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Airflow vs Beamer: What are the differences?

## Introduction

## Key Differences between Airflow and Beamer

1. **Architecture**: Airflow uses Directed Acyclic Graphs (DAGs) to define and execute workflows, while Beamer is a LaTeX document class used for creating presentations. Airflow is designed for orchestrating complex workflows with dependencies and scheduling, while Beamer focuses on creating visually appealing slides for presentations.

2. **Functionality**: Airflow serves as a platform for workflow automation and scheduling tasks, supporting various integrations and plugins for extensibility. On the other hand, Beamer is solely focused on providing tools for creating slides, offering features like themes, overlays, and animations for presentations.

3. **Community**: Airflow has a large and active community of users and contributors, providing support, sharing best practices, and developing new features and integrations. Beamer, being a tool within the LaTeX ecosystem, benefits from the wealth of resources and expertise available for LaTeX users, although its community may not be as specific or dedicated as Airflow's.

4. **Learning Curve**: Airflow may have a steeper learning curve for beginners due to its complex architecture and concepts like DAGs, operators, and sensors. In contrast, Beamer is relatively straightforward for users familiar with LaTeX, as it utilizes LaTeX syntax and commands for creating slides, making it more accessible to those already comfortable with LaTeX typesetting.

5. **Purpose**: The primary purpose of Airflow is workflow orchestration and automation, focusing on managing and monitoring tasks and dependencies within a workflow. Beamer, on the other hand, is specifically designed for creating visually appealing presentations, leveraging LaTeX's typesetting capabilities to produce professional-looking slides.

6. **Flexibility**: Airflow offers flexibility in defining workflows through Python code, allowing for custom logic, integrations, and extensibility through plugins. Beamer, while providing customization options through LaTeX commands, may have limitations compared to the programmability and flexibility of Airflow for complex workflow automation tasks.

In Summary, the key differences between Airflow and Beamer lie in their architecture, functionality, community support, learning curve, purpose, and flexibility, distinguishing them as tools for workflow automation and presentation design, respectively.
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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 · 286.8K views
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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 - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Beamer
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
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    Cons of Airflow
    Cons of Beamer
    • 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 Beamer?

      Use Beamer to announce new features, your latest releases, and relevant news. Improve user engagement with a quick and easy changelog.

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      What companies use Airflow?
      What companies use Beamer?
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      What tools integrate with Airflow?
      What tools integrate with Beamer?

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      What are some alternatives to Airflow and Beamer?
      Luigi
      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.
      Jenkins
      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
      Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
      See all alternatives