Data Modeling in the Age of Big Data Training in Switzerland

Data architects; data modelers; database developers; data integrators; data analysts; report developers; anyone else challenged with the need to make structured enterprise data and non-traditional data sources work together.

• To distinguish between data store modeling (schema on write) and data access modeling (schema on read) and when each is useful
• The elemental characteristics of data that provide a common denominator for data modeling for all types of data
• How the common denominator is used to map various kinds of databases, including relational, dimensional, NoSQL, NewSQL, graph, and document
• When traditional logical-to-physical modeling works and when it makes sense to reverse the process as physical-to-logical
• Trade-offs between methodological rigor and discovery-driven exploration in data modeling

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

• A Quick Review

References and Resources

• To Learn More