The Enterprise Big Data Lake. Delivering the Promise of Big Data and Data Science

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• Preface
  • Who Should Read This Book?
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to Data Lakes
  • Data Lake Maturity
    • Data Puddles
    • Data Ponds
  • Creating a Successful Data Lake
    • The Right Platform
    • The Right Data
    • The Right Interface
      • Providing data at the right level of expertise
      • Getting to the data
    • The Data Swamp
  • Roadmap to Data Lake Success
    • Standing Up a Data Lake
    • Organizing the Data Lake
    • Setting Up the Data Lake for Self-Service
      • Finding and understanding the data
      • Accessing and provisioning the data
      • Preparing the data
      • Analysis and visualization
  • Data Lake Architectures
    • Data Lakes in the Public Cloud
    • Logical Data Lakes
      • Virtualization versus a catalog-based logical data lake
  • Conclusion
• 2. Historical Perspective
  • The Drive for Self-Service DataThe Birth of Databases
  • The Analytics ImperativeThe Birth of Data Warehousing
  • The Data Warehouse Ecosystem
    • Storing and Querying the Data
      • Dimensional modeling and star schemas
      • Slowly changing dimensions
      • Massively parallel processing (MPP) systems
      • Data warehouse (DW) appliances
      • Columnar stores
      • In-memory databases
    • Loading the DataData Integration Tools
      • ETL
      • ETL versus ELT
      • Federation, EII, and data virtualization tools
    • Organizing and Managing the Data
      • Data quality tools
      • MDM systems
      • Data modeling tools
      • Metadata repositories
      • Data governance tools
    • Consuming the Data
      • Advanced analytics
  • Conclusion
• 3. Introduction to Big Data and Data Science
  • Hadoop Leads the Historic Shift to Big Data
    • The Hadoop File System
    • How Processing and Storage Interact in a MapReduce Job
    • Schema on Read
    • Hadoop Projects
  • Data Science
  • What Should Your Analytics Organization Focus On?
  • Machine Learning
    • Explainability
    • Change Management
  • Conclusion
• 4. Starting a Data Lake
  • The What and Why of Hadoop
  • Preventing Proliferation of Data Puddles
  • Taking Advantage of Big Data
    • Leading with Data Science
    • Strategy 1: Offload Existing Functionality
    • Strategy 2: Data Lakes for New Projects
    • Strategy 3: Establish a Central Point of Governance
    • Which Way Is Right for You?
  • Conclusion
• 5. From Data Ponds/Big Data Warehouses to Data Lakes
  • Essential Functions of a Data Warehouse
    • Dimensional Modeling for Analytics
    • Integrating Data from Disparate Sources
    • Preserving History Using Slowly Changing Dimensions
    • Limitations of the Data Warehouse as a Historical Repository
  • Moving to a Data Pond
    • Keeping History in a Data Pond
    • Implementing Slowly Changing Dimensions in a Data Pond
      • Denormalizing attributes to preserve state
      • Preserving state using snapshots
  • Growing Data Ponds into a Data LakeLoading Data Thats Not in the Data Warehouse
    • Raw Data
    • External Data
    • Internet of Things (IoT) and Other Streaming Data
  • Real-Time Data Lakes
  • The Lambda Architecture
  • Data Transformations
  • Target Systems
    • Data Warehouses
    • Operational Data Stores
    • Real-Time Applications and Data Products
  • Conclusion
• 6. Optimizing for Self-Service
  • The Beginnings of Self-Service
  • Business Analysts
    • Finding and Understanding DataDocumenting the Enterprise
    • Establishing Trust
      • Data quality
      • Lineage (provenance)
      • Stewardship
    • Provisioning
    • Preparing Data for Analysis
  • Data Wrangling in the Data Lake
    • Situating Data Preparation in Hadoop
    • Common Use Cases for Data Preparation
      • Use case: Self-service automation for analytics or business applications
      • Customer example
      • Use case: Preparation for IT operationalization
      • Customer example
      • Use case: Exploratory analytics and machine learning
      • Customer example
  • Analyzing and Visualizing
  • The New World of Self-Service Business Intelligence
    • The New Analytic Workflow
    • Gatekeepers to Shopkeepers
    • Governing Self-Service
  • Conclusion
• 7. Architecting the Data Lake
  • Organizing the Data Lake
    • Landing or Raw Zone
    • Gold Zone
    • Work Zone
    • Sensitive Zone
      • Deidentification
  • Multiple Data Lakes
    • Advantages of Keeping Data Lakes Separate
    • Advantages of Merging the Data Lakes
  • Cloud Data Lakes
  • Virtual Data Lakes
    • Data Federation
    • Big Data Virtualization
    • Eliminating Redundancy
  • Conclusion
• 8. Cataloging the Data Lake
  • Organizing the Data
    • Technical Metadata
      • Data profiling
      • Profiling hierarchical data
    • Business Metadata
      • Glossaries, taxonomies, and ontologies
      • Industry ontologies
      • Folksonomies
  • Tagging
    • Automated Cataloging
  • Logical Data Management
    • Sensitive Data Management and Access Control
      • Automated and manual vetting
    • Data Quality
      • Tag-based data quality rules
      • Annotation quality
      • Curation quality
      • Data set quality
  • Relating Disparate Data
  • Establishing Lineage
  • Data Provisioning
  • Tools for Building a Catalog
    • Tool Comparison
  • The Data Ocean
  • Conclusion
• 9. Governing Data Access
  • Authorization or Access Control
  • Tag-Based Data Access Policies
  • Deidentifying Sensitive Data
    • Data Sovereignty and Regulatory Compliance
  • Self-Service Access Management
    • Provisioning Data
  • Conclusion
• 10. Industry-Specific Perspectives
  • Big Data in Financial Services
    • Consumers, Digitization, and Data Are Changing Finance as We Know It
    • Saving the Bank
    • New Opportunities Offered by New Data
    • Key Processes in Making Use of the Data Lake
      • Data inventory and cataloging
      • Entity resolution and fuzzy matching
      • Analytics and modeling
  • Value Added by Data Lakes in Financial Services
  • Data Lakes in the Insurance Industry
  • Smart Cities
  • Big Data in Medicine
• Index

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