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?
The Netflix TechBlog
MARCH 5, 2019
While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. Give us a holler if you are interested in a thought exchange.
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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.
The Netflix TechBlog
NOVEMBER 20, 2023
These challenges are currently addressed in suboptimal and less cost efficient ways by individual local teams to fulfill the needs, such as Lookback: This is a generic and simple approach that data engineers use to solve the data accuracy problem. Users configure the workflow to read the data in a window (e.g.
The Netflix TechBlog
MAY 26, 2020
Spark could look up and retrieve the data in the s3 files that the Mouthful represented. This intermediate step of persisting Mouthfuls allowed us to easily “eat” through S3 event SQS messages at great speed, converting them to far fewer Mouthful SQS Messages which would each be consumed by a single Spark app instance.
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. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.
Abhishek Tiwari
DECEMBER 27, 2017
A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.
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