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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.
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| Colección: | Community experience distilled.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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