The Netflix TechBlog

DECEMBER 18, 2020

Part of our series on who works in Analytics at Netflix?—?and and what the role entails by Alex Diamond This Q&A aims to mythbust some common misconceptions about succeeding in analytics at a big tech company. Within a few months I’d picked up BI tools, predictive modeling, and data ingestion/ETL.

Analytics 138

Analytics Retail Education Engineering 138

The Netflix TechBlog

FEBRUARY 14, 2025

Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries. We can experiment with different content placements or promotional strategies to boost visibility and engagement.

Tuning 165

Tuning Latency Efficiency Storage 165

The Netflix TechBlog

OCTOBER 30, 2020

Part of our series on who works in Analytics at Netflix?—?and I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. Being an Analytics Engineer is like being a hybrid of a librarian ?? One of my favorite things about being an Analytics Engineer is the variety.

Analytics 136

Analytics Engineering Innovation Metrics 136

The Netflix TechBlog

SEPTEMBER 18, 2020

Part of our series on who works in Analytics at Netflix?—?and and what the role entails by Julie Beckley & Chris Pham This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

Analytics 227

Analytics Education Innovation Engineering 227

The Netflix TechBlog

MARCH 5, 2019

While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. What will be the cost of rolling out the winning cell of an AB test to all users?

Infrastructure 184

Infrastructure Cloud Scalability AWS 184

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 212

Analytics C++ Innovation Engineering 212

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.

Best Practices 360

Best Practices Code Testing Data Engineering 360

Dynatrace

JULY 6, 2022

This requires significant data engineering efforts, as well as work to build machine-learning models. Big data automation tools. These tools provide the means to collect, transfer, and process large volumes of data that are increasingly common in analytics applications. Creating a sound IT automation strategy.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

NOVEMBER 14, 2023

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

Processing 230

Processing Data Engineering Efficiency Analytics 230

DZone

JANUARY 13, 2023

However, in recent years there has been an exponential increase in the amount of data that connected systems produce, which has brought about a need for new ways to store and analyze such information.

IoT 178

IoT Analytics Database Metrics 178

The Netflix TechBlog

NOVEMBER 20, 2023

These challenges are currently addressed in suboptimal and less cost efficient ways by individual local teams to fulfill the needs, such as Lookback: This is a generic and simple approach that data engineers use to solve the data accuracy problem. Users configure the workflow to read the data in a window (e.g.

Processing 214

Processing Big Data Efficiency Engineering 214

All Things Distributed

OCTOBER 7, 2015

While BI solutions have existed for decades, customers have told us that it takes an enormous amount of time, engineering effort, and money to bridge this gap. These solutions lack interactive data exploration and visualization capabilities, limiting most business users to canned reports and pre-selected queries.

Cloud 114

Cloud Big Data AWS Analytics 114

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. These characteristics allow for an on-call response time that is relaxed and more in line with traditional big data analytical pipelines.

Network 152

Network Tuning AWS Traffic 152

InfoQ

AUGUST 7, 2024

Canva evaluated different data massaging solutions for its Product Analytics Platform, including the combination of AWS SNS and SQS, MKS, and Amazon KDS, and eventually chose the latter, primarily based on its much lower costs. The company compared many aspects of these solutions, like performance, maintenance effort, and cost.

AWS 87

AWS Analytics Performance Data Engineering 87

Simform

JANUARY 8, 2023

Data ingestion is the foremost layer in a data engineering pipeline, acting as a vital pillar in the overall analytics architecture. Thus, it is essential to implement data ingestion just right. Here is everything you need to know to take the first step toward a flawless data pipeline.

Data Engineering 52

Data Engineering Analytics Architecture Engineering 52

Abhishek Tiwari

JULY 12, 2018

Building data pipelines can offer strategic advantages to the business. It can be used to power new analytics, insight, and product features. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines. Data pipeline initiatives are generally unfinished projects.

Latency 63

Latency Analytics Scalability Engineering 63

O'Reilly

SEPTEMBER 15, 2021

We also asked respondents what tools they used for statistics and machine learning and what platforms they used for data analytics and data management. The highest salaries were associated with Clicktale (now ContentSquare), a cloud-based analytics system for researching customer experience: only 0.2% The Last Word.

Azure 145

Azure Programming AWS Social Media 145

DZone

DECEMBER 21, 2020

Setting up a data warehouse is the first step towards fully utilizing big data analysis. Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation.

Big Data 196

Big Data Google Scalability Processing 196

Abhishek Tiwari

DECEMBER 27, 2017

Data solution vendors like SnapLogic and Informatica are already developing machine learning and artificial intelligence (AI) based smart data integration assistants. These assistants can recommend next-best-action or suggest datasets, transforms, and rules to a data engineer working on a data integration project.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

All Things Distributed

NOVEMBER 15, 2016

They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Data is automatically replicated across multiple Availability Zones for redundancy and also backed up to S3 for durability. How you can get started.

Analytics 125

Analytics Availability Media Social Media 125

The Netflix TechBlog

JANUARY 11, 2022

Curious to learn more about other Data Science and Engineering functions at Netflix? To learn about Analytics and Viz Engineering, have a look at Analytics at Netflix: Who We Are and What We Do by Molly Jackman & Meghana Reddy and How Our Paths Brought Us to Data and Netflix by Julie Beckley & Chris Pham.

Innovation 245

Innovation Metrics Engineering Testing 245

The Netflix TechBlog

JANUARY 6, 2025

This article is the last in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Its easier to develop and maintain, and tends to be more familiar for analytics engineers, data scientists, and data engineers.

Analytics 199

Analytics Engineering Cache Entertainment 199

Adrian Cockcroft

FEBRUARY 13, 2023

STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions. AWS Inferentia 2.6x shorter training time, saving 54% energy and 75% cost. Good example, well presented interesting new material.

AWS 64

AWS Energy Architecture Programming 64

The Netflix TechBlog

DECEMBER 17, 2024

This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engineers and analysts collaborate to provide actionable insights to the engineering organization in a continuous feedback loop that ultimately enhances the business.

Efficiency 202

Efficiency Cloud Analytics Infrastructure 202