Let us learn more about the amazon managed workflows for apache airflow, how it works and its use cases wriht the support of our AWS suport services at Bobcares.

What is Amazon managed workflows for Apache airflow?

With Amazon MWAA, we can easily design workflows using Airflow and Python without worrying about the underlying infrastructure required for scalability, availability, and security.

Amazon MWAA dynamically increases its workflow execution capacity based on the requirements and connects with AWS security services to enable speedy and safe data access.

How does the Amazon managed workflows for Apache airflow work?

Amazon Managed processes for Apache Airflow (MWAA) orchestrates the processes using Python-written Directed Acyclic Graphs (DAGs). We provide MWAA access to an Amazon Simple Storage Service (S3) bucket containing the DAGs, plugins, and Python needs.

Then, using the AWS Management Console, a command line interface (CLI), a software development kit (SDK), or the Apache Airflow user interface (UI), execute and monitor the DAGs.

Amazon managed workflows for Apache airflow : Use cases?

• Firstly, it supports complex workflows:

  Create on-demand or planned processes to prepare and handle complex data from big data sources.
• Secondly, coordinate extract, transform, and load (ETL) jobs:

  Within a complicated ETL pipeline, orchestrate numerous ETL processes that leverage various technologies.
• Thirdly, prepare ML data:

  Automate the pipeline to assist machine learning (ML) modeling systems with data import and training.

Features of Amazon managed workflows for apache airflow

• Automatic Airflow setup:

  When creating an Amazon MWAA environment, we can select an Apache Airflow version to quickly set up your Airflow instance.

  Amazon MWAA will install Apache Airflow using open-source code and a user interface that can be seen on the internet.

• Automatic scaling:

  Set the minimum and maximum number of Apache Airflow Workers that will execute in your environment to automatically scale them.

  Amazon MWAA monitors the Workers in the environment and utilizes its autoscaling component to add Workers as needed to satisfy demand, up to and including the maximum number of Workers you specified.

• Built-in authentication:

  Enable role-based authentication and authorisation for your Apache Airflow Web server by setting access control policies in AWS Identity and Access Management (IAM).

  Apache Airflow Workers follow these standards to ensure secure access to AWS services.

• Built-in security:

  The Amazon MWAA hosts the Apache Airflow Workers and Schedulers in its Amazon VPC. Additionally, the AWS Key Management Service automatically secures the data, ensuring that the environment is safe by default. (Active voice).

• Public or private access modes:

  Use a private or public access mode to connect to your Apache Airflow Web server. The Public network access option makes use of an Internet-accessible VPC endpoint for the Apache Airflow Web server.

  The Private network access mode connects the Apache Airflow Web server to a VPC endpoint that is available within the VPC.

  The access control policy we set in AWS Identity and Access Management (IAM) and AWS SSO governs access for the Apache Airflow users in both circumstances.

• Streamlined upgrades and patches:

  Amazon MWAA releases new versions of Apache Airflow on a regular basis. The images for these versions will update and patch up by the Amazon MWAA team.

• Workflow monitoring:

  View Apache Airflow logs and data in Amazon CloudWatch to discover Apache Airflow task delays or workflow issues without the use of third-party solutions. Amazon MWAA provides environment measurements and, if enabled, Apache Airflow logs to CloudWatch automatically.

• AWS integration:

  Amazon MWAA integrates with Amazon Athena, AWS Batch, Amazon CloudWatch, Amazon DynamoDB, AWS DataSync, Amazon EMR, AWS Fargate, Amazon EKS.

  It also integrates with the Amazon Kinesis Data Firehose, AWS Glue, AWS Lambda, Amazon Redshift, Amazon SQS, Amazon SNS, Amazon SageMaker, and Amazon S3, as well as hundreds of built-in and community-created operators and sensors.

• Worker fleets:

  Amazon MWAA provides support for leveraging containers to grow the worker fleet on demand and reduce scheduler outages using Amazon ECS on AWS Fargate.

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Conclusion

To sum up we have now seen more on amazon managed workflows for Apache airflow with the support of our tech team.