Mar 27

Am investigating various offerings with Hadoop. This is a very good article by Sriram Krishnan and Eva Tse from Netflix. Smile Awesome.. Smile

Hadoop has become the de facto standard for managing and processing hundreds of terabytes to petabytes of data. At Netflix, our Hadoop-based data warehouse is petabyte-scale, and growing rapidly. However, with the big data explosion in recent times, even this is not very novel anymore. Our architecture, however, is unique as it enables us to build a data warehouse of practically infinite scale in the cloud (both in terms of data and computational power).

In this article, we discuss our cloud-based data warehouse, how it is different from a traditional data center-based Hadoop infrastructure, and how we leverage the elasticity of the cloud to build a system that is dynamically scalable. We also introduce Genie, which is our in-house Hadoop Platform as a Service (PaaS) that provides REST-ful APIs for job execution and resource management.

Architectural Overview

In a traditional data center-based Hadoop data warehouse, the data is hosted on the Hadoop Distributed File System (HDFS). HDFS can be run on commodity hardware, and provides fault-tolerance and high throughput access to large datasets. The most typical way to build a Hadoop data warehouse in the cloud would be to follow this model, and store your data on HDFS on your cloud-based Hadoop clusters. However, as we describe in the next section, we have chosen to store all of our data on Amazon’s Storage Service (S3), which is the core principle on which our architecture is based. A high-level overview of our architecture is shown below, followed by the details.

The Netflix Tech Blog: Hadoop Platform as a Service in the Cloud

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