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Google Cloud Next 2024: AI innovation for Google Cloud

Dynatrace

Dynatrace offers essential analytics and automation to keep applications optimized and businesses flourishing. By seamlessly integrating observability, AI-driven insights, and data analytics, organizations can overcome common obstacles such as operational inefficiencies, performance bottlenecks, and scalability concerns.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience.

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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0: Reporting and analytics assets out-of-the-box Bundles offered by Extensions 2.0 Extensions 2.0

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. Data dependencies and framework intricacies require observing the lifecycle of an AI-powered application end to end, from infrastructure and model performance to semantic caches and workflow orchestration.

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AWS observability: AWS monitoring best practices for resiliency

Dynatrace

Because of its matrix of cloud services across multiple environments, AWS and other multicloud environments can be more difficult to manage and monitor compared with traditional on-premises infrastructure. EC2 is Amazon’s Infrastructure-as-a-service (IaaS) compute platform designed to handle any workload at scale.

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Dynatrace Perform 2024 Guide: Deriving business value from AI data analysis

Dynatrace

Platform engineering improves developer productivity by providing self-service capabilities with automated infrastructure operations. Why growing AI adoption requires an AI observability strategy – blog While AI adoption brings operational efficiency and innovation for organizations, it also introduces the potential for runaway AI costs.

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics. It is critical for fast-paced product innovation at Netflix since CL provides foundational data for personalization, A/B experimentation, and performance analytics.