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DataEngineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “DataEngineers of Netflix” series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. Kevin, what drew you to dataengineering?
Software testing, especially in large scale projects, is a time intensive process. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available.
In this article, Tariq King describes the metaverse concept, discusses its key engineering challenges and quality concerns, and then walks through recent technological advances in AI and software testing that are helping to mitigate these challenges. By Tariq King
DataEngineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “DataEngineers of Netflix” interview series, where our very own dataengineers talk about their journeys to DataEngineering @ Netflix. What drew you to Netflix?
In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers itself. Have you ever wondered about systems based on machine learning? A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make.
Netflix’s engineeringculture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. What will be the cost of rolling out the winning cell of an AB test to all users?
Why should you think once again about testing Spark? DataEngineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. Shouldering blame: if something goes wrong, blame your DataEngineer!
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