ago
Google Kubernetes Engine - Google Kubernetes Engine is a powerful cluster manager and orchestration system for running your Docker containers. Set up a cluster in minutes.
Make.com - Tool for workflow automation (Former Integromat)
Docker - Docker is an open platform that enables developers and system administrators to create distributed applications.
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.