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Otter is an infrastructure automation tool that runs under Microsoft Windows, designed by the software company Inedo. Otter utilizes Infrastructure as Code to model infrastructure and configuration.
Application lifecycle management (ALM) is the product lifecycle management of computer programs. It encompasses requirements management, software architecture, computer programming, software testing, software maintenance, change management, continuous integration, project management, and release management.
Build automation is the process of automating the creation of a software build and the associated processes including: compiling computer source code into binary code, packaging binary code, and running automated tests.
This is a comparison of notable free and open-source configuration management software, suitable for tasks like server configuration, orchestration and infrastructure as code typically performed by a system administrator.
Electric Cloud, Inc. was a privately held, DevOps software company based in San Jose, CA. Founded in 2002, Electric Cloud was a provider of application release orchestration (ARO) tools, automating release pipelines and managing application life cycles. Electric Cloud's products included ElectricFlow and ElectricAccelerator.
AnthillPro is a software tool originally developed and released as one of the first continuous integration servers. AnthillPro automates the process of building code into software projects and testing it to verify that project quality has been maintained. Software developers are able to identify bugs and errors earlier by using AnthillPro to track, collate, and test changes in real time to a collectively maintained body of computer code.
Parasoft is an independent software vendor specializing in automated software testing and application security with headquarters in Monrovia, California. It was founded in 1987 by four graduates of the California Institute of Technology who planned to commercialize the parallel computing software tools they had been working on for the Caltech Cosmic Cube, which was the first working hypercube computer built.
Progress Chef is a configuration management tool written in Ruby and Erlang. It uses a pure-Ruby, domain-specific language (DSL) for writing system configuration "recipes". Chef is used to streamline the task of configuring and maintaining a company's servers, and can integrate with cloud-based platforms such as Amazon EC2, Google Cloud Platform, Oracle Cloud, OpenStack, IBM Cloud, Microsoft Azure, and Rackspace to automatically provision and configure new machines. Chef contains solutions for both small and large scale systems.
Release management is the process of managing, planning, scheduling and controlling a software build through different stages and environments; it includes testing and deploying software releases.
DevOps is a methodology in the software development and IT industry. Used as a set of practices and tools, DevOps integrates and automates the work of software development (Dev) and IT operations (Ops) as a means for improving and shortening the systems development life cycle. DevOps is complementary to agile software development; several DevOps aspects came from the agile way of working.
Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate. Continuous testing was originally proposed as a way of reducing waiting time for feedback to developers by introducing development environment-triggered tests as well as more traditional developer/tester-triggered tests.
Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably released at any time and following a pipeline through a "production-like environment", without doing so manually. It aims at building, testing, and releasing software with greater speed and frequency. The approach helps reduce the cost, time, and risk of delivering changes by allowing for more incremental updates to applications in production. A straightforward and repeatable deployment process is important for continuous delivery.
Application-release automation (ARA) refers to the process of packaging and deploying an application or update of an application from development, across various environments, and ultimately to production. ARA solutions must combine the capabilities of deployment automation, environment management and modeling, and release coordination.
Ansible is a suite of software tools that enables infrastructure as code. It is open-source and the suite includes software provisioning, configuration management, and application deployment functionality.
BuildMaster is an application release automation tool, designed by the software development team Inedo. It combines build management and ARA capabilities to manage and automate processes primarily related to continuous integration, database change scripts, and production deployments, overall releasing applications reliably. The tool is browser-based and able to be used "out-of-the-box". Its feature set and scope puts it in line with the DevOps movement, and is marketed as "more than a release automatigs together the people, processes, and practices that allow teams to deliver software rapidly, reliably, and responsibly.” It's a tool that embodies incremental DevOps adoption.
XebiaLabs is an independent software company specializing in DevOps and continuous delivery for large enterprise organizations. XebiaLabs offers a DevOps Platform for application-release automation (ARO). These components include release orchestration, deployment automation and DevOps intelligence.
Infrastructure as code (IaC) is the process of managing and provisioning computer data center resources through machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. The IT infrastructure managed by this process comprises both physical equipment, such as bare-metal servers, as well as virtual machines, and associated configuration resources. The definitions may be in a version control system, rather than maintaining the code through manual processes. The code in the definition files may use either scripts or declarative definitions, but IaC more often employs declarative approaches.
A DevOps toolchain is a set or combination of tools that aid in the delivery, development, and management of software applications throughout the systems development life cycle, as coordinated by an organisation that uses DevOps practices.
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.
References
- 1 2 3 Fletcher, Colin; Cosgrove, Terrence (26 August 2015). Innovation Insight for Continuous Configuration Automation Tools. Gartner (Report).[ dead link ]
- ↑ Ramos, Martin (4 November 2015). "Continuous Integration: Infrastructure as Code in DevOps". easydynamics.com. Archived from the original on 6 February 2016. Retrieved 11 May 2016.
- ↑ Infrastructure As Code: Fueling the Fire for Faster Application Delivery (Report). Forrester. March 2015.
- ↑ Phillips, Andrew (14 May 2015). "Moving from Infrastructure Automation to True DevOps". DevOps.com.
- ↑ Venezia, Paul (21 November 2013). "Puppet vs. Chef vs. Ansible vs. Salt". Network World . Network World. Archived from the original on 18 July 2018. Retrieved 14 December 2015.
- ↑ Garner Market Trends: DevOps – Not a Market, but Tool-Centric Philosophy That supports a Continuous Delivery Value Chain (Report). Gartner. 18 February 2015.
- ↑ "CFEngine 3.18.0 Documentation - What is CFEngine?".
- ↑ Fletcher, Colin; Cosgrove, Terrence (25 March 2016). How I&O teams can combine CCA tools With Containers to Achieve Operational Efficiecies. Gartner (Report).
- ↑ Fletcher, Colin; Cosgrove, Terrence (8 December 2016). Market Guide for Continuous Configuration Automation Tools. Gartner (Report).