Remove Analytics Remove Data Engineering Remove Infrastructure Remove Innovation
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

How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

This freedom allows teams and individuals to move fast to deliver on innovation and feel responsible for quality and robustness of their delivery. All these micro-services are currently operated in AWS cloud infrastructure. In the next section, we will highlight some high level areas of focus in each dimension of our infrastructure.

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

Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

As I mentioned, we live in a world where massive volumes of data are being generated, every day, from connected devices, websites, mobile apps, and customer applications running on top of AWS infrastructure. Put simply, data is not always readily available and accessible to organizational end users. Powered by Innovation.

Analytics 152
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

What is IT automation?

Dynatrace

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.

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
article thumbnail

Expanding the Cloud: Introducing Amazon QuickSight

All Things Distributed

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 137