#devops

#devops

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

For almost ten years, the DORA research initiative has explored the effectiveness and metrics of top-performing organizations driven by technology. This effort has gathered insights from over 36,000 professionals from various sizes of organizations and a wide range of industries. DORA aims to unravel the link between operational practices (capabilities) and their outcomes, focusing on significant achievements both organization-wide and to its members.

It has been a dramatic and challenging year for developers and engineers working in devops organizations. More companies are using AI and automation for both development and IT operations, including for writing requirements, maintaining documentation, and vibe coding. Responsibilities have also increased, as organizations expect devops teams to improve data quality, automate AI agent testing, and drive operational resiliency. AI is driving new business expectations and technical capabilities, and devops engineers must keep pace with the speed of innovation.

As the name suggests, sociotechnical refers to the ways in which social and technical aspects of an organization relate to one another. In the context of technology companies, you can think of the organization itself as a sociotechnical system. Something that's important to note is that sociotechnical theory doesn't just acknowledge these two aspects, it emphasizes that they're inherently interconnected.

However, the job of a software developer today is about more than just writing code. Thanks to the prevalence of DevOps, software developers tend to have a huge responsibility: they own the feature development, bug fixes, deployment pipeline, performance monitoring, cloud infrastructure and security of their code. Software development trends are constantly adapting to help software teams keep pace with the growing requirements of an increasingly digital society, and business leaders are recognising the value of software-powered business initiatives.

A portmanteau of "development" and "operations," devops emerged as a way of bringing together two previously separate groups responsible for the building and deploying of software. In the old world, developers (devs) typically wrote code before throwing it over to the system administrators (operations, or ops) to deploy and integrate that code. But as the industry shifted towards agile development and cloud-native computing, many organizations reoriented around modern, cloud-native practices in the pursuit of faster, better releases.

Most of the time, databases are considered separate entities with their own development cycles. As a result, there is a major disconnect between database development and DevOps. You often see a situation where the application gets updated quickly with bug fixes and new features, yet the databases are lagging behind, slowing down the release process. The Isolation of databases from DevOps created a gap between the application developers and database admins/devs that led to bottlenecks and errors in the development process.

JFrog (NASDAQ: FROG) stunned investors with a blowout third quarter that sent shares soaring more than 24%. The DevOps platform provider not only delivered its eighth straight beat on both revenue and earnings but also showcased accelerating cloud adoption, record profitability, and a growing foothold in AI-driven software delivery. Here are three key takeaways from the results and management's commentary.

The query builder solves a large SQL bottleneck by transforming log analysis from a time-consuming task into a real-time, self-service capability that's fit for any DevOps or site reliability professional. This is an immense time-saver that can collapse typical investigation windows from hours to minutes,

We are a Berlin company with international employees working on decentral collaborative AI services in real estate. We change how people live and work, by making buildings think and act adaptive to occupant needs. It is not another App. We save highest possible energy and run AI in compliance with GDPR. We look for developers who know AI on edge as well as power of Edge&Cloud.

"Bringing DevOps Principles to Controls and Audits", not the most exciting title in the world, but I'm actually going to be talking about a revolution. What I'm talking about is an open-source project that me and my colleagues at Container Solutions are working on. My goal is that by the end of it, you'll be interested enough to check it out, perhaps give feedback, maybe even get involved.

With the new refactor feature, the company addresses a significant pain point: previously, any change to a resource's logical ID caused by renaming or moving a construct would force AWS CloudFormation to delete and then recreate the resource. This destructive process often led to data loss and downtime for stateful resources, such as databases, causing many developers to avoid refactoring altogether.

Designed to be integrated with continuous integration/continuous deployment (CI/CD) platforms such as Jenkins and others, the Zencoder AI agent can resolve issues, implement fixes, improve code quality, generate and run tests, and create documentation. As such, the goal is not just to write more code faster, but rather enable DevOps teams to take advantage of AI agents running in the background to re-engineer workflows in ways that result in more applications being deployed faster, said Filev.

For years,mainframes were synonymous with stability, but rarely with innovation. That image seems to be changing rapidly. New figures from BMC's annual mainframe survey show that confidence in the future of the platform has never been higher. No less than 97 percent of the professionals surveyed indicate that mainframes will remain part of their IT infrastructure. Increasingly, the system is even seen as a driver for new workloads.

System Initiative recently released its AI Native Infrastructure Automation platform, aiming to offer DevOps teams a new way to manage infrastructure through natural language. With this release, users can type simple prompts, such as "make load balancer health checks more aggressive", and the system's AI agent will discover relevant infrastructure, simulate proposed changes, and execute updates upon approval. System Initiative claims all of this will occur while maintaining full automation and safety within live environments.

Innovation simply can't wait. Not today, when rapid evolution and change are the baseline, the table stakes.

Vibe coding is AI-powered, collaborative code creation, where the "vibe" - the team's culture, coding style and collaborative preferences - is harnessed as an operational parameter. Imagine pairing human strengths with advanced generative AI, capturing not just code syntax and logic, but the subtle preferences, patterns and conventions that make YOUR team effective. This isn't about getting AI to write a few snippets. It's about the AI learning your team's DNA.

Fresh off raising $7.2 million in funding, SRE.ai CEO Raj Kadiyala said as the volume of code developed using low-code/no-code and, more recently, artificial intelligence (AI) tools increases, there needs to be a more automated approach to deploying that software based on best DevOps processes that also enables software development teams to monitor and observe those applications. At the core of that effort is a platform that will leverage artificial intelligence (AI) technologies to automate the deployment

The Org Intelligence module added to the Copado AI Platform leverages metadata exposed on the Salesforce platform to extrapolate relationships, promising an 80% reduction in discovery time.

A stack-based approach to IDPs emphasizes reusability, autonomy, and visibility, creating a standardized but flexible system where teams can define and deploy their own devops stack.

Blazor's rise aligns with transformative shifts in the DevOps landscape, particularly with AI and ML integration, which enhances DevOps processes and app performance.