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Airflow vs Apache Beam: What are the differences?


Apache Airflow and Apache Beam are both popular open-source frameworks used for building and executing data pipelines. While they share some similarities in terms of their ability to handle batch and stream processing, there are key differences between the two.

  1. Architecture: Airflow is primarily focused on workflow orchestration and scheduling. It allows users to define and manage complex workflows as a Directed Acyclic Graph (DAG). Beam, on the other hand, is a unified programming model and set of SDKs for developing data processing pipelines. It provides a high-level abstraction to write data transformations that can be executed on various distributed processing backends.

  2. Data Processing Paradigm: Airflow focuses on the orchestration and scheduling aspect of data processing workflows. It provides a way to define dependencies and schedule the execution of tasks, but it doesn't provide built-in data processing capabilities. Beam, on the other hand, is specifically designed for data processing. It supports both batch and stream processing and provides a rich set of operators and transforms to perform complex data transformations.

  3. Flexibility: Airflow offers a lot of flexibility in terms of defining and managing workflows. It allows users to define complex workflows with conditional logic, branching, and error handling. It also supports different types of operators to perform various tasks. Beam, on the other hand, provides a more structured and declarative way of defining data processing pipelines. It enforces a certain programming model and doesn't offer as much flexibility in terms of workflow design.

  4. Execution Environment: Airflow is primarily designed to run on a centralized server and relies on a separate task executor to execute individual tasks. It can integrate with various distributed systems for task execution. Beam, on the other hand, can be run on various execution environments like local machine, Apache Flink, Apache Spark, and Google Cloud Dataflow. It provides a unified programming model that can be executed on different backends.

  5. Development Experience: Airflow provides a web-based interface for managing and monitoring workflows. It allows users to visualize and monitor the progress of their workflows, view logs, and manage tasks. Beam, on the other hand, provides a command-line interface and a set of SDKs for writing pipeline code. It doesn't have a built-in web-based interface for managing and monitoring pipelines.

  6. Ecosystem and Integration: Airflow has a large and active ecosystem with support for various integrations like databases, message queues, and cloud services. It also has a rich set of pre-built operators for common tasks. Beam, on the other hand, has a smaller ecosystem compared to Airflow but is designed to integrate well with other Apache projects like Kafka, Hadoop, and Spark.

In Summary, Airflow is primarily focused on workflow orchestration and scheduling and provides flexibility in workflow design. On the other hand, Beam is focused on data processing and provides a unified programming model for building data pipelines that can be executed on different distributed processing backends.

Advice on Airflow and Apache Beam
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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 · 264.7K 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 Apache Beam
  • 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
  • 5
  • 5
  • 2
  • 2
    Unified batch and stream processing

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Cons of Airflow
Cons of Apache Beam
  • 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 is 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.

    What is Apache Beam?

    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

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