Data Modeling in the Age of Big Data
Course Outline
Module 1 – Big Data Fundamentals
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What is Big Data
o Big Data
o NoSQL
o Structured Data
o Beyond Structured Data
Big Data Opportunities
o Beyond Enterprise Data
o Beyond Transactions
o Understanding Cause and Effect
o Business Impact
NoSQL Technologies
o Relational Technology
o Key-Value Stores
o Document-Oriented Databases
o Graph Databases
o Summary of Database Technologies
o Vendor Landscape
Big Data Challenges
o Beyond Enterprise Data
o Multiple Management Platforms
o Lack of Fixed Schema
o Multiple Uses for Data
o Traditional Focus on Transactions
o Relational Perspective
Exercise: Big Data Opportunities
Module 2 – Modeling and Data
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Models
o What is a Model?
o What is a Data Model?
o Why Model Data?
o More than a Diagram
Modeling for Relational Storage
o Relational Storage and BI
o Fixed Structure and Content
o Schema on Write
o Requirements First
o Data Modelers and Architects
© Adamson, Fuller, and Wells
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Modeling for Non-Relational Storage
o Big Data and BI
o Flexible Schema
o Big Data Notation
o Schema on Read
o Data First, Requirements Last
o Business SMEs, Analytic Modelers, and Programmers
Complementary Approaches
o Relational and Non-Relational Data
o Incremental Value of Big Data
o Rigor vs. Agility
o Roles
Exercise: Modeling Purpose
Module 3 – Key-Value Stores
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Key-Value Stores Defined
o The Basics
o NoSQL Foundation
Key-Value Data Representation
o Representing Things
o Representing Identities
o Representing Properties
o Representing Associations
o Representing Metrics
Use Cases
o Embedded Systems
o High-Performance In-Process Databases
o NoSQL Foundation
Examples
o Common Key-Value Store Products
Exercise: Key-Value Pairs Modeling
Module 4 – Document Stores
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Document Stores Defined
o Document-Oriented Databases
o Basic Terminology
o Flexible Internal Structure
o Document Stores and Key-Value Stores
o Fields Can Have Multiple Values
o Fields Can Contain Sub-Documents
o Summary of Characteristics
© Adamson, Fuller, and Wells
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Document Data Representation
o Representing Things
o Representing Identifiers
o Representing Properties
o Representing Associations
o Representing Metrics
Use Cases
o Choosing Document Storage
o Capture: Data Arrives in Document Format
o Explore Sources that Track Information Differently
o Augment
o Extend
Examples
o Common Document Store Databases
Exercise: Document Modeling
Module 5 – Graph Databases
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Graph Databases Defined
o The Basics
o Data about Relationships
o The Terminology – Nodes and Edges
o The Terminology – Hyperedges
o The Terminology – Properties
Graph Data Representation
o Representing Things
o Representing Identities
o Representing Associations
o Representing Properties
o Representing Metrics
Use Cases
o Social Networks
o Network Analysis and Visualization
o Semantic Networks
Examples
o Common Graph Database Products
Module 6 – Embracing Big Data
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BI Programs and Big Data
o Big Data and Information Asset Management
o The Gaps
 What Is Lost with Non-Relational
 BI and Analytics Gap
 Role/Skill Gaps
© Adamson, Fuller, and Wells
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o Organization and Planning
 Balancing Standards with Flexibility
 Organize Around Purpose, Not Tools
 IAM Roadmap Including Big Data
 Architecture Still Important
o The Journey
 Cataloging and Prioritizing Opportunities
 Evolving Skills
 Technology Decision Models
 Responding to Tool Failures
Human Side of Big Data
o Changing Role of Data Modeling
o Traditional Data Modeler Role
o More Roles Doing Data Modeling
o When Data Modeling Occurs
o Merging Data Modeling and Profiling
Tapping Into Big Data
o Process Agility and Flexibility Over Formality
o More Exploration, Iteration, and Risk
o Importance of Metadata
Taking the Next Steps
o Conversations to Gather Opportunities
o Proofs of Concept
o Business Case / ROI
o Ongoing Value of Data Modeling
o New Tools, Same Workbench
Exercise: Embracing Big Data
Module 7 – Summary and Conclusion
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Summary of Key Points
o A Quick Review
References and Resources
o To Learn More
© Adamson, Fuller, and Wells