This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Non-compliance and misconfigurations thrive in scalable clusters without continuous reporting. Continuous visibility and assessment provide platform engineering, DevSecOps, DevOps, and SRE teams with the ability to track, validate, and remediate potential compliance-relevant findings and create the necessary evidence for the auditing process.
As organizations accelerate innovation to keep pace with digital transformation, DevOps observability is becoming a critical key to success for DevOps and DevSecOps teams. This drive for speed has a cost: 22% of leaders admit they’re under so much pressure to innovate faster that they must sacrifice code quality.
But to be scalable, they also need low-code/no-code solutions that don’t require a lot of spin-up or engineering expertise. With the Dynatrace modern observability platform, teams can now use intuitive, low-code/no-code toolsets and causal AI to extend answer-driven automation for business, development and security workflows.
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?
As enterprises expand their software development practices and scale their DevOps pipelines, effective management of continuous integration (CI) and continuous deployment (CD) processes becomes increasingly important. It is critical for managing code repositories, automating tasks, and enabling collaboration among development teams.
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?
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?
As cloud-native, distributed architectures proliferate, the need for DevOps technologies and DevOps platform engineers has increased as well. DevOps engineer tools can help ease the pressure as environment complexity grows. ” What does a DevOps platform engineer do? A DevOps platform engineer is a more recent term.
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.
That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth. All of these factors challenge DevOps maturity. Data scale and silos present challenges to DevOps maturity DevOps teams often run into problems trying to drive better data-driven decisions with observability and security data.
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.
HashiCorp’s Terraform is an open-source infrastructure as a code software tool that provides a consistent CLI workflow to manage hundreds of cloud services. With this integration, Dynatrace customers can now leverage Terraform to manage their monitoring infrastructure as code,” said Asad Ali, Senior Director of Sales Engineering at Dynatrace.
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.
Infrastructure as code is a way to automate infrastructure provisioning and management. In this blog, I explore how Dynatrace has made cloud automation attainable—and repeatable—at scale by embracing the principles of infrastructure as code. Transparency and scalability. Infrastructure-as-code.
With growing multicloud complexity and the need for organization-wide scalability, self-service and automation capabilities have become increasingly essential for developer productivity. Platform engineers design and implement these platforms, as well as ensure their security, scalability, and reliability.
The old saying in the software development community, “You build it, you run it,” no longer works as a scalable approach in the modern cloud-native world. The ability to effectively manage multi-cluster infrastructure is critical to consistent and scalable service delivery. Monitoring-as-code can also be configured in GitOps fashion.
The DevOps playbook has proven its value for many organizations by improving software development agility, efficiency, and speed. This method known as GitOps would also boost the speed and efficiency of practicing DevOps organizations. Development teams use GitOps to specify their infrastructure requirements in code.
To implement SLOs in your software delivery cycle and consistently add observability measures from the beginning, Dynatrace “configuration as code” (Monaco and Dynatrace Terraform) will soon support the new API. At the same time, dedicated configuration-as-code support in Monaco and Terraform will provide a scalable, automated solution.
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? Here are some examples.
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. SRE applies DevOps principles to developing systems and software that help increase site reliability and performance.
Open source code, for example, has generated new threat vectors for attackers to exploit. Considering open source software (OSS) libraries now account for more than 70% of most applications’ code base, this threat is not going anywhere anytime soon. Security teams need their vulnerability management approach to be seamless.
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.
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. Faced with these requirements, Omnilogy carefully evaluated the following two options for implementing a solution to the pipeline observability challenge.
Process Improvements (50%) The allocation for process improvements is devoted to automation and continuous improvement SREs help to ensure that systems are scalable, reliable, and efficient. SREs invest significant effort in enhancing software reliability, scalability, and dependability. However, this is highly unlikely.
For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. PayPal, a popular online payment systems organization, implemented a full performance as a self-service model for developers to get their code performance tests.
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. SRE applies DevOps principles to developing systems and software that help increase site reliability and performance.
More than half (53%) of IT leaders confirm their organizations are forced to make choices among quality, security, and user experience to ensure they meet the need for instant service delivery. Further, 43% of IT leaders state they are forced to sacrifice code quality, and 29% say they sometimes sacrifice security.
While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Why: Data Makes It Different.
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 benefits.
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 benefits.
As a result, IT operations, DevOps , and SRE teams are all looking for greater observability into these increasingly diverse and complex computing environments. DevSecOps teams can tap observability to get more insights into the apps they develop, and automate testing and CI/CD processes so they can release better quality code faster.
Power boundless observability, security, and business analytics with Grail – resource center Discover everything you need to know about Grail, the core platform technology that unifies data while retaining its context to deliver fast, scalable, and cost-effective AI-powered answers and automation. What is IT automation? Learn more.
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?
AWS Lambda is a serverless compute service that can run code in response to predetermined events or conditions and automatically manage all the computing resources required for those processes. It also enables DevOps teams to connect to any number of AWS services or run their own functions. What is AWS Lambda? How does AWS Lambda work?
Moreover, the demand for rapid software delivery is putting additional stress on DevOps teams. Leveraging open source code and traditional monitoring tools can also increase the risk for vulnerabilities to enter the SDLC. For development teams, code building and review are critical.
Weaving security into the fabric of your DevOps practice prevents breaches and ensures the delivery of secure digital services. 34% of CIOs say they sacrifice code security to deliver innovation quicker. 34% of CIOs say they sacrifice code security to deliver innovation quicker. 249% increase in code base coverage on average.
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.
Being a software developer means much more than simply writing bug-free code. As highly distributed apps become more complex, developers need to ensure their systems are as user-friendly, secure, and scalable as possible.
For AWS Lambda, the largest contributor to startup latency is the time spent initializing an execution environment, which includes loading function code and initializing dependencies. With SnapStart enabled, function code is initialized once when a function version is published. Built for enterprise scalability. What is Lambda?
Cloud environments—including multicloud, hybrid, and cloud-native ecosystems—offer unmatched agility, scalability, and cost-effectiveness, though they also present new challenges and complexities that are impossible to manage manually. Another big advantage of automation-as-code is the scale at which automation is enabled.
We start with metrics, traces, and logs (that’s table stakes) but also provide context and enrichment through topology, behavior, code, metadata, and network, combined with data from application programming interfaces (API) and OpenTelemetry. DevOps and Cloud Ops Automation. Application Modernization.
In short, log management is how DevOps professionals and other concerned parties interact with and manage the entire log lifecycle. Optimally stored logs enable DevOps, SecOps, and other IT teams to access them easily. As logs are generated, log variability creates another challenge for modern DevOps and SecOps professionals.
This means, you don’t need to change even a single line of code in the serverless functions themselves. Although the adoption of serverless functions brings many benefits, including scalability, quick deployments, and updates, it also introduces visibility and monitoring challenges to CloudOps and DevOps.
Part 1 of this series starts will cover the key ingredients needed for successful DevOps use to deliver better software faster, followed by a short overview of GitHub Actions and example use cases related to deployment and release monitoring. Example #1 – Deploy application code to Kubernetes. GitHub and GitHub Actions.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content