No matter where you work or what you do, data will always be a part of your process. With every organization generating data like never before, it is essential to orchestrate tasks and automate data workflows in order to make sure they are properly executed without any delay. Apache Airflow is one of the most popular Automation and Workflow Management tools that come with the broadest range of features. This article will help you manage workflows with AWS Apache Airflow.
Automation plays a key role in improving production rates and work efficiency in various industries. Airflow is used by many Data Engineers and Developers to programmatically author, schedule, and monitor workflows. However, manually maintaining and scaling Airflow, along with handling security and authorization for its users is a daunting task. This is where AWS Apache Airflow comes in. Amazon Managed Workflows for Apache Airflow (MWAA) is a fully managed service that makes it easy to run Apache Airflow on AWS, and to create workflows to perform Extract-Transform-Load (ETL) jobs and Data Pipelines.
Table of Contents
- What is Airflow?
- Key Features of Airflow
- What are Managed Workflows for Apache Airflow (MWAA)?
- Key Features of AWS Apache Airflow
- AWS Apache Airflow Architecture
- AWS Apache Airflow Integrations
- Getting Started with AWS Apache Airflow
- Step 1: Create an Airflow Environment
- Step 2: Upload your DAGs and Plugins to S3
- Step 3: Monitor your Environment
What is Airflow?
Apache Airflow is a well-known open-source Automation and Workflow Management platform for Authoring, Scheduling, and Monitoring workflows. Starting in October 2014 at Airbnb, Airflow joined the Apache Incubator program in 2016 and it has been gaining popularity ever since.
Airflow allows organizations to write workflows as Directed Acyclic Graphs (DAGs) in a standard Python programming language, ensuring anyone with minimal knowledge of the language can deploy one. Airflow helps organizations to schedule their tasks by specifying the plan and frequency of flows. Airflow also provides an interactive interface along with a bunch of different tools to monitor workflows in real-time.
Apache Airflow has gained a lot of popularity among organizations dealing with significant amounts of Data Collection, Processing, and Analysis. There are many tasks that IT experts need to perform manually on a daily basis. Airflow triggers automatic workflow and reduces the time and effort required for collecting data from various sources, processing it, uploading it, and finally creating reports.
Key Features of Airflow
- Open-Source: Airflow is an open-source platform and is available free of cost for everyone to use. It comes with a large community of active users that makes it easier for developers to access resources.
- Dynamic Integration: Airflow uses Python programming language for writing workflows as DAGs. This allows Airflow to be integrated with several operators, hooks, and connectors to generate dynamic pipelines. It can also easily integrate with other platforms like Amazon AWS, Microsoft Azure, Google Cloud, etc.
- Customizability: Airflow supports customization, and it allows users to design their own custom Operators, Executors, and Hooks. You can also extend the libraries as per your needs so that it fits the desired level of abstraction.
- Rich User Interface: Airflow’s rich User Interface (UI) helps in monitoring and managing complex workflows. It uses Jinja templates to create pipelines and it further makes it easy to keep track of the ongoing tasks.
- Scalability: Airflow is highly scalable and is designed to support multiple dependent workflows simultaneously.
What are Managed Workflows for Apache Airflow (MWAA)?
Amazon Managed Workflows for Apache Airflow is a fully managed service in the AWS Cloud for deploying and rapidly scaling open-source Apache Airflow projects. With Amazon Managed Workflows for Apache Airflow, you can author, schedule, and monitor workflows using Airflow within AWS without having to set up and maintain the underlying infrastructure. Amazon MWAA is capable of automatically scaling Airflow’s workflow execution capacity to meet your needs. Airflow is integrated with AWS Security services to provide fast and secure access to your data.
Amazon MWAA uses the Amazon VPC, DAG code, and supporting files in your Amazon S3 storage bucket to create an environment. Airflow allows workflows to be written as Directed Acyclic Graphs (DAGs) using the Python programming language. Airflow workflows fetch input from sources like Amazon S3 storage buckets using Amazon Athena queries and perform transformations on Amazon EMR clusters. The output data can be used to train Machine Learning Models on Amazon SageMaker.
Key Features of AWS Apache Airflow
- Automatic Airflow Setup: You can easily set up Apache Airflow within the Amazon MWAA environment without facing any challenges. Amazon MWAA sets up Apache Airflow using the same Airflow User Interface (UI) and open-source code.
- Built-in Security: As discussed, Airflow Workers and Schedulers run in MWAA’s Amazon VPC, which means data is also automatically encrypted using AWS Key Management Service.
- Scalability: It is very easy to scale Airflow within MWAA, you can automatically scale Airflow Workers by specifying the minimum and a maximum number of workers. Its autoscaling component automatically adds workers to meet the requirements.
- Built-in Authentication: MWAA enables role-based authentication and authorization for your Airflow Web Server by defining the access control policies in AWS Identity and Access Management (IAM).
