Remove Engineering Remove Latency Remove Scalability
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How to Scale Elasticsearch to Solve Your Scalability Issues

DZone

With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries.

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Scalable Annotation Service?—?Marken

The Netflix TechBlog

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. The service should be able to serve real-time, aka UI, applications so CRUD and search operations should be achieved with low latency.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing enables software engineers to model their applications’ business logic as high-level representations in a directed acyclic graph without explicitly defining a physical execution plan. We designed experimental scenarios inspired by chaos engineering. This significantly increases event latency.

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Site reliability engineering: 5 things you need to know

Dynatrace

What is site reliability engineering? Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. Dynatrace news. SRE focuses on automation.

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Site reliability engineering: 5 things to you need to know

Dynatrace

Site reliability engineering (SRE) is the practice of applying software engineering principles to operations and infrastructure processes to help organizations create highly reliable and scalable software systems. Organizations can then integrate these skilled engineers at key points in the DevOps life cycle.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? The complexity of these operational demands underscored the urgent need for a scalable solution.

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs. Key insights from this shiftinclude: A Data-Centric Approach : Shifting focus from model-centric strategies, which heavily rely on feature engineering, to a data-centric one.

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