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...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing
2015.
|
| Edición: | 1st edition |
| Colección: | Community experience distilled.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629675106719 |
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
- Big Data implications for security and privacy
