article thumbnail

Our First Netflix Data Engineering Summit

The Netflix TechBlog

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! In this video, Sr.

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? Let’s dive in!

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

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.

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. ETL workflows), as well as downstream (e.g.

Systems 226
article thumbnail

Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

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. the retry success probability) and compute cost efficiency (i.e., Multi-objective optimizations.

Tuning 210
article thumbnail

How TripleLift Built an Adtech Data Pipeline Processing Billions of Events Per Day

High Scalability

This is a guest post by Eunice Do , Data Engineer at TripleLift , a technology company leading the next generation of programmatic advertising. What is the name of your system and where can we find out more about it? The system is the data pipeline at TripleLift. Why did you decide to build this system?

article thumbnail

3. Psyberg: Automated end to end catch up

The Netflix TechBlog

Data Load Type : The ETL can either load the missed/new data specifically or reload the entire specified range. By focusing solely on updates and avoiding reprocessing of data based on a fixed lookback window, both Stateless and Stateful Data Processing maintain a minimal change footprint.

Tuning 244