The Netflix TechBlog

FEBRUARY 9, 2022

Let’s define some requirements that we are interested in delivering to the Netflix data engineers or anyone who would like to schedule a workflow with some external assets in it. How do you set up your deployment logic to know when to deploy the workflow to a test or dev environment? Is a single location enough? setup.py ???

Storage 210

Storage Data Engineering Testing Code 210

Dynatrace

JULY 6, 2022

Testing automation can be painstaking. It’s also crucial to test frequently when automating IT operations so that you don’t automatically replicate mistakes. This requires significant data engineering efforts, as well as work to build machine-learning models.

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

MARCH 2, 2021

Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group. Learning through data is in Netflix’s DNA. We use A/B tests to introduce new product features, such as our daily Top 10 row that help our members discover their next favorite show.

Analytics 216

Analytics C++ Innovation Engineering 216

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 185

Infrastructure Cloud Scalability AWS 185

The Netflix TechBlog

JULY 21, 2022

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. Some nuances while creating this dataset come from the on-field domain knowledge of our engineers.

Big Data 188

Big Data Cache Engineering Data Engineering 188

The Netflix TechBlog

NOVEMBER 20, 2023

IPS enables users to continue to use the data processing patterns with minimal changes. Introduction Netflix relies on data to power its business in all phases. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful). past 3 hours or 10 days).

Processing 219

Processing Big Data Efficiency Engineering 219

The Netflix TechBlog

SEPTEMBER 18, 2020

I focus on improving experimentation methodology to test how well the newest files are working: do they need less bits to stream while providing a higher video quality? When we test new encodes, we need effective data science methods to quickly and accurately understand whether customers are having a better experience.

Analytics 230

Analytics Education Innovation Engineering 230

O'Reilly

FEBRUARY 2, 2020

On one hand, ops groups are in a good position to do this; they’re already heavily invested in testing, monitoring, version control, reproducibility, and automation. This has important implications for testing. In the last two decades, a tremendous amount of work has been done on testing and building test suites.

Software Architecture 109

Software Architecture Monitoring Software Engineering Code 109

Testsigma

NOVEMBER 28, 2019

This article presents a list of some of the best available online Automation Testing courses and certifications you can consider. Hope this answers a few queries we receive for suggestions on Online Automation Testing Courses and Certifications. There are various courses listed on Udemy which can help people learn automation testing.

Website 53

Website Testing Programming Automotive 53

Abhishek Tiwari

FEBRUARY 26, 2020

Get test automation improved (10-20%). Increase automated test cover (30-40%) and start implementing property tests like performance and load testing. Test coverage (50-70%). Introduce site-reliability engineering best-practices (SLI/SLOs). Skills++: Induct Site-reliability engineers, Data engineers.

Technology 82

Technology Technology Architecture Engineering 82

O'Reilly

MARCH 2, 2020

Fetishizing unit testing. The most important is discovering how to work with data science and artificial intelligence projects. Development timelines for these projects aren’t as predictable as traditional software; they stretch the meaning of “testing” in strange ways; they aren’t deterministic.

Artificial Intelligence 27

Artificial Intelligence Software Architecture Programming C++ 27

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. This work would have not been possible without the solid, in-depth collaborations.

Tuning 217

Tuning Efficiency Big Data Engineering 217

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63

Latency Analytics Scalability Engineering 63

The Agile Manager

JULY 31, 2022

There are shadow IT teams of developers or data engineers that spring up in areas like operations or marketing because the captive IT function is slow, if not outright incapable, of responding to internal customer demand. Iteration plans are commitments; unit tests are guarantees of quality. The scope taken out of the 1.0

Programming 59

Programming Engineering Data Engineering Innovation 59

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

JULY 15, 2020

Most (74%) respondents say their teams own the build-test-deploy-maintain phases of the software lifecycle. Technical roles represented in the “Other” category include IT managers, data engineers, DevOps practitioners, data scientists, systems engineers, and systems administrators. Success with containers.

Database 145

Database Architecture Education Systems 145

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Abhishek Tiwari

JULY 20, 2019

Depending on work you can choose a smaller team of similar expertise (for example a team with mostly frontend engineers) or a smaller team of diverse expertise (team with balanced frontend, backend, data engineers). Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.

Engineering 61

Engineering Retail Airlines Healthcare 61

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 about what it’s like to be a Data Engineer at Netflix?

Innovation 245

Innovation Metrics Engineering Testing 245

The Netflix TechBlog

SEPTEMBER 10, 2019

Toby Mao , Sri Sri Perangur , Colin McFarland Another day, another custom script to analyze an A/B test. You can look at ABlaze (our centralized A/B testing platform) and take a quick look at how it’s performing. At any point a Netflix user is in many different A/B tests orchestrated through ABlaze. Not at Netflix.

Metrics 221

Metrics Architecture Infrastructure Innovation 221

The Netflix TechBlog

JANUARY 6, 2025

Batch processing data may provide a similar impact and take significantly less time. Its easier to develop and maintain, and tends to be more familiar for analytics engineers, data scientists, and data engineers. Additionally, if you are developing a proof of concept, the upfront investment may not be worth it.

Analytics 202

Analytics Engineering Cache Entertainment 202

O'Reilly

NOVEMBER 26, 2024

Entirely new paradigms rise quickly: cloud computing, data engineering, machine learning engineering, mobile development, and large language models. It’s less risky to hire adjunct professors with industry experience to fill teaching roles that have a vocational focus: mobile development, data engineering, and cloud computing.

Education 110

Education Azure AWS Java 110