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Advanced Data Warehousing Workshop: Multi-Dimensional Modeling
 
 
 
 
ibm certified training   this course works with savings passes
 
code. DW820   length. 4 days
type. Instructor-Led   partner. IBM
price.
$2,600  
 
Skill level: Advanced
 
This is an advanced workshop covering the subject of multi-dimensional modeling (MDDM), consisting of two parts. Part 1 presents all of the basic MDDM concepts, terminology, and techniques, including guidelines, heuristics and metrics where appropriate. Part 2 of the workshop further extends the coverage of MDDM modeling, by investigating a well-chosen set of advanced modeling techniques, which are representative of situations where the models have to be built for large (corporate) data warehouses or broad-scope data marts.

In Part 1, you will be able to produce Star- and Snowflake models, corresponding to a given set of end-user requirements, basically expressed as queries and hypothesis for which the DW should provide an answer. By taking part in this part of the course, you will also understand the differences and the positioning/mapping between E/R modelling, Data Warehouse modeling, and multi-dimensional modeling. The advanced MDDM techniques in the Part 2 workshop have several major advantages, up and above the learning of the techniques themselves: Studying advanced modeling issues and their solutions is highly advantageous for understanding the positioning of Star- and Snowflake modeling in the company's overall (corporate) data architecture. Similarly, advanced MDDM modeling techniques are very instrumental for studying the positioning of Temporal E/R models (in most cases, the base technique for corporate warehouses) versus dimensional models and for understanding when and how BOTH approaches may be combined.

For a detailed survey of the techniques covered by both parts of the workshop, please refer to the topical course description and agenda available below.

The workshop is designed as a highly interactive workshop. In the first part, approximately half of the time spent on the subject is in applying the techniques in exercises which are part of a consistently developed, non-trivial case study. This case study is most likely to be a "Supply and Inventory Management" warehouse development project.

The second part of the workshop consists of individually developed case study investigations of advanced MDDM modeling techniques. In this sense, the workshop becomes easily adaptable to specific customer contexts.

The workshop is designed so that it can be used in a standard classroom setting as well as in the context of given data mart development projects (mentored data warehouse development projects).
 
course schedule  
 
There are currently no scheduled dates for this course. If you are interested in this course, request a course date with the links below.
   
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who can benefit
 
 
This advanced course is for Data warehouse modelers with a prime interest in dimensional modeling for OLAP data marts, data base administrators and data warehouse data administrators, business intelligence experts and information analysts with an interest in multi-dimensional data warehouse modeling, and project managers and technical professionals with an interest in MDDM and a good background knowledge of data modeling (E/R modeling for traditional OLTP databases).
 
prerequisites
 
 
You should complete:

Relational Database Design (CF18)
Architecting the Data Warehouse (DW11)
OR
Have knowledge of the basics of data warehouse enablement and of OLAP information analysis in particular
Be familiar with data modeling for traditional (OLTP) database applications
Be familiar with relational database development and with relational database management systems in general


 
skills gained
 
 
By the end of the course the students will be able to:

Apply MDDM basic principles to defined business requirements
Produce dimensional models (Star- and Snowflake models) for non-trivial data warehouse modeling projects
Position MDDM with respect to ER modeling and to Temporal ER modeling in particular


 
ibm education advantage program eligibility:
 
 

Yes - IBM Education Pack - online account




 
course content details  
 


  Introduction to Data Warehouse Modeling

Review of data warehousing and data warehouse modeling
Differentiate data warehouse modeling from OLTP modeling
Discuss data warehouse modeling approaches and investigate their area of applicability
State the basics of analytical data processing and its relationship to data modeling




  Multi-Dimensional Modeling - Methodology

Define the base concepts of Multi-dimensional Data Modeling (MD modeling): terminology, definitions and notation techniques
Apply initial MD modeling techniques
Discuss various MD modeling hints, tips and guidelines
Apply solution validation issues and techniques
Define the detailed modeling of dimensions




  Non-Temporal Design - R-OLAP

Describe the most important design issues associated with a given multi-dimensional data model in a given OLAP query context: Star schemas and Snowflake schemas
Identify the features of DB2 that help improve the handling of table associations (joins) and typical OLAP queries in an MDDM context
Explain how Star-Join support, provided by DB2, works
Propose solutions for the design issues covered
Evaluate the effects of various alternative design techniques
Utilize Cubing Services to improve R-OLAP and M-OLAP performance




  Non-Temporal Design - M-OLAP

Apply M-OLAP techniques to build analysis solutions
Deliver a functional architecture, for producing a M-OLAP solution, based upon the review of the functions present in today's industry-leading M-OLAP tools
Decide whether M-OLAP or R-OLAP techniques would better suit a particular set of analysis requirements




  Temporal Modeling and Design

Explain the basic and advanced concepts of Historical Data Modeling
Discuss the different approaches of Fact Analysis




  Agenda - Day 1

Welcome
Introduction to Data Warehouse Modeling
Case Study Introduction
Multi-Dimensional Modeling - Methodology (Modeling Process Overview)
Case Study 1 and Review
Multi-Dimensional Modeling Methodology (Base Concepts and Terminology)
Case Study 2 and Review
Multi-Dimensional Modeling - Methodology (More Concepts and Terminology)
Case Study 3 and Review




  Day 2

MDM - Methodology (Star versus Snowflake Models)
Case Study 4 and Review
MDM - Methodology (State versus Event Facts)
Case Study 5 and Review
MDM - Methodology (Factless Facts and Fact Identifiers)
Case Study 6 and Review
MDM - Methodology (Dimension Roles)
Case Study 7 and Review
MDM - Methodology (Solution Validation)
Case Study 8 and Review




  Day 3

MDM - Methodology (Detailed Dimension Modeling)
Case Study 9 and Review
Case Study 10 and Review
Non-Temporal Design - R-OLAP: Basics
Non-Temporal Design - R-OLAP: Design and Implementation
Case Study 11 and Review
Case Study 12 and Review




  Day 4

Non-Temporal Design - M-OLAP
Temporal Modeling and Design with Inline Case Studies





 
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