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

Batch layer for data processing

The core of Hadoop technology has been its ability to perform faster, performant, and optimized batch processes. It proved to be a big success in solving some of the more complex problems of long-running batch processing within organizations. The initial implementations of Hadoop were based on open source Hadoop distributions; however, with the inherent need to make it professionally supported, there were a number of features that were incorporated to make it feasible for enterprise use in terms of provisioning, management, monitoring, and alerting. This resulted in some of the more customized distributions led by MapR, Cloudera, and Hortonworks:

Figure 03: The Hadoop 1 framework

As shown in this image, the Hadoop 1 framework can be broadly classified into Storage and Processing. Storage here is represented by Hadoop Distributed File System (HDFS) while processing is represented as a MapReduce API. Hadoop 2 included many of the improved capabilities with the...