The Netflix TechBlog

FEBRUARY 13, 2023

We will then present a case study of using these components in order to optimize, scale, and solidify an existing pipeline. Media Feature Storage: Amber Storage Media feature computation tends to be expensive and time-consuming. We accomplish this by paving the path to: Accessing and processing media data (e.g.

Media 290

Media Storage Infrastructure Systems 290

All Things Distributed

JUNE 26, 2016

AdiMap uses Amazon Kinesis to process real-time streaming online ad data and job feeds, and processes them for storage in petabyte-scale Amazon Redshift. For more details, see the case studies at All AWS Customer Stories. Seamless ingestion of large volumes of sensed data. Let’s build groundbreaking innovations together.

AWS 90

AWS Cloud Healthcare Blockchain 90

Highly Scalable

MAY 1, 2012

This opportunity is leveraged in the following case study. Case Study. Case Study. The straightforward approaches for implementation of this system are: Log all events in a large storage like Hadoop and compute unique visitor periodically using heavy MapReduce jobs or whatever. Case Study.

Analytics 191

Analytics Traffic Big Data Efficiency 191

Highly Scalable

APRIL 2, 2012

The system is implemented in Java. The deployment schema includes three types of nodes – processing nodes, storage nodes, and maintenance nodes. Storage nodes are basically Coherence storage nodes. Both Storage and Maintenance nodes do not serve client requests. Deployment Schema and High-Level Architecture.

Ecommerce 100

Ecommerce Cache Storage Architecture 100