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  5. Introduction to Google Cloud Bigtable

Introduction

The course is part of this learning path

Contents

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Introduction
Demo
Conclusion
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Overview
Transcript
DifficultyIntermediate
Duration49m
Students482

Description

Bigtable is an internal Google database system that’s so revolutionary that it kickstarted the NoSQL industry. In the mid 2000’s, Google had a problem. The web indexes behind its search engine had become massive and it took a long time to keep rebuilding them. The company wanted to build a database that could deliver real-time access to petabytes of data. The result was Bigtable.

Google went on to use Bigtable to power many of its other core services, such as Gmail and Google Maps. Finally, in 2015, it made Cloud Bigtable available as a service that its customers could use for their own applications.

In this course, you will learn which of your applications could make use of Bigtable and how to take advantage of its high performance.

Learning Objectives

  • Identify the best use cases for Bigtable
  • Describe Bigtable’s architecture and storage model
  • Optimize query performance through good schema design
  • Configure and monitor a Bigtable cluster
  • Send commands to Bigtable

Intended Audience

  • Data professionals
  • People studying for the Google Professional Data Engineer exam

Prerequisites

 

The example code is at https://github.com/cloudacademy/cloud-bigtable-examples/tree/master/java/dataproc-wordcount.

 

About the Author

Students8843
Courses25
Learning paths11

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).