DMOD0: Data Modeling Learning Plan
In the Data Modeling Learning Plan, instructor Dave Wells equips participants with insights into the evolving landscape of data modeling practices and techniques. Participants will gain the skills necessary to apply appropriate techniques for a diverse range of use cases. With the increasing variety of data, from traditional structures to unstructured forms, and a broad spectrum of modeling applications, including database design, data products, data architecture, and data interoperability, Data Modeling has become more crucial, complex, and intriguing than ever.
Many organizations have neglected the practices of data modeling as they shifted from traditional data management practices of the past to adopt big data, data lakes, NoSQL technologies, and now AI and machine learning. Past practices focused on relational data and were typically relegated to logical and physical design for developing new databases. Today’s data modeling has a much larger scope in three significant ways:
- Modeling to understand content and structure of existing and acquired data as well as modeling to design new databases
- Semantic and conceptual modeling to go along with logical and physical modeling
- Modeling for all types of data including key-value, document-oriented, knowledge graphs, property graphs, etc.
The variety of data (from traditionally structured to unstructured), and the variety of modeling applications (from database design to data products, data architecture, and data interoperability) make data modeling more important than ever before. In the Data Modeling Learning Plan, instructor Dave Wells equips participants with insights into the evolving landscape of data modeling practices and techniques. Participants will gain the skills necessary to apply appropriate techniques for a diverse range of use cases.
In the Data Modeling Learning Plan you will:
- Grasp the expanded scope and complexity of data modeling in modern data management
- Learn the processes, purposes, and outcomes of contemporary data modeling
- Acquire foundational knowledge in various data models, their roles, and use cases
- Comprehend and interpret entity-relationship data models to express their meaning
- Develop and refine relational data models, applying normalization and abstraction
- Master physical modeling techniques and reverse engineer models from existing databases
- Construct conceptual and logical models for multi-dimensional data, ensuring granularity consistency
- Translate logical models into star schema designs, employing various dimension design techniques
- Understand key NoSQL data types and their modeling processes for both schema-on-read and schema-on-write
- Explore semantic data models, developing ontologies, knowledge graphs, and controlled vocabularies through semantic modeling techniques
- Learn terminology analysis techniques to develop standard and controlled vocabularies
- Learn how semantic models are applied to implement semantic layers, increase data interoperability, and guide data integration efforts
- And much more!
Individual Course Price: $99
Learning Plan Price: $419
Learning Plan CEUs: 10.0 hours
Each Course Includes:
- A 65- to 88-minute educational training video
- The videos for each course are divided into smaller sections for convenient viewing
- A 19- to 23-question exam
- “Check for Understanding” quizzes after each course section
- Self-paced and on-demand e-learning
- Unlimited course access
- Downloadable exercises and resources include:
- Example Company Profile
- Example Company Key-Value Files
- Example Company Order Document Data
- Example Company Recommendations System Requirements
- Exercise Solutions
- Course Slides in .pdf format
Courses within the Data Modeling Learning Plan:
- Data Modeling Fundamentals
- Relational Data Modeling
- Multi-Dimensional Data Modeling
- NoSQL Data Modeling
- Semantic Data Modeling
We offer several bulk licensing options for corporate and group use.
Contact us for a follow-up discussion!
Data Modeling Learning Plan
Course 1: Data Modeling Fundamentals
- Data Modeling Learning Plan – Course Overview
- Course Goals
- Sections
- What is Data Modeling?
- Models
- Data Model
- Data Modeling
- Data Model
- Models
- Data Modeling Purpose
- Analyze and Design
- Modeling Objectives
- Data Modeling as a Design and Knowledge Process
- Data Modeling Results
- The Data Modeling Landscape
- Scope of Data
- Flow of Data
- Data Modeling Context
- Data Modeling Levels
- Data Modeling Levels of Abstraction
- From Semantics to Specifications
- Top-Down and Bottom-Up
- Semantic Model
- Conceptual Model
- Logical Model
- Physical Model
- Technical Specification
- Data Model Types
- Model Type’s Relation to Types of Data Structure
- Relational and Dimensional Models
- Graph Models
- Taxonomic Models
- Document Models
- Data Modeling Use Cases
- Purposeful Data Modeling
- Data Modeling and Data Projects
- What and Where to Model
- Data Modeling and Data Systems
- A Quick Review
- Data Modelling Learning Plan – Progress Review
Course 2: Relational Data Modeling
- Data Modeling Learning Plan – Course Overview
- Course Curriculum
- Sections
- E-R Modeling Basics
- What is Relational Data?
