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Terraform VS Metaflow

Compare Terraform VS Metaflow and see what are their differences

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Terraform logo Terraform

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

Metaflow logo Metaflow

Framework for real-life data science; build, improve, and operate end-to-end workflows.
  • Terraform Landing page
    Landing page //
    2023-09-24
  • Metaflow Landing page
    Landing page //
    2023-03-03

Terraform features and specs

  • Infrastructure as Code
    Terraform allows you to define your infrastructure in configuration files that can be versioned and stored in a version control system. This makes it easy to track changes, roll back if necessary, and collaborate with team members.
  • Multi-Cloud Support
    Terraform supports various cloud providers such as AWS, Azure, Google Cloud, and others. This allows you to manage your entire infrastructure using a single tool, regardless of the underlying provider.
  • Immutability
    Terraform promotes immutable infrastructure, meaning once a component is created, it is not modified in place but replaced if changes are needed. This leads to more predictable and stable deployments.
  • State Management
    Terraform maintains the state of your infrastructure, which helps in tracking resource changes over time and making incremental updates. This is crucial for applying changes in a controlled manner.
  • Community and Ecosystem
    Terraform has a large and active community, along with a rich ecosystem of providers and modules. This makes it easier to find support, share solutions, and leverage pre-built components.

Possible disadvantages of Terraform

  • Complex State Management
    While state management is a significant feature, managing state files can become complex and risky. Issues like state file corruption or sharing between team members can lead to challenges.
  • Learning Curve
    Terraform has a steep learning curve for beginners, especially those who are not familiar with infrastructure as code concepts or the HashiCorp Configuration Language (HCL).
  • Partial Updates
    Terraform's plan and apply operations are not atomic, meaning that partial updates can sometimes leave your infrastructure in an inconsistent state if an error occurs during execution.
  • Dependency Management
    Managing dependencies between resources can be challenging in Terraform. Misconfigured dependencies can lead to issues during resource creation, deletion, or updates.
  • Cost Management
    While Terraform is excellent for provisioning resources, it does not have built-in cost management or optimization features. Users need to rely on third-party tools to manage and optimize costs.

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.

Terraform videos

+ Add

Wampler Terraform | Reverb Tone Report Demo

More videos:

  • Review - MOD PEDAL POWERHOUSE! Wampler TERRAFORM
  • Demo - IT'S FINALLY HERE! | Wampler Terraform Demo | It's as good as you hoped!!!

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 Terraform and Metaflow)

Terraform

Metaflow

DevOps Tools
90 90%
DevOps Tools
10% 10
Workflow Automation
0 0%
Workflow Automation
100% 100
Developer Tools
91 91%
Developer Tools
9% 9
Continuous Integration And Delivery
100 100%
Continuous Integration And Delivery
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 Terraform and Metaflow

Terraform Reviews

Do not use AWS CloudFormation
Terraform, on the other hand, will occupy your shell until the directly-involved AWS service coughs up an error. No additional tooling is required. Terraform will just relay the error message from the affected service indicating what you’ve done wrong.
Source: gswallow.medium.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Although Terraform and Ansible are both server automation tools, there are still a few significant differences between the two. For example, Terraform is declarative while Ansible allows for both procedural configurations and declarative configurations. Also, Ansible works best as a configuration management tool while Terraform leans towards cloud orchestration.
Source: www.servertribe.com
35+ Of The Best CI/CD Tools: Organized By Category
Terraform is compatible with a wide range of Cloud providers, including Azure, VMWare, and AWS. If you’re subscribed to multiple cloud providers, Terraform is a great way to ensure that they have consistent configurations.
Source: www.cloudzero.com
Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation
Example: Terraform and Ansible. You use Terraform to deploy all the underlying infrastructure, including the network topology (i.e., VPCs, subnets, route tables), data stores (e.g., MySQL, Redis), load balancers, and servers. You then use Ansible to deploy your apps on top of those servers.This is an easy approach to start with, as there is no extra infrastructure to run...
Source: blog.gruntwork.io
Ansible overtakes Chef and Puppet as the top cloud configuration management tool
Breaking these results down year-over-year, use of Ansible grew from 36% in 2018 to 41% in 2019--surpassing Chef, which grew from 36% to 37%, as well as Puppet, which grew from 34% to 37%. Rounding out the list is Terraform, which experienced a jump from 20% to 31%, and Salt, which increased in usage from 13% to 18%.
Source: www.techrepublic.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, Terraform should be more popular than Metaflow. It has been mentiond 32 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.

Terraform mentions (32)

  • Scaffolding Serverless Web Application on AWS
    Terraform is an infrastructure as code tool that lets you build, change, and version infrastructure safely and efficiently. Terraform code is in the terraform directory. - Source: dev.to / 10 months ago
  • Integrating Terraform with CI/CD Pipelines
    In recent years, there has been a significant shift towards automation of infrastructure deployment processes. One popular tool that has emerged as a key player in this space is Terraform, an open-source infrastructure as code (IaC) software tool developed by HashiCorp. This article will explore how Terraform can be integrated into continuous integration and delivery (CI/CD) pipelines using GitHub Actions as an... - Source: dev.to / about 1 year ago
  • Deploying Your Outdoor Activities Map with Terraform
    Terraform is an open-source infrastructure-as-code software tool created by HashiCorp. It allows you to define and manage your infrastructure as code, making it easy to provision and manage resources across multiple cloud providers. With Terraform, you can ensure consistent and repeatable deployments, making it an ideal choice for automating your cloud infrastructure. - Source: dev.to / over 1 year ago
  • Trigger CI using Terraform Cloud
    Continuous Integration(CI) pipelines needs a target infrastructure to which the CI artifacts are deployed. The deployments are handled by CI or we can leverage Continuous Deployment pipelines. Modern day architecture uses automation tools like terraform, ansible to provision the target infrastructure, this type of provisioning is called IaaC. - Source: dev.to / about 2 years ago
  • Using Let's Encrypt with the Puppet Enterprise console
    Had an itch I've been meaning to scratch for a while. I build my Puppet environment using Terraform, which makes it nice and easy to tear things down and rebuild them. That is great, but it does leave me with an issue when it comes to the console SSL certificates. - Source: dev.to / about 2 years ago
View more

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 Terraform and Metaflow, you can also consider the following products

Rancher - Open Source Platform for Running a Private Container Service

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

Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.

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

Packer - Packer is an open-source software for creating identical machine images from a single source configuration.

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

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