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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs Metaflow

Airflow vs Metaflow

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Metaflow
Metaflow
Stacks16
Followers51
Votes0
GitHub Stars9.6K
Forks930

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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Advice on Airflow, Metaflow

Anonymous
Anonymous

Jan 19, 2020

Needs advice

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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Comments

Detailed Comparison

Airflow
Airflow
Metaflow
Metaflow

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.

It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
End-to-end ML Platform; Model with your favorite tools; Powered by the AWS cloud; Battle-hardened at Netflix
Statistics
GitHub Stars
-
GitHub Stars
9.6K
GitHub Forks
-
GitHub Forks
930
Stacks
1.7K
Stacks
16
Followers
2.8K
Followers
51
Votes
128
Votes
0
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
No community feedback yet

What are some alternatives to Airflow, Metaflow?

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

NumPy

NumPy

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

Apache Beam

Apache Beam

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

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

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.

PyXLL

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

Unito

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

Shipyard

Shipyard

na

Flumio

Flumio

Flumio is a modern automation platform that lets you build powerful workflows with a simple drag-and-drop interface. It combines the power of custom development with the speed of a no-code/low-code tool. Developers can still embed custom logic directly into workflows.

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