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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. This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. What is RabbitMQ? What is Apache Kafka?

Latency 147
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Istio Ambient Mesh Performance Test and Benchmarking

DZone

Architecturally, the Istio Ambient mesh is a great design that improves performance. Since there are many promotions about Ambient mesh being production-ready, many of our prospects and enterprises are generally eager to try or migrate to Ambient mesh. But whether it performs quickly is still a question.

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Distance-Based ISA for Efficient Register Management

ACM Sigarch

To create a CPU core that can execute a large number of instructions in parallel, it is necessary to improve both the architecturewhich includes the overall CPU design and the instruction set architecture (ISA) designand the microarchitecture, which refers to the hardware design that optimizes instruction execution.

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SKP's Java/Java EE Gotchas: Clash of the Titans, C++ vs. Java!

DZone

This begins not only in designing the algorithm or coming out with efficient and robust architecture but right onto the choice of programming language. One, by researching on the Internet; Two, by developing small programs and benchmarking. The legacy languages — be it ASM or C still rule in terms of performance.

Java 214
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What is application modernization? How to pick an application modernization strategy

Dynatrace

According to IBM , application modernization takes existing legacy applications and modernizes their platform infrastructure, internal architecture, or features. Document and benchmark existing applications, processes, and services. Or, the team might use specific serverless designs as part of the modernization efforts.

Strategy 264
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10 tips for migrating from monolith to microservices

Dynatrace

Transforming an application from monolith to microservices-based architecture can be daunting, and knowing where to start can be difficult. Unsurprisingly, organizations are breaking away from monolithic architectures and moving toward event-driven microservices. Migration is time-consuming and involved.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. We designed experimental scenarios inspired by chaos engineering.