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DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. Clearly, continuing to depend on siloed systems, disjointed monitoring tools, and manual analytics is no longer sustainable.
Deploying and safeguarding software services has become increasingly complex despite numerous innovations, such as containers, Kubernetes, and platform engineering. Recent global IT outages, such as the CrowdStrike incident, remind us how dependent society is on software that works perfectly.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. With the help of log monitoring software, teams can collect information and trigger alerts if something happens that affects system performance and health.
In today’s digital world, software is everywhere. Software is behind most of our human and business interactions. This, in turn, accelerates the need for businesses to implement the practice of software automation to improve and streamline processes. What is software automation? What is softwareanalytics?
That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth. They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. From a technical perspective, however, cloud-based analytics can be challenging.
As more organizations embrace DevOps and CI/CD pipelines, GitHub-hosted runners and GitHub Actions have emerged as powerful tools for automating workflows. Everyone involved in the software delivery lifecycle can work together more effectively with a single source of truth and a shared understanding of pipeline performance and health.
We introduced Digital Business Analytics in part one as a way for our customers to tie business metrics to application performance and user experience, delivering unified insights into how these metrics influence business milestones and KPIs. A sample Digital Business Analytics dashboard. Dynatrace news.
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.
This leads to frustrating bottlenecks for developers attempting to build and deliver software. A central element of platform engineering teams is a robust Internal Developer Platform (IDP), which encompasses a set of tools, services, and infrastructure that enables developers to build, test, and deploy software applications.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
What is log analytics? Log analytics is the process of viewing, interpreting, and querying log data so developers and IT teams can quickly detect and resolve application and system issues. In what follows, we explore log analytics benefits and challenges, as well as a modern observability approach to log analytics.
On the other side of the organization, application owners have hired teams of analysts to dig through web analytics tools to gain insights into the customer experience. Welcome to Dynatrace Digital Business Analytics. What does this mean and how can you unlock Digital Business Analytics? Digital Business Analytics in action.
This leaves DevOps teams with the cumbersome task of having to manually identify user struggles and troubleshoot errors. How do I connect the dots between mobile analytics and performance monitoring? Connect the dots between mobile analytics and performance monitoring with mobile business analytics.
ChatGPT and generative AI: A new world of innovation Software development and delivery are key areas where GPT technology such as ChatGPT shows potential. For example, it can help DevOps and platform engineering teams write code snippets by drawing on information from software libraries.
When organizations implement SLOs, they can improve software development processes and application performance. SLOs improve software quality. Stable, well-calibrated SLOs pave the way for teams to automate additional processes and testing throughout the software delivery lifecycle. SLOs aid decision making.
Why organizations are turning to software development to deliver business value. Digital immunity has emerged as a strategic priority for organizations striving to create secure software development that delivers business value. Software development success no longer means just meeting project deadlines.
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.
Organizations can now accelerate innovation and reduce the risk of failed software releases by incorporating on-demand synthetic monitoring as a metrics provider for automatic, continuous release-validation processes. The ability to scale testing as part of the software development lifecycle (SDLC) has proven difficult. Dynatrace news.
One of the primary drivers behind digital transformation initiatives is the desire to streamline application development and delivery to bring higher quality, more secure software to market faster. Dynatrace enables software intelligence as code. Otherwise, contact our Services team.
In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. From data lakehouse to an analytics platform Traditionally, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.
As recent events have demonstrated, major software outages are an ever-present threat in our increasingly digital world. From business operations to personal communication, the reliance on software and cloud infrastructure is only increasing. Software bugs Software bugs and bad code releases are common culprits behind tech outages.
Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says. Logs on Grail Log data is foundational for any IT analytics. Grail and DQL will give you new superpowers.”
DevOps teams can also benefit from full-stack observability. With improved diagnostic and analytic capabilities, DevOps teams can spend less time troubleshooting. How full-stack observability enhances IT and DevOps. Here are a few ways full-stack observability can benefit your IT and DevOps teams.
Real-time streaming needs real-time analytics As enterprises move their workloads to cloud service providers like Amazon Web Services, the complexity of observing their workloads increases. SREs and DevOps engineers need cloud logs in an integrated observability platform to monitor the whole software development lifecycle.
