The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202

Java Scalability Traffic Architecture 202

The Netflix TechBlog

MARCH 25, 2019

In addition, we derive lineage information from scheduled ETL jobs by extracting workflow definitions and runtime metadata using Meson scheduler APIs. Netflix’s diverse data landscape made it challenging to capture all the right data and conforming it to a common data model.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

The Netflix TechBlog

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. Orient: Gather tuning parameters for a particular table that changed. Also, respond to ad-hoc requests created manually by end-users.

Storage 203

Storage Latency Efficiency Data Engineering 203