The Netflix TechBlog

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

Processing 214

Processing Big Data Efficiency Engineering 214

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. After a few years of wearing many different proverbial hats, I put them all to use in the Analytics Engineer role here.

Analytics 138

Analytics Retail Education Engineering 138

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

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 360

Best Practices Code Testing Data Engineering 360

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 246

Artificial Intelligence Tuning Strategy Big Data 246

The Netflix TechBlog

MARCH 5, 2019

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure.

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

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 114

Cloud Big Data AWS Analytics 114

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 196

Big Data Google Scalability Processing 196

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

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

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

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 125

Analytics Availability Media Social Media 125

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. Batch processing data may provide a similar impact and take significantly less time. Need to catch up?

Analytics 199

Analytics Engineering Cache Entertainment 199

Adrian Cockcroft

FEBRUARY 13, 2023

This is the AWS Professional Services built tooling that customers can use to track the carbon footprint of their operations and processes, along with a customer example. STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions.

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