Data Modelling Recap
- Data modelling basics
- major constructs
- identifying entities
- Data model types, and the linkage between them
Levels of Models
- Enterprise, Conceptual, Logical & Physical
- What is the purpose of each, do we need all of these in a Big Data world
- Where does Dimensional modelling fit in?
Data Modelling – Back to the Future?
- Data Modelling didn’t start with relational! This may be a surprise to many people, but the first uses of data models were well before Relational data bases became the norm. The techniques are applicable to many of the modern non-relational formats we see today.
- Modelling in the pre-relational days. We didn’t have RDBMS’s. We had Flat files, Sequential, VSAM, Hierarchical DBMS’s, Network DBMS’s, Inverted Architecture DBMS’s.
- The techniques that were developed for these are directly appropriate to the NoSQL and Big Data world of today.
Data Modelling for Big Data & NoSQL
- What has to change when we are developing data models for a Hadoop or other Big Data environment?
- Do modelling tools support Big Data technologies, what are the restrictions and considerations?
- What data modelling techniques are applicable when targeting a Big Data platform?
- Does normalisation still have a place in the Big Data world?
- Where’s our metadata in the model now?
- In the age of big data, popular data modeling tools (eg ER/Studio, ERWin, PowerDesigner) continue to help us analyze and understand our data architectures by applying hybrid data modelling concepts. Instead of creating pure a relational data model, we now can embed NoSQL submodels within a relational data model. In general, data size and performance bottlenecks are the factors that help us decide which data goes to the NoSQL system.
- NoSQL & Hadoop: How the 4 types of NoSQL databases still need data models, and how the ACID vs BASE paradigm affects this.
Modelling for Hierarchic Systems & XML
- What must change when developing data models for XML & Hierarchic systems?
Services Oriented Architecture (SOA)
- Why data models are essential for success.
Massively Denormalised Files
- Is modelling needed?
- How do we create data models for Data lakes?
Dimensional Data Models
- How do we create a dimensional model?
- Converting an ER model to Dimensional.
- Slowly changing dimensions, what types and when are they applicable.
- Beyond the basics with conformed dimensions, bridges, junk dimensions & fact less facts.
Application Packages & Data Models
- Do we need to develop data models when implementing a COTS package?
- Uses and benefits.
Using Data Models for Data Integration & Lineage
- How to exploit data models for design of data integration approaches and in data lineage.
Top Down Requirements Capture
- When is it appropriate
- What are the limitations.
Bottom Up Requirements Synthesis
- When this works, where is it appropriate.
- How do we cope with existing DBMS’s and systems.
How to Capture Requirements for Both Data and Process Needs
- What comes first Data or Process – we’ll show the answer.
- The critical importance of understanding processes to get your data models right (and vice versa).
- Interaction between process and data models.
- Approaches for capturing Process AND Data Requirements.
Checking the Data vs the MetaData; Why Does it Matter?
Use of Standard Data Model Constructs and Pattern Models
- Understanding the Bill of materials (BOM) construct. Where can it be applied, why it’s one of the most powerful modelling constructs.
- Party; Role; Relationship: Why mastering this construct can provide phenomenal flexibility.
- Mastering Hierarchies: Different approaches for modelling hierarchies.
Different Data Modelling Notations & a Comparison Between Them
- Progressing beyond 3NF. 4NF, 5NF Boyce-Codd, and why, and when to use them