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