The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 190

Big Data Cache Engineering Data Engineering 190

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. Key challenges.

Big Data 63

Big Data Storage Benchmarking Hardware 63

The Netflix TechBlog

NOVEMBER 20, 2023

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. past 3 hours or 10 days).

Processing 221

Processing Big Data Efficiency Engineering 221

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.

Artificial Intelligence 202

Artificial Intelligence Tuning Strategy Big Data 202

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. Rule Execution Engine is responsible for matching the collected logs against a set of predefined rules.

Tuning 219

Tuning Efficiency Big Data Engineering 219

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 219

Analytics C++ Innovation Engineering 219

The Netflix TechBlog

SEPTEMBER 18, 2020

—?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. Photo from a team curling offsite?—?There’s There’s us to the right!

Analytics 232

Analytics Education Innovation Engineering 232

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 214

Java Scalability Traffic Architecture 214

The Netflix TechBlog

DECEMBER 21, 2020

Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster.

Storage 214

Storage Latency Efficiency Data Engineering 214

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

Uber Engineering

AUGUST 14, 2019

Once identified, … The post Less is More: Engineering Data Warehouse Efficiency with Minimalist Design appeared first on Uber Engineering Blog. In our experience, optimizing for operational efficiency requires answering one key question: for which tables does the maintenance cost supersede utility?

Efficiency 96

Efficiency Engineering Design Storage 96

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. Big data challenges.

Cloud 116

Cloud Big Data AWS Analytics 116

The Netflix TechBlog

MAY 26, 2020

As with any sustainable engineering design, focusing on simplicity is very important. These characteristics allow for an on-call response time that is relaxed and more in line with traditional big data analytical pipelines. Requirements There are multiple ways you can solve this problem and many technologies to choose from.

Network 155

Network Tuning AWS Traffic 155

High Scalability

MARCH 24, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Triplebyte is unique because they're a team of engineers running their own centralized technical assessment.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

APRIL 14, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Triplebyte is unique because they're a team of engineers running their own centralized technical assessment.

Education 105

Education Software Engineering Scalability Engineering 105

High Scalability

MARCH 30, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Triplebyte is unique because they're a team of engineers running their own centralized technical assessment.

Education 105

Education Software Engineering Engineering Big Data 105

High Scalability

MAY 12, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Triplebyte is unique because they're a team of engineers running their own centralized technical assessment.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

APRIL 28, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Triplebyte is unique because they're a team of engineers running their own centralized technical assessment.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

MAY 26, 2020

Scrapinghub is hiring a Senior Software Engineer (Big Data/AI). this is going to be a challenging journey for any backend engineer! this is going to be a challenging journey for any backend engineer! Created by former senior-level AWS engineers of 15 years. Try out their platform. Please apply here.

Education 100

Education AWS Software Engineering Scalability 100

Abhishek Tiwari

DECEMBER 27, 2017

In 2018, we will see new data integration patterns those rely either on a shared high-performance distributed storage interface ( Alluxio ) or a common data format ( Apache Arrow ) sitting between compute and storage. For instance, Alluxio, originally known as Tachyon, can potentially use Arrow as its in-memory data structure.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

Abhishek Tiwari

JULY 7, 2023

In the era of big data and complex data processing, data pipelines have emerged as a popular solution for managing and manipulating data. They provide a systematic approach to extract, transform, and load (ETL) data from various sources, enabling organizations to derive valuable insights.

Logistics 52

Logistics Transportation Scalability Data Engineering 52

InfoQ

APRIL 15, 2024

He specifically delved into Venice DB, the NoSQL data store used for feature persistence. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz

Artificial Intelligence 109

Artificial Intelligence Big Data Data Engineering Latency 109

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. There is a strong argument for ELT i.e. extract, load, and transform model. Classic ETL. Late transformation.

Big Data 40

Big Data Retail Storage Google 40

InfoQ

JULY 3, 2023

LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually. By Rafal Gancarz

Cache 85

Cache Latency Traffic Database 85

The Netflix TechBlog

SEPTEMBER 10, 2019

Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute. The two main challenges with this approach are establishing an easy contribution framework and handling Netflix’s scale of data.

Metrics 222

Metrics Architecture Infrastructure Innovation 222

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. The cost and complexity to implement, scale, and use BI makes it difficult for most companies to make data analysis ubiquitous across their organizations.

Analytics 129

Analytics Availability Media Social Media 129