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As cloud complexity increases and security concerns mount, organizations need log analytics to discover and investigate issues and gain critical business intelligence. But exploring the breadth of log analytics scenarios with most log vendors often results in unexpectedly high monthly log bills and aggressive year-over-year costs.
With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries.
It also breaks down silos across the technology stack, allowing for rapid, scalable analysis and automation to prevent issues before they impact users. Dynatrace offers real-time threat detection, automated vulnerability analytics, Kubernetes Security Posture Management, runtime application protection, and seamless DevSecOps integration.
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.
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By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics. This integration augments our existing support for OpenTelemetry to provide customers with more flexibility.
Dynatrace automatically puts logs into context Dynatrace Log Management and Analytics directly addresses these challenges. Log analytics simplified: Deeper insights, no DQL required Your team will immediately notice the streamlined log analysis capabilities below the histogram. This context is vital to understanding issues.
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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. How can we optimize for performance and scalability?
The Dynatrace platform automatically captures and maps metrics, logs, traces, events, user experience data, and security signals into a single datastore, performing contextual analytics through a “power of three AI”—combining causal, predictive, and generative AI.
This gives us unified analytics views of node resources together with pod-level metrics such as container CPU throttling by node, which makes problem correlation much easier to analyze. The post Flexible, scalable, self-service Kubernetes native observability now in General Availability appeared first on Dynatrace blog.
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Data processing in the cloud has become increasingly popular due to its scalability, flexibility, and cost-effectiveness. This article will explore how these technologies can be used together to create an optimized data pipeline for data processing in the cloud.
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that offers security, scalability, and simplicity of use. Python code also carries limited scalability and the burden of governing its security in production environments and lifecycle management. address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0:
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Realizing that executives from other organizations are in a similar situation to my own, I want to outline three key objectives that Dynatrace’s powerful analytics can help you deliver, featuring nine use cases that you might not have thought possible. Change is my only constant. This is inefficient and creates avoidable risks.
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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. That’s especially true of the DevOps teams who must drive digital-fueled sustainable growth.
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