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 185

Infrastructure Cloud Scalability AWS 185

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 112

Cloud Big Data AWS Analytics 112

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. SUS312 How innovators are driving more sustainable manufacturing  — Marcus Ulmefors Northvolt Director Data and ML Platforms and Muhammad Sajid AWS SA.

AWS 64

AWS Energy Architecture Programming 64

Abhishek Tiwari

JULY 20, 2019

I also have a strong feeling that long-lived teams are not good for innovation and disruption. Contrarian view What I am proposing here is some key principals to change how you deploy your engineers to do their best work in a fast-paced environment. Teach your engineers how to do teaming, reteaming, and onboard new team members.

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 faced numerous challenges in our previous infrastructure. Complex business logic was embedded directly into the ETL pipelines by data engineers. In order to replicate results, scientists had to delve deep into the data, code, and documentation.

Metrics 221

Metrics Architecture Infrastructure Innovation 221

All Things Distributed

NOVEMBER 15, 2016

They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. The cost and complexity to implement, scale, and use BI makes it difficult for most companies to make data analysis ubiquitous across their organizations.

Analytics 123

Analytics Availability Media Social Media 123