The Netflix TechBlog

DECEMBER 14, 2023

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! In this video, Sr.

Data Engineering 259

Data Engineering Engineering Software Engineering Best Practices 259

DZone

FEBRUARY 20, 2023

In software engineering, we've learned that building robust and stable applications has a direct correlation with overall organization performance. The data community is striving to incorporate the core concepts of engineering rigor found in software communities but still has further to go. Posted with permission.

Software Engineering 264

Software Engineering Engineering Software Software 264

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

JANUARY 11, 2022

Here we describe the role of Experimentation and A/B testing within the larger Data Science and Engineering organization at Netflix, including how our platform investments support running tests at scale while enabling innovation. Curious to learn about what it’s like to be a Data Engineer at Netflix?

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Innovation Metrics Engineering Testing 243

The Netflix TechBlog

NOVEMBER 20, 2023

IPS enables users to continue to use the data processing patterns with minimal changes. Introduction Netflix relies on data to power its business in all phases. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful). past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

O'Reilly

FEBRUARY 2, 2020

On one hand, ops groups are in a good position to do this; they’re already heavily invested in testing, monitoring, version control, reproducibility, and automation. This has important implications for testing. In the last two decades, a tremendous amount of work has been done on testing and building test suites.

Software Architecture 67

Software Architecture Monitoring Software Engineering Architecture 67

Abhishek Tiwari

JULY 20, 2019

Over specialisation is considered good in industries such as healthcare and aviation but in software engineering over specialisation can be a blocker. Unlike healthcare and aviation where practices don't change over the decades, software technology is changing every day. product) don't change over a long period. Probably yes.

Engineering 61

Engineering Retail Airlines Healthcare 61