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Data Lake development with Big Data : explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies /
Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manag...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2015.
|
| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Pasupuleti, Pradeep, |e author. | |
| 245 | 1 | 0 | |a Data Lake development with Big Data : |b explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies / |c Pradeep Pasupuleti, Beulah Salome Purra. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2015. | |
| 300 | |a 1 online resource : |b illustrations | ||
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| 490 | 1 | |a Community experience distilled | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed December 20, 2016). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a Cover; Copyright; Credits; About the Authors; Acknowledgement; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Need for Data Lake; Before the Data Lake; Need for a Data Lake; Defining Data Lake; Key benefits of Data Lake; Challenges in implementing a Data Lake; When to go for a Data Lake implementation; Data Lake architecture; Architectural considerations; Architectural composition; Architectural details; Understanding Data Lake layers; Understanding Data Lake tiers; Summary; Chapter 2: Data Intake; Understanding Intake tier zones | |
| 505 | 8 | |a Source System Zone functionalitiesUnderstanding connectivity processing; Understanding Intake Processing for data variety; Transient Landing Zone functionalities; File validation checks; Data Integrity checks; Raw Storage Zone functionalities; Data lineage processes; Deep Integrity checks; Security and governance; Information Lifecycle Management; Practical Data Ingestion scenarios; Architectural guidance; Structured data use cases; Semi-structured and Unstructured data use cases; Big Data tools and technologies; Ingestion of structured data; Ingestion of streaming data; Summary | |
| 505 | 8 | |a Chapter 3: Data Integration, Quality, and EnrichmentIntroduction to the Data Management Tier; Understanding Data Integration; Introduction to Data Integration; Prominent features of Data Integration; Practical Data Integration scenarios; The workings of Data Integration; Raw data discovery; Data quality assessment; Data cleansing; Data transformations; Data enrichment; Collect Metadata and track data lineage; Traditional data integration versus Data Lake; Data pipelines; Data partitioning; Scale on demand; Data ingest parallelism; Extensibility; Big Data tools and technologies; Syncsort | |
| 505 | 8 | |a Use case scenarios for SyncsortTalend; Use case scenarios for Talend; Pentaho; Use case scenarios for Pentaho; Summary; Chapter 4: Data Discovery and Consumption; Understanding the Data Consumption tier; Data Consumption -- Traditional versus Data Lake; An introduction to Data Consumption; Practical Data Consumption scenarios; Data Discovery and metadata; Enabling Data Discovery; Data classification; Relation extraction; Indexing data; Performing Data Discovery; Semantic search; Faceted search; Fuzzy search; Data Provisioning and metadata; Data publication; Data subscription | |
| 505 | 8 | |a Data Provisioning functionalitiesData formatting; Data selection; Data Provisioning approaches; Post-provisioning processes; Architectural guidance; Data discovery; Big Data tools and technologies; Data Provisioning; Big Data tools and technologies; Summary; Chapter 5: Data Governance; Understanding Data Governance; Introduction to Data Governance; The need for Data Governance; Governing Big Data in the Data Lake; Data Governance -- traditional versus Data Lake; Practical Data Governance scenarios; Data Governance components; Metadata management and lineage tracking; Data security and privacy | |
| 520 | |a Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In 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 explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data ... | ||
| 546 | |a English. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Electronic data processing |x Distributed processing |x Management. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Systèmes d'information. | |
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| 650 | 7 | |a information retrieval services. |2 aat | |
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| 650 | 7 | |a Electronic data processing |x Distributed processing |x Management. |2 fast |0 (OCoLC)fst00906991 | |
| 650 | 7 | |a Information storage and retrieval systems. |2 fast |0 (OCoLC)fst00972781 | |
| 700 | 1 | |a Purra, Beulah Salome, |e author. | |
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