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

Other Hadoop Processing Options

Apache Hadoop is something that will always pop up whenever a big data term is used. It has almost become a mandatory piece when dealing with Big Data. There is no doubt that Hadoop is an excellent choice, but it does have some inherent aspects that put a doubt in developers' minds when the choice has to be made, especially when big data and its processing is ever increasing in any enterprise, obviously due to changing business dynamics. Some of its pointed disadvantages are Hadoop's complexity and the way it actually does execution. Due to these reasons, there have been some recent innovations to simplify Hadoop processing further, and some of these simplifications have been brought in by the advent of Pig scripts and Apache Spark.

Pig scripts provide a good alternate to simplify MapReduce activity with pig Latin language, while still enabling non-Java developers to perform MapReduce via a simpler programming style.

Apache Spark streaming, on the other hand...