- Model Components:
- Entities
- Relationships
- Relationship Cardinality
- Attributes
- Exercise: Reading an Entity –Relationship Model
- Conceptual Modeling
- The Primary Entities
- The Major Relationships
- The Important Attributes
- Exercise: Conceptual Modeling
- Logical Modeling
- The Entities
- The Relationships
- The Attributes
- Normalization Forms:
- 1st Normal Form
- 2nd Normal Form
- 3rd Normal Form
- Normalization Step by Step
- Abstraction – Attribute Abstraction
- Abstraction – Entity Abstraction
- State Transition – A Complementary Model
- Data Naming
- Exercise: Logical Modeling
- Physical Modeling
- Logical à Physical
- Entity Types à Tables
- Resolving Many-to-Many Relationships
- Modeling Foreign Key Relationships
- Column (Data Element) Naming
- Data Types
- Column Constraints
- SQL Data Types and Constraints
- Reverse Engineering the Models
- Reverse Engineering Vs. Design Engineering
- Reverse Engineering Process
- Examining the Database
- Profile the Data
- Developing:
- The Metadata
- The Physical Model
- The Logical Model
- The Semantic Model
- A Quick Review
- Data Modeling Learning Plan – Progress Review
Course 3: Multi-Dimensional Data Modeling
- Data Modeling Learning Plan – Courses
- Course Curriculum
- Sections
- Multi-Dimensional Modeling Basics
- What is Multi-Dimensional Data?
- Model Components
- Relational with Additional Constraints
- Conceptual Modeling
- Business Questions
- Measurement Subjects
- Measurement Categories
- Subject-Category Relationships
- The Conceptual Model
- Evolving Conceptual Model
- Exercise: Conceptual Modeling
- Logical Modeling
- Scope of the Logical Model
- The Meter
- Measures
- Dimensions:
- Hierarchy
- Attributes
- Granularity
- Exercise: Logical Modeling
- Physical Modeling
- From Logical Model to Star Schema
- Dimension Tables
- Dimension Tables Keys
- The Fact Table
- The Fact Table Key
- Exercise: Physical Modeling
- Dimension Design Techniques
- Junk Dimensions
- Degenerate Dimensions
- Slowly Changing Dimensions
- A Quick Review
- Data Modeling Learning Plan – Progress Review
Course 4: NoSQL Data Modeling
- Data Modeling Learning Plan – Course Overview
- Course Curriculum
- Sections
- NoSQL Modeling Basics
- The Essence of Every Data Model
- Common NoSQL Data Stories
- Why Model NoSQL Data?
- Modeling NoSQL Data
- Schema-On-Read Vs. Schema-On-Write
- Key Value Data Modeling
- Key-Value Data Store Concepts
- Modeling:
- Things
- Identities
- Properties
- Associations
- Key-Value Modeling Processes
- Exercise: Key-Value Modeling
- Document Data Modeling
- Document Store Concepts
- Document Example
- Conceptual Modeling:
- Things; Document Collection
- Things; Documents
- Things; Sub-Documents
- Associations; Hierarchy
- Document Conceptual Model
- Conceptual à Logical Model
- Modeling:
- Identities
- Properties
- Document Modeling Process
- Exercise: Document Modeling
- Graph Data Modeling
- Graph Database Concepts
- Graph Modeling Concepts
- Modeling:
- Things
- Identities
- Associations
- Properties of Things
- Properties of Associations
- Graph Modeling Process
- Exercise: Graph Modeling
- A Quick Review
- Data Modeling Learning Plan – Progress Review
Course 5: Semantic Data Modeling
- Data Modeling Learning Plan – Course Overview
- Course Curriculum
- Sections
- Semantic Data Modeling Basics
- Data Semantics Defined
- Semantic Web Modeling
- Semantic Modeling with Graphs
- Knowledge Graph Vs. Property Graph
- The Role of Semantics in Data Management
- Semantics and Data Interoperability
- Semantics and Data Integration
- Semantic Data Modeling Process
- Technology Analysis
- Semantic Data-Modeling Results
- Ontology and Taxonomy
- Ontology, Taxonomy and Graphs
- Graph Terminology
- Modeling Ontologies
- Semantic Modeling Plan
- Scope of a Semantic Modeling Project
- The Modeling Inputs
- Entity Analysis
- Relationship Analysis
- Definitions and Annotations
- Ontology as a Knowledge Graph
- Exercise: Ontology as a Knowledge Graph
- Properties Analysis
- More Definitions and Annotations
- Ontology as a Property Graph
- Exercise: Ontology as a Property Graph
- Modeling Taxonomies
- Extending Ontology with Taxonomy
- Taxonomy – Where and Why?
