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As cloud-native, distributed architectures proliferate, the need for DevOps technologies and DevOps platform engineers has increased as well. DevOpsengineer tools can help ease the pressure as environment complexity grows. ” What does a DevOps platform engineer do? .”
Non-compliance and misconfigurations thrive in scalable clusters without continuous reporting. Compliance auditing is a challenge. Kubernetes’s ephemeral nature and limited logging make compliance auditing a nightmare. There is a high likelihood of uncontrolled attack surfaces.
DevOps and platform engineering are essential disciplines that provide immense value in the realm of cloud-native technology and software delivery. Observability of applications and infrastructure serves as a critical foundation for DevOps and platform engineering, offering a comprehensive view into system performance and behavior.
With growing multicloud complexity and the need for organization-wide scalability, self-service and automation capabilities have become increasingly essential for developer productivity. In response to this shift, platform engineering is growing in popularity. Why is platform engineering important?
As organizations accelerate innovation to keep pace with digital transformation, DevOps observability is becoming a critical key to success for DevOps and DevSecOps teams. DevOps and DevSecOps practices help organizations release software faster and more frequently, paving the way for digital transformation.
With hands-on experience in AWS DevOps and Google SRE, I’d like to offer my insights on the comparison of these two systems. Both have proven to be effective in delivering scalable and reliable services for cloud providers. DevOpsDevOps is a widely used term with multiple interpretations.
Platform engineering is on the rise. According to leading analyst firm Gartner, “80% of software engineering organizations will establish platform teams as internal providers of reusable services, components, and tools for application delivery…” by 2026.
Cloud-native environments bring speed and agility to software development and operations (DevOps) practices. So which is it: SRE vs DevOps, or SRE and DevOps? DevOps is focused on optimizing software development and delivery, and SRE is focused on operations processes. DevOps as a philosophy. SRE vs DevOps?
With the world’s increased reliance on digital services and the organizational pressure on IT teams to innovate faster, the need for DevOps monitoring tools has grown exponentially. But when and how does DevOps monitoring fit into the process? And how do DevOps monitoring tools help teams achieve DevOps efficiency?
Organizations are increasingly adopting DevOps to stay competitive, innovate faster, and meet customer needs. By helping teams release new software more frequently, DevOps practices are an essential component of digital transformation. Thankfully, DevOps orchestration has evolved to address these problems. What is orchestration?
In the dynamic realm of modern software development and operations, terms such as Platform Engineering, DevOps, and Site Reliability Engineering (SRE) are frequently used, sometimes interchangeably, often causing confusion among professionals entering or navigating these domains.
What is site reliability engineering? Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. Dynatrace news. SRE bridges the gap between Dev and Ops teams.
As organizations mature on their digital transformation journey, they begin to realize that automation – specifically, DevOps automation – is critical for rapid software delivery and reliable applications. In turn, manual approaches to identifying code issues and troubleshooting are not scalable. This statistic is despite the $9.1
I spoke with Martin Spier, PicPay’s VP of Engineering, about the challenges PicPay experienced and the Kubernetes platform engineering strategy his team adopted in response. This created problems with both visibility and scalability. Platform engineering looks to bring in a unified toolset.” billion. .
Planned effort Site Reliability Engineering (SRE) effort and time allocation planning typically fall into two domains: Operations Management (50%) Operations Management includes on-call responsibilities, post-mortem assessments, addressing other interruptions, and buffer time. These practices are commonly known as “ chaos engineering. ”
Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. DevOps teams must constantly adapt by using agile methodologies and rapid delivery models, such as CI/CD.
In the world of DevOps and SRE, DevOps automation answers the undeniable need for efficiency and scalability. Though the industry champions observability as a vital component, it’s become clear that teams need more than data on dashboards to overcome persistent DevOps challenges.
This allows developers, DBA’s and DevOpsengineers to quickly automate their backups, create new SQL and NoSQL clusters, and monitor the performance of their databases for their application without requiring any internal database expertise. Outsourced Security and Administration.
Everything you need to know about performance engineering. As highly distributed apps become more complex, developers need to ensure their systems are as user-friendly, secure, and scalable as possible. You may also like: A Short History of Performance Engineering.
Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, The Modern DevOps Lifecycle: Shifting CI/CD and Application Architectures. Complementing these practices is site reliability engineering (SRE), a discipline ensuring system reliability, performance, and scalability.
That’s why traceability, scalability, and reliability are crucial aspects of a cloud strategy, and for this county, OpenShift and Dynatrace delivered on these needs. Dynatrace’s AI engine, Davis automatically identified high traffic surges on the county website as the fire took hold. Map of Woolsey Fire Burn Area National Park Service.
The time and effort saved with testing and deployment are a game-changer for DevOps. This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. In production, containers are easy to replicate. What is Docker? Docker Hub is similar in functionality to GitHub.
