Key FeaturesBuild a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the baseDelve into the big data technologies required to meet modern day business strategiesA highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-casesBook DescriptionThe 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 LearnBuild an enterprise-level data lake using the relevant big data technologiesUnderstand the core of the Lambda architecture and how to apply it in an enterpriseLearn the technical details around Sqoop and its functionalitiesIntegrate Kafka with Hadoop components to acquire enterprise dataUse flume with streaming technologies for stream-based processingUnderstand stream- based processing with reference to Apache Spark StreamingIncorporate Hadoop components and know the advantages they provide for enterprise data lakesBuild fast, streaming, and high-performance applications using ElasticSearchMake your data ingestion process consistent across various data formats with configurabilityProcess your data to derive intelligence using machine learning algorithmsAbout the AuthorTomcy John lives in Dubai (United Arab Emirates), hailing from Kerala (India), and is an enterprise Java specialist with a degree in Engineering (B Tech) and over 14 years of experience in several industries. Hes currently working as principal architect at Emirates Group IT, in their core architecture team. Prior to this, he worked with Oracle Corporation and Ernst & Young. His main specialization is in building enterprise-grade applications and he acts as chief mentor and evangelist to facilitate incorporating new technologies as corporate standards in the organization. Outside of his work, Tomcy works very closely with young developers and engineers as mentors and speaks at various forums as a technical evangelist on many topics ranging from web and middleware all the way to various persistence stores. He writes on various topics in his blog and www.javacodebook.comPankaj Misra has been a technology evangelist, holding a bachelors degree in engineering, with over 16 years of experience across multiple business domains and technologies. He has been working with Emirates Group IT since 2015, and has worked with various other organizations in the past. He specializes in architecting and building multi-stack solutions and implementations. He has also been a speaker at technology forums in India and has built products with scale-out architecture that support high-volume, near-real-time data processing and near-real-time analytics.Table of ContentsIntroduction to DataComprehensive Data Lake conceptsLambda Architecture as a Pattern for Data LakeApplied Lambda for Data LakeData Acquisition of Batch Date with Apache SqoopData Acquisition of Stream Data with Apache FlumeMessaging Layer with Apache KafkaData Processing with Apache FlinkData Storage using Apache HadoopIndexed Data StoreData Lake components working togetherUse case suggestions