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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.
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.
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
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Beamer
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1