The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

Tuning 210

Tuning Efficiency Big Data Engineering 210

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63

Latency Analytics Scalability Engineering 63

The Netflix TechBlog

DECEMBER 21, 2020

Use cases We found several use cases where a system like AutoOptimize can bring tons of value. Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster.

Storage 203

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

JULY 21, 2022

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. Some nuances while creating this dataset come from the on-field domain knowledge of our engineers. Labeling the data?

Big Data 179

Big Data Cache Engineering Data Engineering 179