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 138

Analytics Retail Education Engineering 138

The Netflix TechBlog

DECEMBER 3, 2022

Obviously not all tools are made with the same use case in mind, so we are planning to add more code samples for other (than classical batch ETL) data processing purposes, e.g. Machine Learning model building and scoring. This allows other processes, consuming our table, to be notified and start their processing.

Best Practices 358

Best Practices Code Testing Data Engineering 358

All Things Distributed

NOVEMBER 15, 2016

Previously, I wrote about Amazon QuickSight , a new service targeted at business users that aims to simplify the process of deriving insights from a wide variety of data sources quickly, easily, and at a low cost. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 152

Analytics Availability Media Social Media 152

Dynatrace

JULY 6, 2022

At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.

Artificial Intelligence 194

Artificial Intelligence Tuning Strategy Big Data 194

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. Capacity and Efficiency Which resources (clusters, tables, …) are unused or under-utilized and why?

Infrastructure 182

Infrastructure Cloud Scalability AWS 182

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 207

Analytics C++ Innovation Engineering 207

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 137

Cloud Big Data AWS Analytics 137

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

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

DZone

DECEMBER 21, 2020

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. This is where data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transformed (ELT).

Big Data 189

Big Data Google Scalability Processing 189

Abhishek Tiwari

DECEMBER 27, 2017

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49