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Leverage AI for proactive protection: AI and contextual analytics are game changers, automating the detection, prevention, and response to threats in real time. UMELT are kept cost-effectively in a massive parallel processing data lakehouse, enabling contextual analytics at petabyte scale, fast.
Dynatrace enables various teams, such as developers, threat hunters, business analysts, and DevOps, to effortlessly consume advanced log insights within a single platform. This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries.
Scale with confidence: Leverage AI for instant insights and preventive operations Using Dynatrace, Operations, SRE, and DevOps teams can scale efficiently while maintaining software quality and ensuring security and reliability. AI-driven analytics transform data analysis, making it faster and easier to uncover insights and act.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Log monitoring is a process by which developers and administrators continuously observe logs as they’re being recorded. What is log analytics?
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.
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.
When it comes to mobile monitoring, everyone has their own point of view… Mobile is not a single technology: it involves different development teams handling Android and iOS apps, performance engineering teams, cloud operations, and marketing. How do I connect the dots between mobile analytics and performance monitoring?
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.
Today, development teams suffer from a lack of automation for time-consuming tasks, the absence of standardization due to an overabundance of tool options, and insufficiently mature DevSecOps processes. This leads to frustrating bottlenecks for developers attempting to build and deliver software.
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.
As more organizations embrace DevOps and CI/CD pipelines, GitHub-hosted runners and GitHub Actions have emerged as powerful tools for automating workflows. Once the data is formatted, it is ingested into Dynatrace Business Analytics using the Dynatrace SDK.
Exploding volumes of business data promise great potential; real-time business insights and exploratory analytics can support agile investment decisions and automation driven by a shared view of measurable business goals. Traditional observability solutions don’t capture or analyze application payloads. What’s next?
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.
As enterprises embrace more distributed, multicloud and applications-led environments, DevOps teams face growing operational, technological, and regulatory complexity, along with rising cyberthreats and increasingly demanding stakeholders.
As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. The goal is to abstract away the underlying infrastructure’s complexities while providing a streamlined and standardized environment for development teams.
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.
Service-level objectives (SLOs) are a great tool to align business goals with the technical goals that drive DevOps (Speed of Delivery) and Site Reliability Engineering (SRE) (Ensuring Production Resiliency). Introduction Objective Driven Development (ODD) for some Business SLOs. Dynatrace news. Mobile Crashes.
However, the 2024 State of Observability report from Dynatrace reveals that the explosion of data generated by these complex ecosystems is pushing traditional monitoring and analytics approaches to their limits. They enable developers, engineers, and architects to drive innovation, but they also introduce new challenges."
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.
DevOps teams can also benefit from full-stack observability. With improved diagnostic and analytic capabilities, DevOps teams can spend less time troubleshooting. Instead, they can apply their talent to developing innovative new features that benefit users and move the business forward. See observability in action!
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.
At the 2024 Dynatrace Perform conference in Las Vegas, Michael Winkler, senior principal product management at Dynatrace, ran a technical session exploring just some of the many ways in which Dynatrace helps to automate the processes around development, releases, and operation. Real-time detection for fast remediation.
In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.
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.
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. Adding to the complexity are containers–tools for cloud development—which can be ephemeral. Learn more here. What is AWS Lambda? What is AWS Lambda?
The development of internal platform teams has taken off in the last three years, primarily in response to the challenges inherent in scaling modern, containerized IT infrastructures. 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.
And a staggering 83% of respondents to a recent DevOps Digest survey have plans to adopt platform engineering or have already done so. As developers continue to use generative AI-powered autonomous agents to write code, organizations will be exposed to greater risks of unexpected problems that affect customer and user experiences.
With the launch of ChatGPT, an AI chatbot developed by OpenAI, in November 2022, large language models (LLMs) and generative AI have become a global sensation, making their way to the top of boardroom agendas and household discussions worldwide. GPTs can also help quickly onboard team members to new development platforms and toolsets.
Dynatrace observability, security, and data analytics capabilities empower users to derive greater insights and benefits from their monitoring data, ensuring they stay ahead in their mobile monitoring environments while offering similar feature parity to Visual Studio.
The focus on bringing various organizational teams together—such as development, business, and security teams — makes sense as observability data, security data, and business event data coalesce in these cloud-native environments. As organizations develop new applications, vulnerabilities will continue to emerge.
This complexity creates silos that affect the ability of IT, development, security, and business teams to achieve the awareness they need to make data-driven decisions. A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. Development and DevOps.
However, the growing awareness of the potential for bias in artificial intelligence will be a barrier to widespread automation in business operations, IT, development, and security. This will negate efficiency gains and hinder efforts to automate business, development, security, and operations processes. Observability trend no.
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.
Organizations across industries are embracing generative AI, a technology that promises faster development and increased productivity. Our guide covers AI for effective DevSecOps, converging observability and security, and cybersecurity analytics for threat detection and response. Learn more in this blog.
Therefore, organizations are increasingly turning to artificial intelligence and machine learning technologies to get analytical insights from their growing volumes of data. AI applies advanced analytics and logic-based techniques to interpret data and events, support and automate decisions, and even take intelligent actions.
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. The need for developers and innovation is now even greater. Thus, the concept of NoOps takes DevOps a step further.
Container technology is very powerful as small teams can develop and package their application on laptops and then deploy it anywhere into staging or production environments without having to worry about dependencies, configurations, OS, hardware, and so on. The time and effort saved with testing and deployment are a game-changer for DevOps.
This significantly accelerates release cycles, reduces time to market, prevents unexpected disruptions, and lowers costs by catching issues early in the software development lifecycle (SDLC). The ability to scale testing as part of the software development lifecycle (SDLC) has proven difficult.
To tame this complexity and deliver differentiated digital experiences, IT, development, security, and business teams need automated workflows throughout these cloud ecosystems. Cloud environments have become ever more complex, with an increasingly interconnected set of services.
For example, look for vendors that use a secure development lifecycle process to develop software and have achieved certain security standards. This can require process re-engineering to fill gaps and ensuring clear communication and collaboration across security, operations, and development teams. Resource constraints.
Logs assist operations, security, and development teams in ensuring the reliability and performance of application environments. These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues.
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.
Developers use generative AI to find errors in code and automatically document their code. How will organizations and cybersecurity teams keep up with the “relentless pace of AI development,” as the keynote asks? Learn how security improves DevOps. DevOps vs DevSecOps: Why integrate security and DevOps?
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