Data Modeling Scheme

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

13 hours
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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Explain the strengths of each and the differences among the 12 types of data models. Create business terms, logical, and physical data models across relational, dimensional, document, and graph approaches. Apply practical techniques such as normalization, dimensional grain and meter design, JSON nested arrays, graph patterns (RDF and Property), denormalization, indexing, views, and partitioning. This course gives learners a complete journey through the Align (common business vocabulary), Refine (business requirements), and Design (technical solution) approach. Much more than a lecture, you will see lots of examples, compare modeling approaches, and build models step by step. By the end of the course, you will be better prepared to choose the right type of model for the right need and communicate your decisions with confidence. What makes this course unique is its breadth and practicality. Most courses stay in one modeling world. This one brings together relational, dimensional, document, and graph in one clear framework. It also connects business vocabulary, business requirements, and technical design through the powerful Align/Refine/Design approach, so learners can see how modeling works from start to finish. This is a course for people who want to become more competent and versatile data modelers.

Syllabus

Data Modeling Scheme Overview

Become adept at identifying the characteristics used to distinguish each of the 12 different types of data models. There are three levels of detail (align, refine, and design) and four main modeling mindsets (relational, dimensional, document, and graph), leading to 12 different types of models we can create. Align is where we create the business terms model (conceptual data model) to establish a common business vocabulary. Refine is where we create the logical data model to precisely capture the business requirements. Design is where we create the physical data model to document a technology-optimized version of the business requirements. Relational helps us capture business rules, primarily for operational applications (OLTP) and large integration hubs such as data warehouses. Dimensional helps us capture business questions, primarily for analytical applications (OLAP). Document helps us capture nested structures, used for storing and retrieving highly related content (like documents). Graph helps us capture patterns in the data, used for understanding and predicting connections. We'll cover the 12 types at a high level and provide an example of each.

Relational Business Terms Model (Align), also known as Conceptual

Learn all about the relational BTM and build one too! Explore several examples of the relational BTM to fully experience the characteristics of this type of model. Next, journey through the steps in building a relational BTM. Finally, design a relational BTM.

Dimensional Business Terms Model (Align), also known as Conceptual

Learn all about the dimensional BTM and build one too! Explore several examples of the dimensional BTM to fully experience the characteristics of this type of model. Next, journey through the steps in building a dimensional BTM. Finally, design a dimensional BTM.

Document Business Terms Model (Align), also known as Conceptual

Learn all about the document BTM and build one too! Explore several examples of the document BTM to fully experience the characteristics of this type of model. Next, journey through the steps in building a document BTM. Finally, design a document BTM.

Graph Business Terms Model (Align), also known as Conceptual

Learn all about the graph BTM and build one too! Explore several examples of the graph BTM to fully experience the characteristics of this type of model. Next, journey through the steps in building a graph BTM. Finally, design a graph BTM.

Relational Logical Data Model (Refine)

Learn all about the relational LDM and build one too! Explore several examples of the relational LDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a relational LDM. Finally, design a relational LDM. Apply all of the levels of normalization, from First Normal Form (1NF) all the way through Fifth Normal Form (5NF)!

Dimensional Logical Data Model (Refine)

Learn all about the dimensional LDM and build one too! Explore several examples of the dimensional LDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a dimensional LDM. Finally, design a dimensional LDM.Compare and contrast the dimensional modeling components of measure, meter, dimension, and grain. Apply the best practices of dimensional modeling through a series of what-to-do and what-not-to-do guidelines for your modeling in analytics applications.Become competent in incorporating different types of measures and dimensions into your models.

Document Logical Data Model (Refine)

Learn all about the document LDM and build one too! Explore several examples of the document LDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a document LDM. Finally, design a document LDM.

Graph Logical Data Model (Refine)

Learn all about the graph LDM and build one too! Explore several examples of the graph LDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a graph LDM. Finally, design a graph LDM.

Relational Physical Data Model (Design)

Learn all about the relational PDM and build one too! Explore several examples of the relational PDM to fully experience the characteristics of this type of model. There are four main techniques used as building blocks in the physical: denormalization, indexing, views, and partitioning. Explain the different ways to denormalize and know the pros and cons of each technique. Contrast the different types of indexes and know the default translations from logical to physical. Define views and know the pros and cons of traditional versus materialized views. Describe horizontal and vertical partitioning, and know when to use each.

Dimensional Physical Data Model (Design)

Learn all about the dimensional PDM and build one too! Explore several examples of the dimensional PDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a dimensional PDM. Finally, design a dimensional PDM. Apply several modeling techniques specific to dimensional physical modeling, including the use of junk and degenerate dimensions.

Document Physical Data Model (Design)

Learn all about the document PDM and build one too! Explore several examples of the document PDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a document PDM. Finally, design a document PDM.

Graph Physical Data Model (Design)

Learn all about the graph PDM and build one too! Explore several examples of the graph PDM to fully experience the characteristics of this type of model. Next, journey through the steps in building a graph PDM. Finally, design a graph PDM.