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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.

Systems 226
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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data 154
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SQL Server On Linux: Forced Unit Access (Fua) Internals

SQL Server According to Bob

SQL Server relies on Forced-Unit-Access (Fua) I/O subsystem capabilities to provide data durability, detailed in the following documents: SQL Server 2000 I/O Basic and SQL Server I/O Basics, Chapter 2. Be sure to deploy SQL Server 2017 CU6 or newer for best data durability and performance results. “. The Back Story. 4 Socket, TPCC.

Servers 90
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Front-End Performance Checklist 2021

Smashing Magazine

Performance isn’t just a technical concern: it affects everything from accessibility to usability to search engine optimization, and when baking it into the workflow, design decisions have to be informed by their performance implications. It will help you build up a company-tailored case study with real data.

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Data Mining Problems in Retail

Highly Scalable

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods.

Retail 152
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HTTP/3: Performance Improvements (Part 2)

Smashing Magazine

HTTP/3 performance features ( current article ). A TCP connection, for example, doesn’t just start sending data at full bandwidth, because this could end up overloading (or congesting) the network. This is because, as we said, each network link has only a certain amount of data it can (physically) process every second.