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 137

Analytics Engineering Innovation Metrics 137

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. Working in Studio Data Science & Engineering (“Studio DSE”) was basically a dream come true.

Analytics 139

Analytics Retail Education Engineering 139

The Netflix TechBlog

FEBRUARY 14, 2025

Every image you hover over isnt just a visual placeholder; its a critical data point that fuels our sophisticated personalization engine. Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries.

Tuning 166

Tuning Latency Efficiency Storage 166

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 230

Analytics Education Innovation Engineering 230

The Netflix TechBlog

MARCH 5, 2019

Netflix’s engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility and they are free to operate with freedom to satisfy their mission. All these micro-services are currently operated in AWS cloud infrastructure.

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Infrastructure Cloud Scalability AWS 185

The Netflix TechBlog

DECEMBER 3, 2022

Languages Over time, the extraction of data from Netflix’s source systems has grown to encompass a wider range of end-users, such as engineers, data scientists, analysts, marketers, and other stakeholders. A large number of our data users employ SparkSQL, pyspark, and Scala.

Best Practices 361

Best Practices Code Testing Data Engineering 361

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 216

Analytics C++ Innovation Engineering 216

Dynatrace

JULY 6, 2022

As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. This requires significant data engineering efforts, as well as work to build machine-learning models.

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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.

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Processing Data Engineering Efficiency Analytics 234

The Netflix TechBlog

NOVEMBER 20, 2023

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.

Processing 219

Processing Big Data Efficiency Engineering 219

All Things Distributed

OCTOBER 7, 2015

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 112

Cloud Big Data AWS Analytics 112

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

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. And excellent logging is needed for debugging purposes and supportability.

Network 154

Network Tuning AWS Traffic 154

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

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 88

AWS Analytics Performance Data Engineering 88

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

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

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

Adrian Cockcroft

FEBRUARY 13, 2023

SUS206 Sustainability and AWS silicon  — Kamran Khan AWS Senior Product Manager Inferential/Trainium/FPGA, David Chaiken Pinterest Chief Architect, and Paul Mazurkiewicz AWS Senior Principal Engineer. Excellent talk on the NOAA programs to share data and build communities around it.

AWS 64

AWS Energy Architecture Programming 64

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. It is the underlying engine that allows QuickSight to deliver blazing fast response times on large data sets. How you can get started.

Analytics 123

Analytics Availability Media Social Media 123

The Netflix TechBlog

JANUARY 11, 2022

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?

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 202

Analytics Engineering Cache Entertainment 202

The Netflix TechBlog

DECEMBER 17, 2024

Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. One crucial way in which we do this is through the democratization of highly curated data sources that sunshine usage and cost patterns across Netflixs services and teams.

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Efficiency Cloud Analytics Infrastructure 206