Argo Description

Open-source solutions for Kubernetes enable efficient workflow management, cluster administration, and effective GitOps practices. These Kubernetes-native workflow engines allow for the implementation of both Directed Acyclic Graph (DAG) and step-based workflows, promoting a declarative approach to continuous delivery alongside a comprehensive user interface. They simplify advanced deployment strategies, such as Canary and Blue-Green, to streamline the process. Argo Workflows stands out as an open-source, container-native engine specifically designed for orchestrating parallel jobs within Kubernetes environments, implemented as a Custom Resource Definition (CRD). Users can design complex, multi-step workflows by arranging tasks sequentially or representing their dependencies through a graphical model. This capability enables the execution of demanding computational tasks, such as machine learning or data processing, significantly faster when utilizing Argo Workflows on Kubernetes. Moreover, CI/CD pipelines can be executed natively on Kubernetes, eliminating the need for complicated configurations typically associated with traditional software development tools. Built specifically for container environments, these tools avoid the burdens and constraints that come with legacy virtual machine and server-based systems, paving the way for more efficient operational practices. This makes Argo Workflows an essential component for modern cloud-native development strategies.

Integrations

API:
Yes, Argo has an API

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Company Details

Company:
Argo
Headquarters:
United States
Website:
argoproj.github.io

Media

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Product Details

Platforms
Windows
Linux
Types of Training
Training Docs
Webinars
Training Videos
Customer Support
Online Support

Argo Features and Options

Continuous Delivery Software

Application Lifecycle Management
Application Release Automation
Build Automation
Build Log
Change Management
Configuration Management
Continuous Deployment
Continuous Integration
Feature Toggles / Feature Flags
Quality Management
Testing Management

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