Aprašymas
About This BookComprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architectureEfficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalabilityPacked with industry best practices and use-case scenarios to get you up-and-runningWho This Book Is ForThis book is for architects and senior managers who are responsible for building a strategy around their current data architecture. The reader will need a good knowledge of master data management, information lifecycle management, data governance, data product design, data engineering, and systems architecture. Also required is experience of Big Data technologies such as Hadoop, Spark, Splunk, and Storm.What You Will LearnIdentify the need for Data Lake in your enterprise context and learn to architect a Data LakeLearn to build the various tiers of a Data Lake, including data intake, management, consumption, and governance, with a focus on practical implementation scenariosFind out the key considerations to be taken into account while building each tier of a Data LakeUnderstand the Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modesExplore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologiesEnable data discovery on the Data Lake to allow users to discover dataDiscover how data is packaged and provisioned for consumptionComprehend the importance of including data governance disciplines while building a Data LakeIn Detail
A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and guides you through building a Data Lake managed by Hadoop and accessed as required by other Big Data applications such as Spark, Storm, and Hive.
This book will take readers through the intake, management, and consumption processes for developing a Data Lake. It will also explore crucial considerations while building Data Lakes, with a focus on how to architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of how to build a Data Lake with Big Data.
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