The Netflix TechBlog

JUNE 1, 2021

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. What drew you to Netflix?

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

The Netflix TechBlog

MARCH 5, 2019

Netflix’s engineering culture 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. All these micro-services are currently operated in AWS cloud infrastructure.

Infrastructure 182

Infrastructure Cloud Scalability AWS 182

Adrian Cockcroft

FEBRUARY 13, 2023

STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions. SUS302 Optimizing architectures for sustainability — Katja Philipp AWS SA and Szymon Kochanski AWS SA. SUS209 — there was no talk with this code.

AWS 64

AWS Energy Architecture Programming 64

Abhishek Tiwari

JULY 20, 2019

The engineering organisation described may not work for you because of a team of 8-10 people is still a very big overhead. In this model, software architecture and code ownership is a reflection of the organisational model. I also have a strong feeling that long-lived teams are not good for innovation and disruption.

Engineering 61

Engineering Retail Airlines Healthcare 61

The Netflix TechBlog

SEPTEMBER 10, 2019

This enables us to optimize their experience at speed. Our data scientists often want to apply their knowledge of the business and statistics to fully understand the outcome of an experiment. The two main challenges with this approach are establishing an easy contribution framework and handling Netflix’s scale of data.

Metrics 215

Metrics Architecture Infrastructure Innovation 215

All Things Distributed

NOVEMBER 15, 2016

The reality is that many traditional BI solutions are built on top of legacy desktop and on-premises architectures that are decades old. They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Powered by Innovation.

Analytics 152

Analytics Availability Media Social Media 152