Remove Availability Remove Data Engineering Remove Design Remove Engineering
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.

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

Data pipeline asset management with Dataflow

The Netflix TechBlog

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

Storage 201
article thumbnail

Ready-to-go sample data pipelines with Dataflow

The Netflix TechBlog

The main workflow definition file holds the logic of a single run, in this case one day-worth of data. This logic consists of the following parts: DDL code, table metadata information, data transformation and a few audit steps. It’s designed to run for a single date, and meant to be called from the daily or backfill workflows.

article thumbnail

Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

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 more about other Data Science and Engineering functions at Netflix?

article thumbnail

A Day in the Life of an Experimentation and Causal Inference Scientist @ Netflix

The Netflix TechBlog

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207