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Table of contents
1.
Introduction
2.
Amazon Managed Workflows for Apache Airflow (MWAA)
3.
Working
4.
Features
5.
Getting Started with Amazon MWAA
6.
Use Cases of Amazon MWAA
7.
Amazon MWAA Pricing
8.
Frequently Asked Questions
8.1.
What is Amazon MWAA?
8.2.
What is the relationship between this service and other AWS services?
8.3.
What does Amazon MWAA handle on my behalf?
8.4.
When should I use Amazon MWAA?
8.5.
How do I keep track of my MWAA service and workflow execution?
9.
Conclusion 
Last Updated: Mar 27, 2024

Amazon Managed Workflows for Apache Airflow

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Introduction

Data will always be a part of your process, no matter where you work or what you do. With every organization generating data, it is critical to orchestrate tasks and automate data workflows to ensure they are executed correctly and without delay. Apache Airflow is one of the most popular Automation and Workflow Management tools, offering the most features. In this blog, we will about Amazon Managed Workflows for Apache Airflow (MWAA) and its working.

Automation is critical to increasing production rates and work efficiency in various industries. Many Data Engineers and Developers use Airflow to author, schedule, and monitor workflows programmatically. Manually maintaining and scaling Airflow and handling security and authorization for its users is a difficult task. AWS Apache Airflow can help with this. Amazon Managed Workflows for Apache Airflow is a fully managed service that allows you to quickly run Apache Airflow on AWS and create workflows to perform Extract-Transform-Load (ETL) jobs and Data Pipelines.

Amazon Managed Workflows for Apache Airflow (MWAA)

Amazon Managed Workflows for Apache Airflow (MWAA) is a managed orchestration service for Apache Airflow that makes it easier to scale end-to-end data pipelines in the cloud. Apache Airflow is an open-source tool for authoring, scheduling, and monitoring sequences of processes and tasks known as "workflows." You can use Airflow and Python to create workflows with Managed Workflows without having to manage the underlying infrastructure for scalability, security, and availability. Managed Workflows scales its workflow execution capacity automatically to meet your needs and is integrated with AWS security to help provide you with fast and secure data access.

We are done with the brief intro about Amazon MWAA. Let's learn about its working.

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Working

Amazon Managed Workflows for Apache Airflow (MWAA) orchestrates and schedules your workflows using Python-written Directed Acyclic Graphs (DAGs). You give Managed Workflows an S3 bucket containing your DAGs, plugins, and Python dependencies list and upload to it, either manually or through a code pipeline, to describe and automate the Extract, Transform, Load (ETL), and Learn process. Then, using the CLI, SDK, or Airflow UI, run and monitor your DAGs.

Working of Amazon Managed Workflows for Apache Airflow (MWAA)

Features

Following are some features of Amazon MWAA:

  • Automatic Airflow Setup: Apache Airflow can be easily configured within the Amazon MWAA environment. Amazon MWAA configures Apache Airflow using the same Airflow UI and open-source code.
  • Built-in Security: As previously stated, Airflow Workers and Schedulers run in MWAA's Amazon VPC, which means data is automatically encrypted using AWS Key Management Service.
  • Scalability:  Scaling Airflow within MWAA is very simple; you can automatically scale Airflow Workers by specifying a minimum and a maximum number of workers. Its autoscaling component adds workers automatically to meet the requirements.
  • Built-in Authentication: By defining access control policies in AWS Identity and Access Management, MWAA enables role-based authorization and authentication for your Airflow Web Server (IAM).
  • AWS Integration: Deploying Airflow on AWS opens up the possibility of open-source integrations with various AWS services such as Amazon Athena, Amazon DynamoDB, AWS Batch, AWS DataSync, Amazon EMR, Amazon EKS, AWS Glue, Amazon SageMaker, Amazon Redshift, Amazon S3, and so on.

