Remove Architecture 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 Samuel Setegne

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Samuel Setegne Samuel Setegne This post is part of our “Data Engineers of Netflix” interview series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. What drew you to 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

Your technology architecture and engineering organization should coevolve as your startup grows

Abhishek Tiwari

The evolution of your technology architecture should depend on the size, culture, and skill set of your engineering organization. There are no hard-and-fast rules to figure out interdependency between technology architecture and engineering organization but below is what I think can really work well for product startup.

article thumbnail

Less is More: Engineering Data Warehouse Efficiency with Minimalist Design

Uber Engineering

Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog. In our experience, optimizing for operational efficiency requires answering one key question: for which tables does the maintenance cost supersede utility?

article thumbnail

Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. Users configure the workflow to read the data in a window (e.g. The window is set based on users’ domain knowledge so that users have a high confidence that the late arriving data will be included or will not matter (i.e.

article thumbnail

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes data consumed by few critical customer facing services (e.g. You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted.

article thumbnail

Reimagining Experimentation Analysis at Netflix

The Netflix TechBlog

Our data scientists often want to apply their knowledge of the business and statistics to fully understand the outcome of an experiment. Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute.

Metrics 215