Event logging and software tracing help application developers and operations teams understand what’s happening throughout their application flow and system. While logging is the act of recording logs, organizations extract actionable insights from these logs with log monitoring, log analytics, and log management.
And a staggering 83% of respondents to a recent DevOps Digest survey have plans to adopt platform engineering or have already done so. In 2024, more organizations will experience major digital service outages due to poor-quality and insufficiently supervised software code. Data indicates these technology trends have taken hold.
For example, nearly two-thirds (61%) of technology leaders say they will increase investment in AI over the next 12 months to speed software development. As they continue on this path, organizations expect other benefits , from enabling business users to easily customize dashboards (54%) to building interactive queries for analytics (48%).
Serverless architecture enables organizations to deliver applications more efficiently without the overhead of on-premises infrastructure, which has revolutionized software development. Its approach to serverless computing has transformed DevOps. Dynatrace extends contextual analytics and AIOps for open observability.
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. Platform engineering is on the rise. Ensure that you get the most out of your product.
Application vulnerabilities remain a key concern Application vulnerabilities—weaknesses or flaws in software applications that malicious attackers can use to exploit IT systems—exist in any type of software, including web and mobile applications.
The end goal, of course, is to optimize the availability of organizations’ software. While I am excited that the people who create software are also responsible for it – in contrast to “throw over the wall” approaches – it poses consistency and compliance challenges in larger organizations. Note that the work doesn’t get reduced.
These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. How does a data lakehouse—the combination of a data warehouse and a data lake—together with software intelligence, bring data insights to life?
We are proud to s hare Dynatrace has been named the winner in the “ Best Overall AI-based Analytics Company ” category, recognized for our innovation and the business-driving impact of our AI engine, Davis. . million a year in employee productivity alone. . The difference Davis makes.
A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Development and DevOps. Indeed, IT operations, security, and DevOps teams feel the strain of ever-growing cloud environments and the increasing amounts and types of data they generate.
Our guide covers AI for effective DevSecOps, converging observability and security, and cybersecurity analytics for threat detection and response. A unified observability and security analytics strategy can guide organizations toward a more proactive security posture at scale. Learn more in this blog. Read now and learn more!
They can also use generative AI for cybersecurity, write prototype code, and implement complex software systems. Second, for causal AI to provide a deep and rich context to unleash GPT’s full potential for software delivery and productivity use cases.”
As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. The pair showed how to track factors including developer velocity, platform adoption, DevOps research and assessment metrics, security, and operational costs.
NoOps, or “no operations,” emerged as a concept alongside DevOps and the push to automate the CI/CD pipelines as early as 2010. For most teams, evolving their DevOps practices has been challenging enough. DevOps requires infrastructure experts and software experts to work hand in hand. Or is it just a passing cloud?
As businesses take steps to innovate faster, software development quality—and application security—have moved front and center. Indeed, according to one survey, DevOps practices have led to 60% of developers releasing code twice as quickly. Dynatrace news. This is fueling key DevSecOps trends in 2022.
2: Observability, security, and business analytics will converge as organizations strive to tame the data explosion. To address this, observability, security, and business analytics will converge as organizations consolidate their tools. Observability trend no. Observability trend no.
Development teams need automated workflows so they’re not stuck manually monitoring all stages of the software development lifecycle in their cloud environments. And, ultimately, platform and site reliability engineers can provide answer-driven automation and higher-quality software with automated security and quality gates.
Autonomous Cloud is what enables our globally distributed development teams at Dynatrace to deliver better software faster following our NoOps approach: Fully Autonomous and as a Self-Service! Three waves of DevOps leading to Autonomous Cloud. DevOps Transformation at Dynatrace enacted live on stage at Perform 2017!
As strained IT, development, and security teams head into 2022, the pressure to deliver better, more secure software faster has never been more consequential. A key arrow in the quiver for game-changers for developing and managing modern software is automatic, intelligent observability. DevOps and DevSecOps orchestration.
As the pace of business quickens, software development has adapted. Increasingly, teams release software features more quickly to accommodate customer needs. As a result, organizations are weighing microservices vs. monolithic architecture to improve software delivery speed and quality. Hard on DevOps. Dynatrace news.
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