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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.
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.
Metadata enrichment improves collaboration and increases analytic value. The Dynatrace® platform continues to increase the value of your data — broadening and simplifying real-time access, enriching context, and delivering insightful, AI-augmented analytics. Our Business Analytics solution is a prominent beneficiary of this commitment.
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This results in site reliability engineers nudging development teams to add resource attributes, endpoints, and tokens to their source code. Second, embracing the complexity of OpenTelemetry signal collection must come with a guaranteed payoff: gaining analytical insights and causal relationships that improve business performance.
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Dynatrace automatically puts logs into context Dynatrace Log Management and Analytics directly addresses these challenges. You can easily pivot between a hot Kubernetes cluster and the log file related to the issue in 2-3 clicks in these Dynatrace® Apps: Infrastructure & Observability (I&O), Databases, Clouds, and Kubernetes.
This latest integration with Microsoft Sentinel expands our partnership, providing joint customers with a holistic view of their entire cloud environment; from application to infrastructure, data, and security. “As The Davis AI engine automatically and continuously delivers actionable insights based on an environment’s current state.
On average, organizations use 10 different tools to monitor applications, infrastructure, and user experiences across these environments. Such fragmented approaches fall short of giving teams the insights they need to run IT and site reliability engineering operations effectively.
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. Automation, automation, automation.
In fact, 76% of technology leaders say the dynamic nature of Kubernetes makes it more difficult to maintain visibility of their infrastructure compared with traditional technology stacks. The company receives tens of thousands of requests per second on its edge layer and sees hundreds of millions of events per hour on its analytics layer.
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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.
a Netflix member via Twitter This is an example of a question our on-call engineers need to answer to help resolve a member issue?—?which Now let’s look at how we designed the tracing infrastructure that powers Edgar. We needed to increase engineering productivity via distributed request tracing.
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Sure, cloud infrastructure requires comprehensive performance visibility, as Dynatrace provides , but the services that leverage cloud infrastructures also require close attention. Extend infrastructure observability to WSO2 API Manager. Cloud-based application architectures commonly leverage microservices. What’s next?
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HANA maintains all the business and analytics data that your business runs on. However, if you’re an operations engineer who’s been tasked with migrating to HANA from a legacy database system, you’ll need to get up to speed quickly. Enable the Davis AI causation engine to automatically analyze every metric.
With extended contextual analytics and AIOps for open observability, Dynatrace now provides you with deep insights into every entity in your IT landscape, enabling you to seamlessly integrate metrics, logs, and traces—the three pillars of observability. Dynatrace extends its unique topology-based analytics and AIOps approach.
We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. – DevOps Engineer, large healthcare company. Start your free trial today for best-in-class APM, infrastructure monitoring, and AIOps, all in a single solution. This is great! New to Dynatrace?
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In this blog, I will be going through a step-by-step guide on how to automate SRE-driven performance engineering. This opens up new analytics use case to e.g: Dynatrace news. Keptn uses SLO definitions to automatically configure Dynatrace or Prometheus alerting rules. detect those test cases that cause high database or CPU overhead.
Stream processing enables software engineers to model their applications’ business logic as high-level representations in a directed acyclic graph without explicitly defining a physical execution plan. Failures can occur unpredictably across various levels, from physical infrastructure to software layers.
With Dashboards , you can monitor business performance, user interactions, security vulnerabilities, IT infrastructure health, and so much more, all in real time. Even if infrastructure metrics aren’t your thing, you’re welcome to join us on this creative journey simply swap out the suggested metrics for ones that interest you.
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