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Puppet Enterprise VS Metaflow

Compare Puppet Enterprise VS Metaflow and see what are their differences

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Puppet Enterprise logo Puppet Enterprise

Get started with Puppet Enterprise, or upgrade or expand.

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.
  • Puppet Enterprise Landing page
    Landing page //
    2023-06-24
  • Metaflow Landing page
    Landing page //
    2023-03-03

Puppet Enterprise features and specs

  • Scalability
    Puppet Enterprise is designed to manage thousands of nodes efficiently, making it a good fit for large-scale IT environments.
  • Automation
    It offers powerful automation capabilities, which help streamline repetitive tasks and reduce human error.
  • Compliance
    Puppet Enterprise includes strong compliance features, ensuring that the IT infrastructure adheres to various regulatory standards.
  • Pre-built Modules
    A wide array of pre-built modules is available, which can be used to quickly deploy and configure applications and services.
  • Reporting and Visibility
    Provides detailed reporting and dashboards, which offer insights into the status and performance of your infrastructure.
  • Integrations
    Seamless integration with various third-party tools and platforms, enhancing its functionality and adaptability to different environments.
  • Enhanced Security
    Supports role-based access control (RBAC) and other security features to protect sensitive infrastructure configurations.
  • Expert Support
    Access to professional support and services from the Puppet team, ensuring that issues can be resolved quickly and efficiently.

Possible disadvantages of Puppet Enterprise

  • Cost
    Puppet Enterprise can be expensive, especially for smaller organizations or startups with limited budgets.
  • Complexity
    The platform can be complex to set up and manage, requiring a learning curve for new users or administrators.
  • Resource Intensive
    Running Puppet Enterprise can consume significant system resources, which might impact the performance of smaller infrastructure.
  • Vendor Lock-in
    Once you have integrated Puppet into your infrastructure, migrating to another tool can be difficult and time-consuming.
  • Customization
    While there are many pre-built modules, creating custom modules can be complex and time-consuming, requiring extensive knowledge of Puppet's DSL.
  • Initial Setup
    The initial setup of Puppet Enterprise can be time-consuming and may require expert knowledge to configure correctly.
  • Documentation
    While there is extensive documentation available, it can sometimes be overwhelming or unclear for new users.

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.

Puppet Enterprise videos

+ Add

Sml merch Jeffy puppet review and more

More videos:

  • Review - Muppet Whatnot Workshop Puppet Review...(Kinda)
  • Demo - How Puppet works

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

Category Popularity

0-100% (relative to Puppet Enterprise and Metaflow)

Puppet Enterprise

Metaflow

DevOps Tools
89 89%
DevOps Tools
11% 11
Workflow Automation
0 0%
Workflow Automation
100% 100
Continuous Integration And Delivery
100 100%
Continuous Integration And Delivery
0% 0
Other Infrastructure Tools
100 100%
Other Infrastructure Tools
0% 0

User comments

Share your experience with using Puppet Enterprise and Metaflow. For example, how are they different and which one is better?
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Reviews

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

Puppet Enterprise Reviews

5 Best DevSecOps Tools in 2023
There are multiple providers for Infrastructure as Code such as AWS CloudFormation, RedHat Ansible, HashiCorp Terraform, Puppet, Chef, and others. It is advised to research each to determine what is best for any given situation since each has pros and cons. Some of these also are not completely free while others are. There are also some that are specific to a particular...
Source: devopsauthority.tech
What Are The Best Alternatives To Ansible? | Attune, Jenkins &, etc.
Puppet is a DevOps configuration management tool that is available for both open-source and enterprise versions. Puppet is an application developed by Puppet Labs and used to centralize and automate the procedure of configuration management.
Source: www.apzomedia.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Puppet uses a server/client architecture, requiring a longer installation process than Ansible, as an agentless system that only needs installation on the master node. In addition, Ansible uses YAML for configuration management while Puppet uses PuppetDSL with YAML datastore. The configuration management language style in Ansible is procedural, and that of Puppet is...
Source: www.servertribe.com
35+ Of The Best CI/CD Tools: Organized By Category
For those who are unfamiliar, Puppet Enterprise is the commercial version of Puppet, an open-source software management tool. It specializes in the automation of not just the configuration process but can also be used for patching, provisioning, and deployment.
Source: www.cloudzero.com
Chef vs Puppet vs Ansible
Puppet follows a master-agent or master-slave architecture. In the case of Puppet’s architecture, the master machine serves as the platform for running the Puppet server. The client machines provide the platforms for running Puppet clients as agents. In addition, the requirement of signing a certificate between the master machine and the agent adds complexity. Therefore,...
Source: www.whizlabs.com

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

Social recommendations and mentions

Based on our record, Metaflow seems to be a lot more popular than Puppet Enterprise. While we know about 14 links to Metaflow, we've tracked only 1 mention of Puppet Enterprise. 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.

Puppet Enterprise mentions (1)

  • Installing Puppet Enterprise 2021
    Now that the system requirements have been verified we need to download the Puppet Enterprise installer. To download the installer, go to the Puppet website to access the free 10 node trial (https://puppet.com/try-puppet/puppet-enterprise). - Source: dev.to / over 3 years ago

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

What are some alternatives?

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

Terraform - Tool for building, changing, and versioning infrastructure safely and efficiently.

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

Ansible - Radically simple configuration-management, application deployment, task-execution, and multi-node orchestration engine

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

Rancher - Open Source Platform for Running a Private Container Service

Azkaban - Azkaban is a batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

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