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How Netflix Content Engineering makes a federated graph searchable (Part 2)

By Alex Hutter, Falguni Jhaveri, and Senthil SayeebabaIn a previous post, we described the indexing architecture of Studio Search and how we scaled the architecture by building a config-driven self-service platform that allowed teams in Content Engineering to spin up search indices easily.This post will discuss how Studio Search supports querying the...

Scaling Appsec at Netflix (Part 2)

By Astha Singhal, Lakshmi Sudheer, Julia KnechtThe Application Security teams at Netflix are responsible for securing the software footprint that we create to run the Netflix product, the Netflix studio, and the business. Our customers are product and engineering teams at Netflix that build these software services and platforms. The Netflix cultural...

A Survey of Causal Inference Applications at Netflix

At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy.To measure causal effects we rely heavily on AB testing, but we also leverage quasi-experimentation in...

Evolution of ML Fact Store

by Vivek KaushalAt Netflix, we aim to provide recommendations that match our members’ interests. To achieve this, we rely on Machine Learning (ML) algorithms. ML algorithms can be only as good as the data that we provide to it. This post will focus on the large volume of high-quality data stored in Axion — our fact store that is leveraged to compute...

How Netflix Content Engineering makes a federated graph searchable

By Alex Hutter, Falguni Jhaveri and Senthil SayeebabaOver the past few years Content Engineering at Netflix has been transitioning many of its services to use a federated GraphQL platform. GraphQL federation enables domain teams to independently build and operate their own Domain Graph Services (DGS) and, at the same time, connect their domain with...

Rapid Event Notification System at Netflix

By: Ankush Gulati, David GevorkyanAdditional credits: Michael Clark, Gokhan OzerIntroNetflix has more than 220 million active members who perform a variety of actions throughout each session, ranging from renaming a profile to watching a title. Reacting to these actions in near real-time to keep the experience consistent across devices is critical for...

Data pipeline asset management with Dataflow

by Sam Setegne, Jai Balani, Olek GorajekGlossaryasset — any business logic code in a raw (e.g. SQL) or compiled (e.g. JAR) form to be executed as part of the user defined data pipeline.data pipeline — a set of tasks (or jobs) to be executed in a predefined order (a.k.a. DAG) for the purpose of transforming data using some business logic.Dataflow — Netflix...

Demystifying Interviewing for Backend Engineers @ Netflix

By Karen Casella, Director of Engineering, Access & Identity ManagementHave you ever experienced one of the following scenarios while looking for your next role?You study and practice coding interview problems for hours/days/weeks/months, only to be asked to merge two sorted lists.You apply for multiple roles at the same company and proceed through...

Netflix: A Culture of Learning

Martin Tingley with Wenjing Zheng, Simon Ejdemyr, Stephanie Lane, Colin McFarland, Mihir Tendulkar, and Travis BrooksThis is the last post in an overview series on experimentation at Netflix. Need to catch up? Earlier posts covered the basics of A/B tests (Part 1 and Part 2 ), core statistical concepts (Part 3 and Part 4), how to build confidence in...

Fixing Performance Regressions Before they Happen

Angus CrollNetflix is used by 222 million members and runs on over 1700 device types ranging from state-of-the-art smart TVs to low-cost mobile devices.At Netflix we’re proud of our reliability and we want to keep it that way. To that end, it’s important that we prevent significant performance regressions from reaching the production app. Sluggish scrolling...

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