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Why applying chaos engineering to data-intensive applications matters

Dynatrace

The jobs executing such workloads are usually required to operate indefinitely on unbounded streams of continuous data and exhibit heterogeneous modes of failure as they run over long periods. This significantly increases event latency. Performance is usually a primary concern when using stream processing frameworks.

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Architectural Insights: Designing Efficient Multi-Layered Caching With Instagram Example

DZone

Caching is a critical technique for optimizing application performance by temporarily storing frequently accessed data, allowing for faster retrieval during subsequent requests. Multi-layered caching involves using multiple levels of cache to store and retrieve data.

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Optimize your environment: Unveiling Dynatrace Hyper-V extension for enhanced performance and efficient troubleshooting

Dynatrace

Hyper-V plays a vital role in ensuring the reliable operations of data centers that are based on Microsoft platforms. This leads to a more efficient and streamlined experience for users. Microsoft Hyper-V is a virtualization platform that manages virtual machines (VMs) on Windows-based systems.

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Edge Data Platforms, Real-Time Services, and Modern Data Trends

DZone

We all know that data is being generated at an unprecedented rate. You may also know that this has led to an increase in the demand for efficient and secure data storage solutions that won’t break the bank. What Are Edge Data Platforms? These platforms offer several advantages over traditional cloud computing.

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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. the retry success probability) and compute cost efficiency (i.e., Multi-objective optimizations.

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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

Reduced tail latencies In both our GRPC and DGS Framework services, GC pauses are a significant source of tail latencies. For a given CPU utilization target, ZGC improves both average and P99 latencies with equal or better CPU utilization when compared to G1. There is no best garbage collector.

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