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Metaflow VS Azkaban

Compare Metaflow VS Azkaban and see what are their differences

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  3. » Reviews
  4. » Alternatives

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.

Azkaban logo Azkaban

Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.
  • Metaflow Landing page
    Landing page //
    2023-03-03
  • Azkaban Landing page
    Landing page //
    2019-08-01

Metaflow features and specs

  • Ease of Use
    Metaflow is designed with a strong focus on user experience, providing users with a simple and user-friendly interface for building and managing workflows. Its Pythonic API makes it easy for data scientists to work with complex data workflows without needing to learn a lot of new concepts.
  • Scalability
    Metaflow supports scalable data workflows, allowing users to run their workflows seamlessly from a laptop to the cloud. It integrates well with AWS, enabling users to utilize Amazon's scalable infrastructure for processing large datasets.
  • Versioning
    Metaflow provides built-in support for data and model versioning, making it easier for teams to track changes and reproduce results. This feature is crucial for maintaining consistency and reliability in machine learning projects.
  • Integration with Popular Tools
    Metaflow integrates well with popular data science and machine learning tools, including Jupyter notebooks and AWS services, enhancing its usability within existing data ecosystems.
  • Error Handling and Monitoring
    Metaflow offers robust error handling and monitoring capabilities, allowing users to track the execution of workflows, identify errors, and debug issues efficiently.

Possible disadvantages of Metaflow

  • AWS Dependency
    While Metaflow supports other infrastructures, it is tightly integrated with AWS. Users who do not use AWS may find it less convenient compared to other tools that are more agnostic in their cloud support.
  • Limited Support for Non-Python Environments
    Metaflow primarily supports Python, which might be a limitation for teams or projects that rely heavily on other programming languages for their workflows.
  • Learning Curve for Advanced Features
    Although Metaflow is designed to be user-friendly, utilizing its advanced features and realizing its full potential can have a steep learning curve, especially for users without prior experience with workflow management systems.
  • Community and Ecosystem Size
    Compared to some of its competitors, Metaflow has a smaller community and ecosystem, which might limit the availability of third-party resources, plugins, and community support.
  • Enterprise Features
    Some advanced enterprise features, while robust, may not be as developed or extensive compared to other dedicated data processing and workflow management platforms.

Azkaban features and specs

  • Scalability
    Azkaban is designed to efficiently manage and schedule batch jobs, making it suitable for handling large-scale data processing tasks in a distributed environment.
  • Dependency Management
    Azkaban offers robust dependency management, allowing complex job workflows with dependencies to be easily orchestrated and visualized.
  • Web-Based Interface
    It provides a user-friendly web interface for managing workflows, monitoring job execution, and handling configurations, which enhances user interaction.
  • Open Source
    As an open-source tool, Azkaban allows for customization and community contributions, which can lead to rapid feature enhancements and bug fixes.
  • Integration
    Azkaban integrates well with other Hadoop ecosystem tools, making it an excellent choice for big data environments.

Possible disadvantages of Azkaban

  • Complex Setup
    Setting up and configuring Azkaban can be complex and time-consuming, requiring detailed knowledge of its system and dependencies.
  • Limited Support
    Being an open-source project, it might not have the extensive enterprise-level support that commercial workflow management tools offer.
  • Resource Management
    Azkaban lacks advanced resource management capabilities found in some other orchestration tools, which can be a limitation in environments with diverse resource needs.
  • Interface Limitations
    While it does provide a web-based interface, some users find it less intuitive compared to those of other modern workflow management systems.
  • Feature Set
    Azkaban may lack some advanced features and integrations available in newer orchestration tools, which could be limiting for certain complex or evolving needs.

Metaflow videos

+ Add

useR! 2020: End-to-end machine learning with Metaflow (S. Goyal, B. Galvin, J. Ge), tutorial

More videos:

  • Review - Screencast: Metaflow Sandbox Example

Azkaban videos

+ Add

Harry Potter and the Prisoner of Azkaban - Movie Review

More videos:

  • Review - Harry Potter and the Prisoner of Azkaban - Movie Review
  • Review - Harry Potter and The Prisoner of Azkaban

Category Popularity

0-100% (relative to Metaflow and Azkaban)

Metaflow

Azkaban

Workflow Automation
63 63%
Workflow Automation
37% 37
DevOps Tools
58 58%
DevOps Tools
42% 42
Developer Tools
52 52%
Developer Tools
48% 48
Automation
100 100%
Automation
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Metaflow and Azkaban

Metaflow Reviews

Comparison of Python pipeline packages: Airflow, Luigi, Gokart, Metaflow, Kedro, PipelineX
Metaflow enables you to define your pipeline as a child class of FlowSpec that includes class methods with step decorators in Python code.
Source: medium.com

Azkaban Reviews

We have no reviews of Azkaban yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Metaflow should be more popular than Azkaban. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Metaflow mentions (14)

  • 20 Open Source Tools I Recommend to Build, Share, and Run AI Projects
    Metaflow is an open source framework developed at Netflix for building and managing ML, AI, and data science projects. This tool addresses the issue of deploying large data science applications in production by allowing developers to build workflows using their Python API, explore with notebooks, test, and quickly scale out to the cloud. ML experiments and workflows can also be tracked and stored on the platform. - Source: dev.to / 6 months ago
  • Recapping the AI, Machine Learning and Computer Meetup — August 15, 2024
    As a data scientist/ML practitioner, how would you feel if you can independently iterate on your data science projects without ever worrying about operational overheads like deployment or containerization? Let’s find out by walking you through a sample project that helps you do so! We’ll combine Python, AWS, Metaflow and BentoML into a template/scaffolding project with sample code to train, serve, and deploy ML... - Source: dev.to / 9 months ago
  • What are some open-source ML pipeline managers that are easy to use?
    I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home. Source: about 2 years ago
  • Needs advice for choosing tools for my team. We use AWS.
    1) I've been looking into [Metaflow](https://metaflow.org/), which connects nicely to AWS, does a lot of heavy lifting for you, including scheduling. Source: about 2 years ago
  • Selfhosted chatGPT with local contente
    Even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf. Source: about 2 years ago
View more

Azkaban mentions (3)

What are some alternatives?

When comparing Metaflow and Azkaban, you can also consider the following products

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.

RunDeck - RunDeck is an open source automation service with a web console, command line tools and a WebAPI.

DepHell - :package: :fire: Python project management. Manage packages: convert between formats, lock, install, resolve, isolate, test, build graph, show outdated, audit. Manage venvs, build package, bump ver...

Activeeon - ProActive Workflows & Scheduling is a java-based cross-platform workflow scheduler and resource manager that is able to run workflow tasks in multiple languages and multiple environments: Windows, Linux, Mac, Unix, etc.

Kubernetes - Kubernetes is an open source orchestration system for Docker containers

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