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Dynatrace simplifies OpenTelemetry metric collection for context-aware AI analytics

Dynatrace

Second, embracing the complexity of OpenTelemetry signal collection must come with a guaranteed payoff: gaining analytical insights and causal relationships that improve business performance. The missed SLO can be analytically explored and improved using Davis insights on an out-of-the-box Kubernetes workload overview.

Analytics 321
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

RabbitMQ is designed for flexible routing and message reliability, while Kafka handles high-throughput event streaming and real-time data processing. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is Apache Kafka?

Latency 147
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Unlock end-to-end observability insights with Dynatrace PurePath 4 seamless integration of OpenTracing for Java

Dynatrace

Dynatrace is fully committed to the OpenTelemetry community and to the seamless integration of OpenTelemetry data , including ingestion of custom metrics , into the Dynatrace open analytics platform. Find OpenTracing for Java seamlessly integrated into PurePath 4. Deep-code execution details. Always-on profiling in transaction context.

Java 264
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New analytics capabilities for messaging system-related anomalies

Dynatrace

An example of a critical event-based messaging service for many businesses is adding a product to a shopping cart. We’ve introduced brand-new analytics capabilities by building on top of existing features for messaging systems. Finally, you can configure and activate them there. New to Dynatrace?

Analytics 246
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How a data lakehouse brings data insights to life

Dynatrace

These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. Further, these resources support countless Kubernetes clusters and Java-based architectures. where an error occurred at the code level.

Analytics 264
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Dynatrace and Red Hat expand enterprise observability to edge computing

Dynatrace

As an example, many retailers already leverage containerized workloads in-store to enhance customer experiences using video analytics or streamline inventory management using RFID tracking for improved security. In this case, Davis finds that a Java Spring Micrometer metric called Failed deliveries is highly correlated with CPU spikes.

Retail 306
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Dynatrace extends AI-powered observability for SAP together with PowerConnect

Dynatrace

Monitoring SAP products can present challenges Monitoring SAP systems can be challenging due to the inherent complexity of using different technologies—such as ABAP, Java, and cloud offerings—and the sheer amount of generated data. Visibility into SAP CPI messages, down to every single attribute.

Java 246