Are you wondering how you can automate application development and reduce costs while improving product quality? One of the best ways is to use DevOps allowing organizations to deliver functionality at high velocity.
Companies across different industries employ a DevOps model, for instance, Amazon, Google, Netflix, Etsy, Frontier, Biberk, Capital One, Alphapoint, and Vendasta. In this article, we will consider the main benefits of DevOps for business. Find out how you can gain a competitive advantage.
What is DevOps?
DevOps is a set of practices combining software development (Dev) and IT operations (Ops). These practices include continuous integration, continuous delivery, microservices architecture, infrastructure as code, monitoring and logging, communication and collaboration. Aiming to accelerate and integrate processes between software engineering and IT teams, DevOps enables organizations to build, test, and deploy apps and updates faster.
With a focus on organizational culture, DevOps bridges the gap between development and operations teams by implementing practices such as information flow, shared responsibilities, and learning.
The key benefits of DevOps for business
There are many advantages of using DevOps best practices, from simplified infrastructure maintenance and improved scalability to the enablement of high availability under heavy loads. Let’s take a closer look.
1. Software development speed
With DevOps, companies can deliver applications and features much faster than those organizations that use traditional methods of building software.
Thanks to DevOps, it is possible to reduce time-to-market by 2-3 times, which is crucial for making the necessary customizations to meet customer demand. As a result, you not only cut down expenses but outrun competitors that have not yet adopted DevOps practices.
High development speed is achieved through continuous integration (CI) and continuous delivery (CD). CI enables DevOps teams to automate the process of app assembling by instantly detecting errors in the code and making rapid changes.
CD expands upon CI by moving code changes to the test and/or production environment. Due to continuous delivery, engineers always have an artifact that has already been tested and is ready for deployment.
2. Scalability
DevOps allows developers to provide scalability, the ability of a software system to efficiently handle a growing amount of work, for example, support an increasing number of users and simultaneously process numerous requests.
By implementing infrastructure automation practices such as infrastructure as code, engineers enable elastic scaling of computing resources up or down.
3. High availability
High availability is one of the key benefits of DevOps for business. By creating microservices, it is possible to ensure that the system will be stable even under heavy loads. In this type of software architecture, engineers develop an app as a set of small loosely-coupled services, each running its own process.
Since they operate independently from each other, users can interact with your product even if some service fails. With monitoring and logging, DevOps experts track software and infrastructure performance, quickly detect issues, and investigate the root causes of failures.
4. Reliability
Employing DevOps best practices, companies ensure fast and reliable feature delivery. Through the enablement of scalability and high availability, organizations provide stable operation of their services 24/7.
Although ML can take a while to be implemented, if the algorithms and network architectures are correctly aligned, the machine learning system will start producing the results that correspond to the actual ones.
ML as a rescue ranger for DevOps
Algorithms of machine analysis and learning allow you to monitor information objects (e.g., databases, applications, etc.)
The system determines itself how the objects should function adequately, and for additional adjustments, parameterization mechanisms would suffice.
Adjustment mechanisms help make the algorithms more accurate, as well as adapt them to specific needs.
Using ML can reveal anomalies in this data such as large amounts of code, long build times, extended release times and code checks, and identify many “deviations” in software development processes, including inefficient use of resources, frequent task switching, or slowing down the process.
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