Additionally, the Dynatrace Automation Engine will leverage SLO alerts to create event-triggered workflows to inform relevant stakeholders, provide reports, or automatically kick off remediation activities. At the same time, dedicated configuration-as-code support in Monaco and Terraform will provide a scalable, automated solution.
For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. One way to apply improvements is transforming the way application performance engineering and testing is done. Here is the definition of this model: ?. Try it today using Keptn .
Without the ability to see the logs that are relevant to your service, infrastructure, or cloud function—at exactly the right time and in exactly the right format—your cloud or DevOpsengineers lose the ability to find the root causes of the issues they troubleshoot.
The goal was to develop a custom solution that enables DevOps and engineering teams to analyze and improve pipeline performance issues and alert on health metrics across CI/CD platforms. Drill-downs into details from the overview page allow comparing different CI/CD vendors and offer further insights to analyze and fix bottlenecks.
Dynatrace scored highest across 4 of 5 use cases, DevOps/AppDev, SRE/CloudOps, IT Operations, and Digital Experience Monitoring, and second highest in the Application Owner/Line of Business use case. We anticipated the industry’s move to dynamic multicloud environments and DevOps processes.
According to one statistic, 76% of digital teams are responsible for delivering revenue , so software reliability and scalability are an increasing focus as these teams contribute to the bottom line. Chaos engineering. As teams develop software more quickly, they can’t rely on manual methods to test applications. Auto-remediation.
But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise. All these tasks can take place seamlessly—ultimately resolving issues or identifying them proactively—and without needing to interrupt an engineer from a more strategic task.
This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Can’t we just fold it into existing DevOps best practices?
Messaging systems can significantly improve the reliability, performance, and scalability of the communication processes between applications and services. To know which services are impacted, DevOps teams need to know what’s happening with their messaging systems. – DevOpsEngineer, large healthcare company.
Understanding the difference between observability and monitoring helps DevOps teams understand root causes and deliver better applications. DevOps and DevSecOps orchestration. DevOps brings developers and operations teams together and enables more agile IT. What is DevOps? Learn how security improves DevOps.
2020 cemented the reality that modern software development practices require rapid, scalable delivery in response to unpredictable conditions. Using a microservices approach, DevOps teams split services into functional APIs instead of shipping applications as one collective unit. Dynatrace news. Microservices managed.
2020 cemented the reality that modern software development practices require rapid, scalable delivery in response to unpredictable conditions. Using a microservices approach, DevOps teams split services into functional APIs instead of shipping applications as one collective unit. Dynatrace news. Microservices managed.
Grail, the Dynatrace causational data lakehouse with a massively parallel processing analytics engine, unites observability, security, and business data from multicloud and cloud-native environments while retaining the data’s context to deliver precise answers in real time. DevOps metrics and digital experience data are critical to this.
As Porsche Informatik migrated from a monolithic environment to a containerized, hybrid-cloud landscape, OpenShift facilitated greater agility and scalability of their Kubernetes-orchestrated DevOps projects, boosting both the company’s ability to innovate and reduce time to market. Want to try it and see for yourself?
Only 38% of CISOs agree that employing more IT operations, DevOps, and site reliability engineering professionals will help overcome complexities. Rather, 86% believe extending a DevSecOps culture to more teams and applications will be key to accelerating digital transformation and driving faster, more secure software delivery.
Software companies who have already been following and adopting DevOps and site reliability engineering (SRE) practices alongside their shared ancestry in agile concepts came out on top – especially if they adopted those practices across the whole organization and customer value stream.
Composite’ AI, platform engineering, AI data analysis through custom apps This focus on data reliability and data quality also highlights the need for organizations to bring a “ composite AI ” approach to IT operations, security, and DevOps. Enter causal AI.
That’s why we have Dynatrace extended (not shifted) to the left to address both needs: developers have easy and safe access to staging and production deployments while central SRE and DevOps teams have the scalable and automatic observability they need to remain compliant, consistent, and resilient.
Many architects and engineers know the Cloud is the future of development and IT and the are gearing up to be as succesful as possible in this new normal. Today, I am pleased to announce that we have released this exam publicly, as well as awarded our first DevOpsEngineer certifications to those who successfully completed the beta exam.
Gone are the days for Christian manually looking at dashboards and metrics after a new build got deployed into a testing or acceptance environment: Integrating Keptn into your existing DevOps tools such as GitLab is just a matter of an API call. Automate Performance aka Performance as a Self-Service: Watch SRE-Driven Performance Engineering.
More: @swardley : I occasionally hear companies announcing they are kicking off a "DevOps" program and I can't but feel sympathy for them. Contraining the engineers tends to lead to poorer results; giving them choices produces a better chance of success. . it's a bit sad really. cornazano : Building the wrong thing is a nightmare.
The Dynatrace Software Intelligence Platform accelerates cloud operations, helping users achieve service-level objectives (SLOs) with automated intelligence and unmatched scalability. Automatic observability and root-cause analysis for DevOps, cloud, and apps teams. Built for enterprise scalability.
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