article thumbnail

1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. What is late-arriving data?

article thumbnail

Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis 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. Pallavi Phadnis is a Senior Software Engineer at Netflix.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems.

Systems 226
article thumbnail

Article: Design Pattern Proposal for Autoscaling Stateful Systems

InfoQ

In this article, Rogerio Robetti discusses the challenges in auto-scaling stateful storage systems and proposes an opinionated design solution to automatically scale up (vertical) and scale out (horizontal) from a single node up to several nodes in a cluster with minimum configuration and interference of the operator. By Rogerio Robetti

Design 144
article thumbnail

3. Psyberg: Automated end to end catch up

The Netflix TechBlog

Audit Run various quality checks on the staged data. Several audits, such as verifying source and target counts, are performed on this batch of data. Publish If the audits are successful, cherry-pick the staging snapshot to publish the data to production. Metadata Recording : Metadata is persisted for traceability.

Tuning 244
article thumbnail

Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

Specifically, if we are able to predict or analyze the Out of Memory kills, we can take device specific actions to pre-emptively lower the performance in favor of not crashing?—?aiming aiming to give the user the ultimate Netflix Experience within the “performance vs pre-emptive action” tradeoff limitations. Labeling the data?

Big Data 179
article thumbnail

Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

Full ownership often means building new data pipelines, navigating complex schemas and large data sets, developing or improving metrics for business performance, and creating intuitive visualizations and dashboards?—?always and technical excellence ??. always with an eye towards actionable insights.

Analytics 240