The Netflix TechBlog

OCTOBER 28, 2021

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.

Data Engineering 178

Data Engineering Engineering Big Data Software Engineering 178

The Netflix TechBlog

MARCH 5, 2019

Central engineering teams enable this operational model by reducing the cognitive burden on innovation teams through solutions related to securing, scaling and strengthening (resilience) the infrastructure. All these micro-services are currently operated in AWS cloud infrastructure.

Infrastructure 184

Infrastructure Cloud Scalability AWS 184

Dynatrace

JULY 6, 2022

With ever-evolving infrastructure, services, and business objectives, IT teams can’t keep up with routine tasks that require human intervention. This requires significant data engineering efforts, as well as work to build machine-learning models. Big data automation tools. Creating a sound IT automation strategy.

Artificial Intelligence 201

Artificial Intelligence Tuning Strategy Big Data 201

The Netflix TechBlog

MARCH 2, 2021

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 213

Analytics C++ Innovation Engineering 213

The Netflix TechBlog

MAY 26, 2020

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the Cloud Network Infrastructure to address the identified problems. As with any sustainable engineering design, focusing on simplicity is very important.

Network 152

Network Tuning AWS Traffic 152

The Netflix TechBlog

DECEMBER 3, 2022

A large number of our data users employ SparkSQL, pyspark, and Scala. A small but growing contingency of data scientists and analytics engineers use R, backed by the Sparklyr interface or other data processing tools, like Metaflow. Centralized Best Practices Data infrastructure evolves continually.

Best Practices 360

Best Practices Code Testing Data Engineering 360

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 127

Cloud Big Data AWS Analytics 127