- AWS Integration: Deploying Airflow within AWS opens doors for open-source integrations with various AWS services such as Amazon Athena, AWS Batch, Amazon DynamoDB, AWS DataSync, Amazon EMR, Amazon EKS, AWS Glue, Amazon Redshift, Amazon SageMaker, Amazon S3, etc.
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AWS Apache Airflow Architecture
The Apache Airflow Scheduler and Workers are AWS Fargate containers that connect to the private sub-networks in the Amazon Service VPC for your environment. Airflow metadatabases are managed by AWS, and they can be accessed by Airflow Scheduler and Workers Fargate containers via a privately-secured VPC endpoint.
However, other AWS services like Amazon CloudWatch, Amazon S3, Amazon SQS, Amazon ECR, and AWS KMS are separate from Amazon MWAA architecture. But they can still be accessed from the Apache Airflow Scheduler(s) and Workers in the Fargate containers.
Airflow Web Server can be accessed in both ways, over the Internet, and within the Amazon VPC. To access the Airflow Server over the Internet, you can select the Public Network Apache Airflow Access Mode. And, to access the Airflow Server within the Amazon VPC, you can select the Private Network Apache Airflow Access Mode. In both ways, authentication, and authorization for your Airflow Server are controlled by the access control policy defined in AWS Identity and Access Management (IAM).
Take a look at the overall architecture of AWS Apache Airflow.
AWS Apache Airflow Integrations
As discussed in the previous sections, deploying Airflow within AWS opens doors for open-source integrations with various AWS services as well as 100s of built-in and community-created operators and sensors. The community-created operators or plugins for Apache Airflow simplify connections to AWS services such as Amazon S3, Amazon Redshift, Amazon EMR, AWS Glue, Amazon SageMaker, Amazon Athena, etc. You can further use these community-driven operators to connect with services on other Cloud platforms as well.
To provide flexibility in performing Data Processing Tasks, AWS Apache Airflow fully supports integration with AWS services and popular third-party tools such as Apache Hadoop, Hive, Presto, and Spark. On top of that, Amazon MWAA maintains compatibility with the Amazon MWAA API.
Getting Started with AWS Apache Airflow
To start using Amazon Managed Workflows for Apache Airflow, follow the below-mentioned steps.
- Step 1: Create an Airflow Environment
- Step 2: Upload your DAGs and Plugins to S3
- Step 3: Monitor your Environment
Create an Airflow Environment Using Amazon MWAA
- To create an Airflow Environment, open your MWAA console. From the Amazon MWAA console, click on “Create environment”. It will now prompt you to name the environment and select the Airflow version to use.
Upload your DAGs and Plugins to S3
- The next step requires you to upload DAGs and Plugins to S3. To do so, select the S3 Bucket where you want the codes and files to be uploaded.
- Then, you can select the folder to upload your DAG code. The S3 Bucket name must start with airflow-.
- In addition to that, you can also specify a plugin file and a requirements file to be uploaded to S3.
- The plugins file (ZIP) contains the plugins used by your DAGs.
- The requirements file describes the Python dependencies required to run your DAGs.
For plugins and requirements, select the S3 object version to use.
- Click on “Next” to configure the advanced settings. In the “Networking” window, you have the option to choose the network (Public network or Private network) for web server access. For the purpose of this demonstration, a Public network is chosen.
- You can now allow MWAA to create a VPC Security Group based on the selected web server access.
- Next up, you need to configure the “Environment class”. Based on the number of DAGs, you’re provided with a suggestion on which class can be used. However, you can modify its class at any time.
- Coming to encryption, the data at rest is always encrypted. However, you can select a customized key managed byAWS Key Management Service (KMS).
Monitor your Environment
- With MWAA, you can monitor your environment with CloudWatch. To do so, the environmental performance needs to be published to CloudWatch Metrics, an option that is enabled by default.
- In addition to environment metrics, you can also send Airflow Logs to CloudWatch Logs. To do so, specify the log level and the Airflow components that should send their logs to CloudWatch Logs. For the purposes of this demonstration, log level INFO is used.
- Finally, you need to configure the permissions to be used by your environment to access your DAGs, write logs, and run DAGs. Select “Create a new role” and click on the “Create environment” button. The new Airflow environment is now ready to use.
As the complexity of your Data Pipelines increase, it becomes necessary to orchestrate the overall process into a series of sub-tasks. Apache Airflow is used by many Developers and Data Engineers to programmatically automate and manage workflows. And with AWS Apache Airflow, you can get rid of the common challenges involved in running your own Airflow environments.
Amazon Managed Workflows for Apache Airflow often referred to as AWS Apache AIrflow, is a fully managed service that makes it easy to run Apache Airflow on AWS. This article introduced you to AWS Apache Airflow and helped you get started with it.