Getting Started with Amazon MWAA

With the following basic steps, you can get started with Amazon MWAA:

  1. Create your Managed Workflow environment
    Inform Managed Workflows of the location of your DAGs, plugins, and Python dependencies within the S3 bucket.
  2. Write your workflow code and upload it.
    Package and upload your code to S3 after writing your workflow code. When you make a change, use a code pipeline to automate the process.
  3. Run and monitor your DAGs in Airflow
    Workflow Management In Airflow, load your code. Run DAGs using the CLI, SDK, or Airflow UI.

Use Cases of Amazon MWAA

Following are some use cases of Amazon MWAA:

  1. Enable Complex Workflows: Managed Workflows use a web user interface or Cloudwatch to centrally monitor complex workflows.
  2. Coordinate Extract, Transform, and Load (ETL) Jobs: Managed Workflow can be used to coordinate multiple AWS Glue, Batch, and EMR jobs to blend and prepare data for analysis.
  3. Prepare Machine Learning (ML) Data: To enable machine learning, source data must be collected, processed, and normalized so that ML modeling systems such as Amazon SageMaker, a fully managed service, can train on it. Managed Workflows address this issue by making it easier to connect the steps required to automate your ML pipeline.

Amazon MWAA Pricing

You only pay for what you use with Amazon Managed Workflows for Apache Airflow (MWAA). There are no minimum fees or commitments required. You pay for the time your Airflow Environment is active, as well as any additional auto-scaling to provide additional worker or web server capacity.

There are various factors contributing to the pricing like Environment Pricing, Additional Worker Instance Pricing, Additional Scheduler Instance Pricing, and Database Storage. To know more about pricing, refer to Amazon Managed Workflows for Apache Airflow Pricing.

We've learned a lot of new concepts up to this point, so let's look at some Frequently Asked Questions related to them.

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Frequently Asked Questions

What is Amazon MWAA?

Amazon Managed Workflows for Apache Airflows (MWAA) is a managed Apache Airflow service used to extract business insights across an organization by combining, enriching, and transforming data via a workflow. Managed Workflows relieve you of the responsibility of managing, configuring, and scaling the Airflow environment while you orchestrate data processing workflows and manage their execution via AWS-backed logging and monitoring.

What is the relationship between this service and other AWS services?

Amazon MWAA is a workflow environment that enables data engineers and data scientists to create workflows that use other AWS, on-premise, and cloud services. Amazon MWAA workflows use Athena queries to retrieve input from sources such as S3, perform transformations on clusters, and can use the resulting data to train machine learning models on SageMaker. Workflows in Amazon MWAA are written in Python as Directed Acyclic Graphs (DAGs). The ability to create task plugins for any AWS or on-premise resources required for your workflows, such as Athena, Batch, Cloudwatch, DynamoDB, DataSync, EMR, ECS/Fargate, EKS, Firehose, Glue, Lambda, Redshift, SQS, SNS, Sagemaker, and S3, is a key benefit of Airflow.

What does Amazon MWAA handle on my behalf?

Amazon MWAA manages all aspects of Airflow setup, from provisioning infrastructure capacity (server instances and storage) to installing software and providing simplified user management and authorization via AWS Identity and Access Management (IAM) and Single Sign-On (SSO).

When should I use Amazon MWAA?

You should use MWAA to spend more time developing workflows and less time managing the infrastructure and Airflow environment, all while getting consistent performance from the managed service.

How do I keep track of my MWAA service and workflow execution?

Amazon MWAA will make available Airflow environments accessible via the AWS management console, AWS CLI, and SDK. The Airflow user interface supports direct Internet and VPC access. An API call and the AWS CLI will provide access to airflow command-line instructions.

Conclusion 

In this article, we have extensively discussed Amazon Managed Workflows for Apache Airflow (MWAA) and its working and features.

After reading about Amazon MWAA, are you not feeling excited to read/explore more articles on the topic of AWS? Don't worry; Coding Ninjas has you covered. To learn, see Introduction to AWSAWS FeaturesManaging Devices with AWS IoTAWS Amplify, and AWS Cost & Usage Report

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