In this chapter, we went through various parts of a data lake and the Lambda Architecture at a high level and established a foundation for chapters later in this book. There, we will dive into greater technical details towards realizing this architecture. This chapter introduced the concept of data lake, some high-level concepts around data acquisition, messaging layer, ingestion layer, and Lambda Architecture layers, namely speed and batch. We also discussed, to some extent, the concepts around data storage and differences between storing data for random access and sequential access.
Data Lake for Enterprises
By :
Data Lake for Enterprises
By:
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
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Part 1 - Overview
Part 2 - Technical Building blocks of Data Lake
Part 3 - Bringing It All Together
Free Chapter
Introduction to Data
Comprehensive Concepts of a Data Lake
Lambda Architecture as a Pattern for Data Lake
Applied Lambda for Data Lake
Data Acquisition of Batch Data using Apache Sqoop
Data Acquisition of Stream Data using Apache Flume
Messaging Layer using Apache Kafka
Data Processing using Apache Flink
Data Store Using Apache Hadoop
Indexed Data Store using Elasticsearch
Data Lake Components Working Together
Data Lake Use Case Suggestions
Customer Reviews