- Entity Taxonomy:
- Classification of Things
- Definitions and Annotation
- Subclasses and Properties Analysis
- More Properties Analysis
- Subclasses and Relationship Analysis
- Classifying Attribute Values
- Modeling Taxonomies
- Exercise: Modeling Concepts
- Properties Taxonomy – Classifying Attribute Values
- Exercise – Modeling Taxonomies
- The Enterprise Semantic Model
- After the Semantic Model is Created
- APIs, Data Services, Data Products, and Semantics
- Data Semantics in Data Management Architecture
- A Quick Review
- Data Modeling Learning Plan – Progress Review
Milestone
Complete All Five Data Modeling Courses
1. DMOD1: Data Modeling Fundamentals
required
Course
In this course, Dave Wells introduces participants to modern data modeling, highlighting the transition from traditional approaches to those driven by recent developments such as big data, analytics, data science, data lakes, NoSQL, and more. This evolution in data modeling underscores its growing relevance across various data-centric professions, including data engineering, data architecture, data analytics, and data science, making it an essential skill for today's data professionals.
View Details2. DMOD2: Relational Data Modeling
required
Course
Relational data modeling is the most widely practiced of all data modeling methods, the concepts and techniques of which provide fundamental capabilities to build upon and develop more recent and more advanced modeling skills. In this course, instructor Dave Wells equips participants with the knowledge and skills to expand this practice: including logical and conceptual modeling to resolve data disparity and improving data integration efforts. He also introduces reverse engineering models from tables in SaaS, ERP, and other operational systems to capture information needed for data integration and interoperability efforts.
View Details3. DMOD3: Multi-Dimensional Data Modeling
required
Course
In the course, Dave Wells teaches participants how to model multi-dimensional data, which is an extension of relational data modeling that includes additional constraints. The course emphasizes the importance of ensuring that the data model encompasses relevant business concepts and is structured efficiently. This approach results in a cohesive set of business metrics and performance indicators, enabling insightful analysis and informed decision-making. The course aims to provide participants with the knowledge to create data models that reflect a broader business scope and operate effectively, facilitating a comprehensive and interconnected suite of business metrics and performance indicators for profound analytical insights.
View Details4. DMOD4: NoSQL Data Modeling
required
Course
NoSQL data encompasses a range of formats, from key-value stores to graph databases, and is not organized in traditional relational row-and-column structures. This often leads to the misconception that data modeling techniques are not applicable to NoSQL data. However, upon closer examination, it becomes evident that data modeling is equally effective for NoSQL as it is for relational and multi-dimensional data. A data model is essentially a blueprint of the content and structure of a dataset. Regardless of the organization of the data, all data represents entities, relationships among entities, and attributes of entities. The process of identifying and understanding these elements is at the core of NoSQL data modeling. The fundamental principles remain consistent across key-value stores, document stores, and graph databases, although the techniques may vary.
View Details5. DMOD5: Semantic Data Modeling
required
Course
A semantic data model is the understructure of a semantic data layer that provides business-friendly data access. Mapping disparate data stores to a common semantic model builds a strong foundation for data interoperability, data exchange, and data sharing. In this course, instructor Dave Wells explores how all data, regardless of its native structure, can be viewed through a semantic lens and incorporated into a semantic data model. He provides participants with the ability to develop ontologies and taxonomies, and to collectively use these models to describe the standard terminology and agreed meaning of data in a problem domain.
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