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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. This significantly increases event latency.

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

Dynatrace

These are the goals of AI observability and data observability, a key theme at Dynatrace Perform 2024 , the observability provider’s annual conference, which takes place in Las Vegas from January 29 to February 1, 2024. Join us at Dynatrace Perform 2024 , either on-site or virtuall y, to explore these themes further.

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Application observability meets developer observability: Unlock a 360º view of your environment

Dynatrace

In a recent webinar , Dynatrace DevOps activist Andi Grabner and senior software engineer Yarden Laifenfeld explored developer observability. In this example, Grabner saw that the adservice workload was running on EKS and could see the relevant metrics, logs, services, events, error logs, and more.

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix. Pallavi, what’s your journey to data engineering at Netflix?

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Automated observability, security, and reliability at scale

Dynatrace

While infrastructure has historically been treated as a bottleneck where proper scaling and compute power are applied to improve performance, these aspects are now typically addressed by hyperscalers that offer cloud-based infrastructure and infrastructure as a service.

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The state of site reliability engineering: SRE challenges and best practices in 2023

Dynatrace

This can often result from a lack of understanding of the discipline’s role in delivering on key performance objectives: SLOs. To overcome this hurdle, the panelists recommended that engineers communicate the value of SRE to higher-level executives through business data.

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Designing Instagram

High Scalability

from a client it performs two parallel operations: i) persisting the action in the data store ii) publish the action in a streaming data store for a pub-sub model. User Feed Service, Media Counter Service) read the actions from the streaming data store and performs their specific tasks. After that, the various services (e.g.

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