Data Lake for Enterprises

Data Lake for EnterprisesReviews
Author: Tomcy John, Pankaj Misra
Pub Date: 2017
ISBN: 978-1787281349
Pages: 596
Language: English
Format: PDF/EPUB/AZW3
Size: 59 Mb

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A practical guide to implementing your enterprise data lake using Lambda Architecture as the base
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.
What You Will Learn

  • Build an enterprise-level data lake using the relevant big data technologies
  • Understand the core of the Lambda architecture and how to apply it in an enterprise
  • Learn the technical details around Sqoop and its functionalities
  • Integrate Kafka with Hadoop components to acquire enterprise data
  • Use flume with streaming technologies for stream-based processing
  • Understand stream- based processing with reference to Apache Spark Streaming
  • Incorporate Hadoop components and know the advantages they provide for enterprise data lakes
  • Build fast, streaming, and high-performance applications using ElasticSearch
  • Make your data ingestion process consistent across various data formats with configurability
  • Process your data to derive intelligence using machine learning algorithms