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Share your experience of working with AWS Apache Airflow in the comments section below.
Amazon Managed Workflows for Apache Airflow (MWAA) orchestrates your workflows using Directed Acyclic Graphs (DAGs) written in Python. You provide MWAA an Amazon Simple Storage Service (S3) bucket where your DAGs, plugins, and Python requirements reside.
Worker fleets – Amazon MWAA offers support for using containers to scale the worker fleet on demand and reduce scheduler outages using Amazon ECS on AWS Fargate. Operators that invoke tasks on Amazon ECS containers, and Kubernetes operators that create and run pods on a Kubernetes cluster are supported.
Apache Airflow is an open-source tool to programmatically author, schedule, and monitor workflows. It is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines. You can easily visualize your data pipelines' dependencies, progress, logs, code, trigger tasks, and success status.
Easy to Use
Anyone with Python knowledge can deploy a workflow. Apache Airflow does not limit the scope of your pipelines; you can use it to build ML models, transfer data, manage your infrastructure, and more.
Airflow is free and open source, licensed under Apache License 2.0.
To review an environment summary
Review the environment summary, choose Create environment. It takes about twenty to thirty minutes to create an environment.
Apache Airflow and AWS Glue were made with different aims but they share some common ground. Both allow you to create and manage workflows. Due to this similarity, some tasks you can do with Airflow can also be done by Glue and vice versa.
Since Fargate essentially just runs docker containers, you can develop locally using docker and run your docker images in Fargate without worrying about compatibility.
Fargate runs on a fully managed server so you dont worry about the cluster or server as you would in Elastic beanstalk. You just manage the Docker container making it more of a Software-As-A-Service (SaaS) model.
Easy To Start
A great thing about Airflow is that building your first toy DAG is very easy. First all you need to do is write a few parameterized operators. Suddenly you're running Airflow. Locally or maybe on a EC2 instance.
Syllabus: Learning Apache Airflow with Python in easy way in 40 Minutes.
Airflow is a powerful tool to run ETL pipelines; however, Airflow needs to be extended to run machine learning pipelines. With Flyte, you can version control your code, audit the data, reproduce executions, cache the outputs, and insert checkpoints without dwelling on the scalability of your machine learning pipelines.
Apache Airflow is an open-source Python-based workflow automation tool for setting up and maintaining powerful data pipelines. Airflow isn't an ETL tool per se. But it manages, structures, and organizes ETL pipelines using Directed Acyclic Graphs (DAGs).
Note: SQLite is used in Airflow tests. Do not use it in production. We recommend using the latest stable version of SQLite for local development. Please note that with respect to Python 3 support, Airflow 2.0.
Airflow is written in Python, and workflows are created via Python scripts.
Airflow vs Jenkins: Production and Testing
Since Airflow is not a DevOps tool, it does not support non-production tasks. This means that any job you load on Airflow will be processed in real-time. However, Jenkins is more suitable for testing builds. It supports test frameworks like Robot, PyTest, and Selenium.
Airflow is Python-based but you can execute a program irrespective of the language. For instance, the first stage of your workflow has to execute a C++ based program to perform image analysis and then a Python-based program to transfer that information to S3.
The main characteristic of Airflow workflows is that all workflows are defined in Python code. “Workflows as code” serves several purposes: Dynamic: Airflow pipelines are configured as Python code, allowing for dynamic pipeline generation.
- Authenticate your AWS account via AWS CLI;
- Get a CLI token and the MWAA web server hostname via AWS CLI;
- Send a post request to your MWAA web server forwarding the CLI token and Airflow CLI command;
- Check the response, parse the results and decode the output.
- Step 1: Create an ADF Pipeline. Create a new Data Factory resource in your ADF dashboard, by visiting the resources group. ...
- Step 2: Connect App with Azure Active Directory. ...
- Step 3: Build a DAG Run for ADF Job.
AWS Glue is a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. AWS Glue can run your extract, transform, and load (ETL) jobs as new data arrives.
AWS Glue now supports the Scala programming language, in addition to Python, to give you choice and flexibility when writing your AWS Glue ETL scripts. You can run these scripts interactively using Glue's development endpoints or create jobs that can be scheduled.
AWS Glue Studio now provides the option to define transforms using SQL queries, allowing you to perform aggregations, easily apply filter logic to your data, add calculated fields, and more. This feature makes it easy to seamlessly mix SQL queries with AWS Glue Studio's visual transforms while authoring ETL jobs.
While Fargate is a Container as a Service (CaaS) offering, AWS Lambda is a Function as a Service (FaaS offering). Therefore, Lambda functions do not necessarily need to be packaged into containers, making it easier to get started with Lambda. But if you have containerized applications, Fargate is the way to go.
