What is Amazon Managed Workflows for Apache Airflow?
It is a managed orchestration service for Apache Airflow1 that makes it easier to set up and operate end-to-end data pipelines in the cloud at scale. With Managed Workflows, you can use Airflow and Python to create workflows without having to manage the underlying infrastructure for scalability, availability, and security. Managed Workflows automatically scales its workflow execution capacity to meet your needs, and is integrated with AWS security services to help provide you with fast and secure access to data.
Amazon Managed Workflows for Apache Airflow is a tool in the Workflow Manager category of a tech stack.
Who uses Amazon Managed Workflows for Apache Airflow?
Companies
6 companies reportedly use Amazon Managed Workflows for Apache Airflow in their tech stacks, including Voodoo, Cloud & SRE & DevOps, and HUMANOO.
Developers
14 developers on StackShare have stated that they use Amazon Managed Workflows for Apache Airflow.
Amazon Managed Workflows for Apache Airflow Integrations
Amazon S3, AWS Lambda, Amazon CloudWatch, Amazon DynamoDB, and Amazon SQS are some of the popular tools that integrate with Amazon Managed Workflows for Apache Airflow. Here's a list of all 15 tools that integrate with Amazon Managed Workflows for Apache Airflow.
Amazon Managed Workflows for Apache Airflow's Features
- Deploy Airflow rapidly at scale
- Run Airflow with built-in security
- Reduce operational costs
- Use a pre-existing plugin or use your own
Amazon Managed Workflows for Apache Airflow Alternatives & Comparisons
What are some alternatives to Amazon Managed Workflows for Apache Airflow?
GitHub Actions
It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
Camunda
With Camunda, business users collaborate with developers to model and automate end-to-end processes using BPMN-powered flowcharts that run with the speed, scale, and resiliency required to compete in today’s digital-first world
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Luigi
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Related Comparisons
No related comparisons found