The Netflix TechBlog
JULY 15, 2021
Data Engineers of Netflix?—?Interview Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Kevin, what drew you to data engineering?
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?
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The Netflix TechBlog
DECEMBER 3, 2022
The most commonly used one is dataflow project , which helps folks in managing their data pipeline repositories through creation, testing, deployment and few other activities. It lets you create YAML formatted mock data files based on selected tables, columns and a few rows of data from the Netflix data warehouse.
The Netflix TechBlog
FEBRUARY 9, 2022
see “data pipeline” Intro The problem of managing scheduled workflows and their assets is as old as the use of cron daemon in early Unix operating systems. The design of a cron job is simple, you take some system command, you pick the schedule to run it on and you are done. workflow ?—?see Example: 0 0 * * MON /home/alice/backup.sh
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?
The Netflix TechBlog
MARCH 2, 2021
Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group. Learning through data is in Netflix’s DNA. We use A/B tests to introduce new product features, such as our daily Top 10 row that help our members discover their next favorite show.
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).
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