But since Fargate scales capacity in a stair-step process, Lambda is often faster in this regard. Also, Fargate does not support GPUs or tasks that require more than 10 GB of disk storage per container, although this is still far more than Lambda's 512 MB. Lambda, however, has no maximum concurrency limits.
Fargate is also not a Kubernetes distribution. It's not based on or derived from Kubernetes. Instead, probably the best way to think about Fargate's relationship to Kubernetes is to say that Fargate is an optional management tool that complements Elastic Kubernetes Service (EKS), Amazon's managed Kubernetes platform.
Elastic Beanstalk isn't great if you need a lot of environment variables. The simple reason is that Elastic Beanstalk has a hard limit of 4KB to store all key-value pairs. The environment had accumulated 74 environment variables — a few of them had exceedingly verbose names.
Fargate proves to be a great choice if your application consists of variable workloads. This is because, with Fargate, you only pay for the amount of resources you provision. Fargate is better than EC2 in terms of flexibility. However, there is no long-term commitment to the resources you have provisioned.
In summary, Fargate is a PaaS-like layer on top of ECS that abstracts the infrastructure which enables users to focus on the desired state of the application.
Apache Airflow is an open-source platform to Author, Schedule and Monitor workflows. It was created at Airbnb and currently is a part of Apache Software Foundation. Airflow helps you to create workflows using Python programming language and these workflows can be scheduled and monitored easily with it.
Apache Airflow is an open source tool that helps DevOps teams build and manage workflows programmatically. It can help drive data pipelines by using standard Python features… Share your ideas with millions of readers.
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Airflow is purpose-built to orchestrate the data pipelines that provide ELT at scale for a modern data platform.
Amazon Managed Workflows for Apache Airflow (MWAA)
Managed Workflows is a managed orchestration service for Apache Airflow that makes it easy for data engineers and data scientists to execute data processing workflows on AWS. With MWAA, you can deploy and get started with Airflow in minutes.
Our growing workforce of data engineers, data scientists and analysts are using Airflow, a platform we built to allow us to move fast, keep our momentum as we author, monitor and retrofit data pipelines.
Who uses Airflow? 309 companies reportedly use Airflow in their tech stacks, including Airbnb, Robinhood, and Slack.
Luigi, Apache NiFi, Jenkins, AWS Step Functions, and Pachyderm are the most popular alternatives and competitors to Airflow.
Infrastructure as Code (IaC) brings automation to the provisioning process, which was traditionally done manually. Rather than relying on manually performed steps, both administrators and developers can instantiate infrastructure using configuration files.
Large System Extensions (LSE) improve the performance of atomic operations in systems with many processors. Understanding LSE helps developers port software applications to AWS Graviton processors.
Resolution. Amazon S3 access control lists (ACLs) enable you to manage access to S3 buckets and objects. Every S3 bucket and object has an ACL attached to it as a subresource. The ACLs define which AWS accounts or groups are granted access along with the type of access.
In Airflow, a DAG – or a Directed Acyclic Graph – is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG is defined in a Python script, which represents the DAGs structure (tasks and their dependencies) as code.
IaC makes your whole infrastructure, including your Kubernetes clusters, versionable, testable and repeatable.
IaC helps you to align development and operations because both teams can use the same description of the application deployment, supporting a DevOps approach. The same deployment process should be used for every environment, including your production environment.
The main difference comes down to how intimate CloudFormation is to AWS in that it only works with AWS IaC.
AWS Glue provides both visual and code-based interfaces to make data integration easier. Users can easily find and access data using the AWS Glue Data Catalog. Data engineers and ETL (extract, transform, and load) developers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio.
Glue can only execute jobs using Scala or Python code. Lambda can execute code from triggers by other services (SQS, Kafka, DynamoDB, Kinesis, CloudWatch, etc.) vs. Glue which can be triggered by lambda events, another Glue jobs, manually or from a schedule.
We evaluate the network ACL rules when traffic enters and leaves the subnet, not as it is routed within a subnet. Network ACLs are stateless, which means that responses to allowed inbound traffic are subject to the rules for outbound traffic (and vice versa).
The biggest advantage of using ACL is that you can control the access level of not only buckets but also of an object using it. Whereas IAM or Bucket Policies can only be attached to buckets but not to objects in the bucket, Bucket ACLs can be assigned to buckets as well as objects in it.
Airflow isn't an ETL tool per se. But it manages, structures, and organizes ETL pipelines using Directed Acyclic Graphs (DAGs).
You can have as many DAGs as you want, each describing an arbitrary number of tasks. In general, each one should correspond to a single logical workflow. When searching for DAGs, Airflow will only consider files where the string “airflow” and “DAG” both appear in the contents of the . py file.
A DAG is a Directed Acyclic Graph — a conceptual representation of a series of activities, or, in other words, a mathematical abstraction of a data pipeline. Although used in different circles, both terms, DAG and data pipeline, represent an almost identical mechanism.