A Novel Approach of Design & Implementation of Cloud Big Table

Abstract

Cloud Bigtable, which is sparsely populated table to scale billions of rows and thousands of columns, enabling storing petabytes or terabytes of data across thousands of commodity servers. Google has had to resolve the challenges that many companies bearing the difference is the sheer scale of the problem. They’ve often had to design entirely new approaches to meet the demand of their businesses. Over the past decade, Google has developed many traditional solutions to carry their own products and services. They’ve proposed many of these internal solutions in white papers and so many have developed into open source projects that now are the footing of the Hadoop ecosystem. Five foundational Google projects that have changed the era of big data landscape forever. Many projects at Google store data in Bigtable, including Google MapReduce (Apache Hadoop), Google Bigtable (Apache HBase), Google “Borg”(Apache Mesos), Google Chubby(Apache Zookeeper), Google Dremel(Apache Drill). These are just a few examples of the ways Google has set the stage for the Bigdata revolution. Bigtable has successfully furnished a flexible, high-performance solution for all of these Google products. In this paper we represent the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, features of cloud Bigtable, and we describe the design and implementation of Bigtable. This paper describes overview of the client API, the underlying Google infrastructure on which Bigtable depends, fundamentals of the Bigtable implementation; We describe several examples of how Bigtable is used at Google.