Data Modeling Fundamentals

Best Practices Approach to Developing a Competency in Data Modeling

23 - 24 June 2020 

Melbourne Business School, Carlton

Steve Hoberman’s Data Modeling Workshops are recognized as the most comprehensive in the industry.

Top 4 Objectives :

    • Understand the value that using data models can deliver in your organization.
    • Determine how and when to use each data modeling component.
    • Build relational and dimensional conceptual, logical, and physical data models.
    • Incorporate supportability and extensibility features into the data model.

Who Should Attend

This workshop is designed for anyone with data, analyst, architect, developer, database and modeler in their job title. No prior knowledge is assumed.

2 Day Workshop

$ 1,990
  • LIMITED PLACES. BOOK EARLY.
  • Academic Registrations are available for University staff and students under 28 years

2 Day Workshop + DMZ Conference

$ 3,400
  • OUR BEST PRICED DEAL
  • Partner Association Members receive 20% Discount on DMZ Asia Pacific conference and workshop packages
SAVE $580

About this Workshop

Data modeling is about understanding the data used within our operational and analytics processes, documenting this knowledge in a precise form called the “data model”, and then validating this knowledge through communications with both business and IT stakeholders. Underlying all successful applications is a robust and precise data model, and similarly, most software development failures are due to a lack of understanding of the data or data requirements.

A data model is, therefore, an essential part of applications development including forward engineering, reverse engineering, and integration efforts. Forward engineering means focusing on business requirements, whereas reverse engineering means modeling existing systems to drive the support, replacement, or customization of applications. Integration projects such as business intelligence efforts, data lakes, and master data initiatives, require a consistent holistic view of concepts such as Customer, Account, and Product.

Data Modeling Fundamentals contains two days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.

Top 4 Objectives:

  • Understand the value that using data models can deliver in your organisation.
  • Determine how and when to use each data modeling component.
  • Build relational and dimensional conceptual, logical, and physical data models.
  • Incorporate supportability and extensibility features into the data model.
Day 1

Modeling Basics:

Assuming no prior knowledge of data modeling, we work on our first case study to illustrate four important gaps filled by data models. Next, we explore data modeling concepts and terminology, and with a set of questions. We quickly and precisely build a data model, explaining each component and gain practice reading business rules. We will complete several exercises, including creating a data model based upon an existing set of data. 

You will be able to answer the following questions: 

  • What is a data model, and what characteristics make a data model an essential wayfinding tool?
  • What are critical skills for a data modeler?
  • Why is precision so important?
  • What three situations can ruin a data model’s credibility? 

You will also learn these concepts, terms and skills:

Concepts & Terms

  • Applying the 80/20 rule to data modeling
  • Entities, attributes, and relationships
  • Exclusive and non-exclusive subtypes
  • Candidate, primary, natural, alternate, and foreign keys
  • Surrogate keys
  • Cardinality and referential integrity
  • Recursion

Skills

  • Six questions to translate ambiguity into precision
  • How to “read” a data model
  • Asking the most important Questions when reviewing a data model
  • Use different modeling notations
  • Represent subtypes
  • Model hierarchies and networks
Day 2

Understanding conceptual, logical, and physical data models:

The conceptual data model captures a business need within a well-defined scope, the logical data model captures the business solution, and the physical data model captures the technical solution. Relational, dimensional, and NoSQL techniques will be described at each of these three levels.

We will also practice building several data models, and you will be able to answer the following questions:

  • How do relational and dimensional models differ?
  • What are the ten different types of data models?
  • Why are conceptual and logical data models so important?
  • What are four different ways of communicating the conceptual?
  • What are the six conceptual data modeling challenges?
  • What is the lure of NoSQL?
  • What are the advantages and disadvantages of going to “schema-less”?
  • What is MongoDB?

You will also learn these concepts, terms and skills:

Concepts & Terms

  • Concept and Question Templates  
  • Grain, base, and atomic on a dimensional 
  • Transaction, snapshot and accumulating facts  
  • Conformed dimensions 
  • Junk, degenerate, and behavioural dimensions  
  • Outriggers, measureless meters, and bridge tables  
  • A star schema and a snowflake 
  • The Attributes Template 
  • Aggregation and summarisation 
  • Slowly Changing Dimensions 
  • NoSQL and RDBMS 
  • Document, Column, Key-value, and Graph databases 
  • ACID and BASE  
  • Physical and implementation data models 

Skills

  • Use the five strategic conceptual modeling questions
  • Build a conceptual data model
  • Capture a program-level view of business questions
  • Navigate a dimensional data model
  • Leveraging the grain matrix
  • Apply the Normalization Hike
  • Use views, indexing, and partitioning to improve performance
  • Subtyping on a physical data model
  • Using denormalisation

About Steve Hoberman

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. He is known for his entertaining and interactive teaching style (watch out for flying candy!) and is often brought in by organisations to build data skills.

Steve’s Data Modeling Master Class is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. Steve is also and the author of Blockchainopoly.

One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, lecturer at Columbia University, and recipient of the DAMA International Professional Achievement Award.

Venue

Workshop Times

  • Registration 8.00 am
  • Workshop starts 8.30 am
  • Workshop close 4.3o pm

Lunch and refreshments are provided.

What should I bring?

  • This is a participative workshop. A detailed handbook is provided complete with case studies.

Parking

  • The University Square Public car park is a 5-minute walk from the venue.
  • Casual Entry: $25.00 all day. 

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