Book Image

Data Lake for Enterprises

By : Vivek Mishra, Tomcy John, Pankaj Misra
Book Image

Data Lake for Enterprises

By: Vivek Mishra, Tomcy John, Pankaj Misra

Overview of this book

The term "Data Lake" has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake.
Table of Contents (23 chapters)
Title Page
About the Authors
About the Reviewers
Customer Feedback
Part 1 - Overview
Part 2 - Technical Building blocks of Data Lake
Part 3 - Bringing It All Together

History of Lambda Architecture

Nathan Marz coined the term Lambda Architecture (LA) to describe a generic pattern for data processing that is scalable and fault-tolerant. He gathered this expertise working extensively with big-data-related technologies at BackType and Twitter. The pattern is conceptualized to handle/process a huge amount of data by using two of its important components, namely batch and speed layer. Nathan generalized his findings and experience in the form of this pattern, which should cater to some of the important architecture principles, such as these:

  • Linear scalable: It should scale out and not up and should cater to different kinds of use cases
  • Fault-tolerant: Capable of a wide range of workloads, it should also shield the system from hardware and software failure and inherent human mistakes
  • Backtype: Reads and updates
  • Extensibility: Manageable, easy to extend, and easy to keep adding new features and data elements

There is a wealth of details documented at http://lambda...