Airflow vs Kubeflow

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

Kubeflow

Stacks205

Followers585

Votes18

Airflow vs Kubeflow: What are the differences?

Introduction

Airflow and Kubeflow are both popular tools used in data engineering and data science workflows. While they both have the goal of managing and orchestrating complex workflows, there are several key differences between the two that set them apart and make them suitable for different use cases.

Architecture: Airflow is a task scheduler and workflow management platform that uses Directed Acyclic Graphs (DAGs) to define and execute tasks. It runs on a centralized server and relies on a scheduler to trigger task executions. On the other hand, Kubeflow is an open-source machine learning toolkit that runs natively on Kubernetes. It leverages the container orchestration capabilities of Kubernetes to distribute and scale workloads.
Decentralized Execution: In Airflow, tasks are executed by workers running on separate machines or nodes. The tasks are scheduled and coordinated by the central Airflow server. Kubeflow, however, enables decentralized execution by running tasks within containers on a Kubernetes cluster. This allows for efficient resource allocation, scaling, and fault tolerance.
Focus: Airflow primarily focuses on workflow management and scheduling, allowing users to define and orchestrate tasks. It provides a rich set of operators and connectors for integration with various systems and services. Kubeflow, on the other hand, is specifically designed for the deployment and management of machine learning workflows. It provides tools and components tailored to the machine learning lifecycle, such as data preprocessing, model training, and serving.
Integration with Kubernetes: While Airflow can run on Kubernetes to achieve containerization and scalability, it is not tightly integrated with Kubernetes as a native solution. In contrast, Kubeflow is built on top of Kubernetes and leverages its features for container orchestration, automatic scaling, and workload management. Kubeflow also provides additional components, such as Kubeflow Pipelines, for building and deploying machine learning workflows.
Community and Ecosystem: Airflow has a mature and active community with a wide range of contributed operators, connections, and plugins. It has been extensively adopted and used by many organizations. Kubeflow, being a more specialized tool, has a growing community focused on machine learning workflows. It offers integration with popular machine learning frameworks and libraries and benefits from the broader Kubernetes ecosystem.
Use Cases: Airflow is suitable for a variety of use cases beyond machine learning, such as data pipelines, ETL (Extract, Transform, Load) processes, and workflow automation. It provides flexibility and extensibility for diverse data engineering and data science workflows. Kubeflow, on the other hand, shines in the machine learning domain, providing features specifically tailored for building, training, and serving machine learning models at scale.

In Summary, Airflow and Kubeflow differ in their architecture, execution model, focus, integration with Kubernetes, community, and use cases. While Airflow is a general-purpose workflow management platform, Kubeflow is a specialized toolkit for machine learning workflows on Kubernetes.

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Advice on Airflow, Kubeflow

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	Kubeflow
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.	The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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.	-
Statistics
Stacks 1.7K	Stacks 205
Followers 2.8K	Followers 585
Votes 128	Votes 18
Pros & Cons
Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 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	Pros 9 System designer 3 Customisation 3 Kfp dsl 3 Google backed 0 Azure
Integrations
No integrations available	Kubernetes Jupyter TensorFlow

What are some alternatives to Airflow, Kubeflow?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Apache Beam

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

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.

Related Comparisons

Airflow vs Kubeflow: What are the differences?

Introduction

Architecture: Airflow is a task scheduler and workflow management platform that uses Directed Acyclic Graphs (DAGs) to define and execute tasks. It runs on a centralized server and relies on a scheduler to trigger task executions. On the other hand, Kubeflow is an open-source machine learning toolkit that runs natively on Kubernetes. It leverages the container orchestration capabilities of Kubernetes to distribute and scale workloads.
Decentralized Execution: In Airflow, tasks are executed by workers running on separate machines or nodes. The tasks are scheduled and coordinated by the central Airflow server. Kubeflow, however, enables decentralized execution by running tasks within containers on a Kubernetes cluster. This allows for efficient resource allocation, scaling, and fault tolerance.
Focus: Airflow primarily focuses on workflow management and scheduling, allowing users to define and orchestrate tasks. It provides a rich set of operators and connectors for integration with various systems and services. Kubeflow, on the other hand, is specifically designed for the deployment and management of machine learning workflows. It provides tools and components tailored to the machine learning lifecycle, such as data preprocessing, model training, and serving.
Integration with Kubernetes: While Airflow can run on Kubernetes to achieve containerization and scalability, it is not tightly integrated with Kubernetes as a native solution. In contrast, Kubeflow is built on top of Kubernetes and leverages its features for container orchestration, automatic scaling, and workload management. Kubeflow also provides additional components, such as Kubeflow Pipelines, for building and deploying machine learning workflows.
Community and Ecosystem: Airflow has a mature and active community with a wide range of contributed operators, connections, and plugins. It has been extensively adopted and used by many organizations. Kubeflow, being a more specialized tool, has a growing community focused on machine learning workflows. It offers integration with popular machine learning frameworks and libraries and benefits from the broader Kubernetes ecosystem.
Use Cases: Airflow is suitable for a variety of use cases beyond machine learning, such as data pipelines, ETL (Extract, Transform, Load) processes, and workflow automation. It provides flexibility and extensibility for diverse data engineering and data science workflows. Kubeflow, on the other hand, shines in the machine learning domain, providing features specifically tailored for building, training, and serving machine learning models at scale.

Airflow vs Kubeflow

Overview