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Airflow vs Metaflow: What are the differences?
Introduction
In this article, we will compare and highlight the key differences between Airflow and Metaflow. Both Airflow and Metaflow are popular workflow management platforms used for developing, scheduling, and monitoring data workflows. Let's explore the differences between them.
Cloud Support: Airflow has strong support for various cloud platforms such as AWS, Google Cloud, and Microsoft Azure. It provides built-in support for integrating with cloud-based services, making it easy to incorporate cloud resources into workflows. On the other hand, Metaflow primarily focuses on the support for AWS services and does not have built-in support for other cloud platforms.
Ease of Use: Airflow provides a user-friendly web interface for managing and visualizing workflows. It offers a drag-and-drop interface for creating workflows, making it easier for users to design and manage complex workflows. On the other hand, Metaflow prioritizes simplicity and ease of use in its Python-based programming model. It has a more intuitive and Pythonic API, making it easier for data scientists and developers to work with.
Workflow Paradigm: Airflow follows a task-based workflow paradigm. Workflows are designed as directed acyclic graphs (DAGs) consisting of tasks and their dependencies. Airflow focuses on managing the execution and scheduling of tasks in a distributed environment. In contrast, Metaflow follows a more high-level, data-centric workflow paradigm. It abstracts away the complexities of managing individual tasks and focuses on managing the flow of data through the workflow.
Integration with Data Science Ecosystem: Metaflow provides deep integration with popular data science libraries and tools such as Pandas, TensorFlow, and AWS SageMaker. It offers built-in features for versioning, tracking, and reproducing data science experiments. Airflow, on the other hand, is more focused on managing the broader data engineering and data pipeline workflows. While Airflow can integrate with data science libraries, it may require additional customization and configuration.
Maturity and Community: Airflow has been around since 2014 and has gained significant adoption in the industry. It has a large and active community contributing plugins, integrations, and support. Airflow has a mature ecosystem with comprehensive documentation, making it easier to find resources and solutions to common issues. Metaflow, on the other hand, is relatively newer (introduced in 2019) and has a smaller community compared to Airflow. While Metaflow is backed by Netflix and gaining traction, the community and ecosystem are still growing.
Execution and Scaling: Airflow uses a distributed architecture that allows scaling the execution of workflows across multiple nodes. It supports horizontal scaling by adding more workers and can handle large-scale data processing. Metaflow is designed with scalability in mind and provides built-in support for distributed execution across compute resources, allowing it to handle large-scale data processing as well.
In summary, Airflow and Metaflow differ in terms of cloud support, ease of use, workflow paradigm, integration with data science ecosystem, maturity and community, and execution and scaling capabilities. Choosing between the two depends on specific requirements and priorities, such as cloud platform preferences, the need for a user-friendly interface, the workflow paradigm, and level of integration with data science tools.
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 Metaflow
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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