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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.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

As a result, requests are uniformly handled, and responses are processed cohesively. This standardization enhances adoption within the personalization stack, simplifies the system, and improves understanding and debuggability for engineers. The request schema for the observability endpoint.

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Software engineering for machine learning: a case study

The Morning Paper

Software engineering for machine learning: a case study Amershi et al., Previously on The Morning Paper we’ve looked at the spread of machine learning through Facebook and Google and some of the lessons learned together with processes and tools to address the challenges arising. A general process. ICSE’19.

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Auto-adaptive thresholds for AI-driven quality gating

Dynatrace

This process, known as auto-adaptive thresholding, eliminates the need to define a static threshold upfront. Once the learning phase is complete, all subsequent validation results are fed into Davis AI to fine-tune the thresholds based on changed behavior. Instead, it derives the suitable thresholds from previous validation results.

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How To Use BenchmarkDotNet: 6 Simple Performance-Boosting Tips To Get Started

DZone

As software engineers, we are always striving for high performance and efficiency in our code. Whether it’s optimizing algorithms or fine-tuning data structures, every decision we make can have a significant impact on the overall performance of our applications.

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Automated observability, security, and reliability at scale

Dynatrace

As software development grows more complex, managing components using an automated onboarding process becomes increasingly important. Efficient environment configuration at scale One of software engineers’ most significant challenges is managing the numerous tools and technologies required for the software product lifecycle.