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AWS Data Pipeline vs Airflow: What are the differences?

  1. Configuration: AWS Data Pipeline is a managed service that allows users to orchestrate and automate the movement and transformation of data across various AWS services as well as on-premises data sources. It provides a graphical interface for creating and managing data pipelines, making it easy for users to define the structure and steps of their data processing workflows. On the other hand, Airflow is an open-source platform that enables users to programmatically author, schedule, and monitor workflows. It uses Python code as its configuration, providing more flexibility and control over the data processing tasks.

  2. Workflow Definition: In AWS Data Pipeline, workflows are defined using a visual interface where users can drag and drop different components and connect them to create a pipeline. This makes it easier for users who are not familiar with programming to create complex workflows. Airflow, on the other hand, defines workflows as directed acyclic graphs (DAGs) using Python code. This allows developers to have more flexibility and control over the workflow definition, making it easier to track dependencies, handle error scenarios, and dynamically generate tasks.

  3. Integration with AWS Services: AWS Data Pipeline provides seamless integration with various AWS services, such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon EMR. It offers pre-built connectors to these services, allowing users to easily incorporate them into their data pipelines. Airflow is also capable of integrating with AWS services using Python libraries and third-party plugins. However, users have to manually configure the integration and handle the authentication and access control.

  4. Monitoring and Alerting: AWS Data Pipeline provides a comprehensive monitoring dashboard that allows users to track the status and progress of their pipelines. It also offers built-in email notifications and CloudWatch alarms to alert users about any issues or failures in their pipelines. Airflow, on the other hand, provides a web-based user interface where users can monitor and visualize the status of their workflows. It also supports integration with external monitoring tools such as Grafana and Prometheus for more advanced monitoring and alerting capabilities.

  5. Scalability and Performance: AWS Data Pipeline is a fully managed service that automatically scales resources based on the workload. It can handle large datasets and parallel processing using AWS services like Amazon EMR and AWS Glue. Airflow, being an open-source platform, requires users to manually provision and manage their own infrastructure. Users can scale Airflow horizontally by adding more worker nodes to handle concurrent tasks, but they are responsible for managing the scalability and performance aspects.

  6. Community and Support: AWS Data Pipeline has the advantage of being a managed service provided by AWS, which ensures ongoing support and maintenance. It also has a large user community and extensive documentation. Airflow, being an open-source project, relies on its community for support and maintenance. It has an active developer community and provides comprehensive documentation, but users may have to rely on community forums and discussions for troubleshooting and support.

In Summary, AWS Data Pipeline and Airflow have key differences in their configuration options, workflow definition methods, integration with AWS services, monitoring and alerting capabilities, scalability and performance management, as well as the level of community support provided.

Advice on Airflow and AWS Data Pipeline
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Apache SparkApache Spark

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 267.9K views

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval -

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Pros of Airflow
Pros of AWS Data Pipeline
  • 51
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
  • 6
    Open source
  • 5
    Complex workflows
  • 5
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
  • 1
    Easy to create DAG and execute it

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Cons of Airflow
Cons of AWS Data Pipeline
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
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    What are some alternatives to Airflow and AWS Data Pipeline?
    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.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
    See all alternatives