Extended TDWI Data Modeling: An In-Depth Tutorial on Data Warehouse Design & Analysis Techniques

Transcription

1 : An In-Depth Tutorial on Data Warehouse Design & Analysis Techniques Class Format: The class is an instructor led format using multiple learning techniques including: lecture to present concepts, principles, methods, and techniques, class discussion to personalize information presented by lecture, exercises to reinforce concepts, principles and techniques, self-tests to ensure comprehension and improve retention, exercises to apply techniques and develop data models, an extended workshop to combine all of the techniques and apply them to the clients specific industry. The workshop will be conducted in a small team format, with groups of three to six people working together to develop solutions and present them to the rest of the class for review. The program is designed to be highly interactive and participative. Learning Objectives: On completion of this course, successful students will be able to: Understand the role and purpose of data modeling in data warehousing projects Describe the various kinds of data stores in a data warehousing environment Identify differences between application data modeling and warehouse data modeling Distinguish between conceptual, logical, and physical modeling objectives and techniques Understand the purpose of subject modeling and develop a subject area model. Apply a variety of techniques to develop a robust and representative list of business questions Map business questions to an implementation-neutral data structure (fact/qualifier model) Identify clusters of data elements with natural affinity to be grouped as data marts Make informed choices between relational and dimensional data structures Design a data flow configuration of warehousing data stores Develop logical E/R models for relational data warehouses and marts Develop logical dimensional models for dimensional data marts Understand normalization (to 3NF) and know when to apply it in data warehousing Distinguish between snapshot and audit data structures and know when to use each Adjust logical models to satisfy time, security, distribution, access, and navigation requirements Map logical dimensional models as star-schema Describe the optimization considerations for data warehouses and data marts Understand and apply a variety of optimization techniques Understand and apply surrogate and synthetic key mapping techniques Describe modeling techniques for slowly-changing dimensions Describe modeling techniques for ragged and unbalanced hierarchies Understand and model degenerate dimensions Agenda: Day One: Module One - Introduction to Data Modeling for Data Warehousing The Data Warehousing Institute page 1 of 5

2 Day Two: Data Modeling Concepts roles of modeling data characteristics definitions The Warehouse Data Modeler the roles the skill set differences from application data modeling Warehousing Data Stores staging database data warehouse data marts Exercise: Data Store Configuration Modeling Techniques Overview subject modeling entity/relationship modeling fact/qualifier modeling dimensional data modeling state-transition modeling data store characteristics modeling techniques and model components Module Two - Requirements Analysis Models Requirements Modeling Overview objectives and context where in the development life cycle? Developing Business Questions: Purpose, Process, and Techniques Exercise: Developing Business Questions Subject Modeling: Purpose, Process, and Techniques Exercise: Subject Area Modeling Fact/Qualifier Analysis Overview Fact/Qualifier Analysis: Mapping Business Questions Exercise: Mapping Business Questions Module Two Requirements Analysis Models (continued) Fact Qualifier Analysis: Fact Analysis and Refinement Fact/Qualifier Analysis: Qualifier Analysis and Refinement Exercise: Fact/Qualifier Model Refinement Target Configuration Modeling: Purpose, Process, and Techniques business questions techniques subject modeling techniques The Data Warehousing Institute page 2 of 5

3 Day Three: mapping business questions fact analysis and refinement qualifier analysis and refinement data store configurations Module Three / Unit A Design & Specification Modeling Concepts Normalization State-Transition Modeling: Purpose, Process, and Techniques Exercise: State-Transition Modeling Source Data Triage Modeling for Time-Variance Modeling for Distribution and Security Modeling for Access and Navigation Optimization Techniques derivation aggregation summarization partitioning surrogate and synthetic keys optimization special cases Exercise: Applying Optimization Techniques normalization triage state-transition time-variance distribution and security access and navigation optimization techniques Module Three / Unit B Designing Data Marts relational vs. dimensional data marts logical, structural, and physical models Modeling Relational Data Marts logical modeling techniques and example structural considerations implementation considerations physical modeling techniques and example Modeling Dimensional Data Marts Logical Modeling identifying the meter identifying the measures identifying dimensions understanding dimension hierarchies understanding dimension characteristics The Data Warehousing Institute page 3 of 5

4 Exercise: Logical Dimensional Modeling Extended Dimensional Techniques ragged hierarchies unbalanced hierarchies multiple hierarchies degenerate dimensions conformed dimensions Modeling Dimensional Data Marts Structural & Physical Modeling structural considerations and optimization implementation considerations and optimization MOLAP vs ROLAP implementation star schema design snowflake schema design Exercise: Developing a Star Schema relational vs. dimensional data marts relational modeling techniques logical dimensional modeling star schema design Module Three / Unit C Designing Data Warehouses Logical Modeling Structural and Physical Modeling Module Three / Unit D Designing Staging Databases Logical Modeling Structural and Physical Modeling optimization considerations for data marts optimization considerations for the data warehouse optimization considerations for staging databases Day Four: Module Four Data Modeling and Design Summary and Review Modeling Techniques Modeling Objectives Optimization Considerations Modeling Checklist Extended Workshop With instructor as facilitator/consultant, focus on client s business and industry as a group identify business drivers discuss common business measures The Data Warehousing Institute page 4 of 5

5 in small teams develop a list of business questions map the business questions as facts and qualifiers refine the fact/qualifier model identify candidate data marts develop data stores configuration flow diagram select a dimensional data mart for design develop a logical dimensional model design a star schema for the data mart verify that the design can answer the targeted business questions The Data Warehousing Institute page 5 of 5