Building The Data Warehouse Course Description
Course Introduction
Dimensional
modeling has emerged as the only coherent architecture for building distributed
data warehouse systems. The core dimensional modeling techniques those are the
concepts such as slowly changing dimensions, heterogeneous products, factless
fact tables, and architected data marts continue to be discussed in data
warehouse design workshops around the globe.
This course
is started with basic concepts and introduce more advanced content as the book
unfolds. And later this course will developing the design techniques by example
is an extremely effective approach because it allows us to share very tangible
guidance.
Course Objectives
At the end of
the course, students will be able to:
- Explain the basic concepts underlying dimensional modeling
- Got some experiences from case studies.
Audience:
The course is
appropriate for technical management, project advisors, technologists,
designers, testers, and developers.
Prerequisites:
Participants
should have a basic understanding of the database technologies, exposure to the
software development process.
Course Texts
John
Wiley and Sons, Inc, he Data Warehouse Toolkit, Second Edition, The
Complete Guide to Dimensional Modeling 2005, Ralph Kimball Margy Ross.
Course Contents:
Chapter 1: Dimensional Modeling Primer
Explore
the components of the overall data warehouse architecture, establish core vocabulary,
dispel some of the myths and misconceptions about dimensional modeling, and also discuss the role of normalized models.
Chapter 2: Retail Sales
Retailing
is the classic example used to illustrate dimensional modeling. This chapter is
begun by discussing the four-step process for designing dimensional models, then
explore dimension tables in depth, including the date dimension. We also
discuss degenerate dimensions, snow flaking, and surrogate keys.
Chapter 3: Inventory
We
remain within the retail industry for our second case study but turn our
attention to another business process. This case study will provide a very
vivid example of the data warehouse bus architecture and the use of conformed
dimensions and facts. These concepts are critical to anyone looking to construct
a data warehouse architecture that is integrated and extensible.
Chapter 4: Procurement
This
chapter reinforces the importance of looking at your organization’s value chain
as you plot your data warehouse. We also explore a series of basic and advanced
techniques for handling slowly changing dimension attributes.
Chapter 5: Order Management
In
this case study we take a look at the business processes that are often the
first to be implemented in data warehouses as they supply core business performance
metrics—what are we selling to which customers at what price? We discuss the
situation in which a dimension plays multiple roles within a schema. We also
explore some of the common challenges modelers face when dealing with order
management information, such as header/line item considerations, multiple
currencies or units of measure, and junk dimensions with miscellaneous
transaction indicators. We compare the three fundamental types of fact tables:
transaction, periodic snapshot, and accumulating snapshot. Finally, we provide
recommendations for handling more real-time warehousing requirements.
Chapter 6: Customer Relationship Management
Numerous
data warehouses have been built on the premise that we need to better understand
and service our customers. This chapter covers key considerations surrounding
the customer dimension, including address standardization, managing large
volume dimensions, and modeling unpredictable customer hierarchies. It also
discusses the consolidation of customer data from multiple sources.
Chapter 7: Accounting
In
this totally new chapter we discuss the modeling of general ledger information
for the data warehouse. We describe the appropriate handling of year-to-date
facts and multiple fiscal calendars, as well as the notion of consolidated
dimensional models that combine data from multiple business processes.
Chapter 8: Human Resources Management
This
new chapter explores several unique aspects of human resources dimensional
models, including the situation in which a dimension table begins to behave
like a fact table. We also introduce audit and keyword dimensions, as well as
the handling of survey questionnaire data.
Chapter 9: Financial Services
The
banking case study explores the concept of heterogeneous products in which each
line of business has unique descriptive attributes and performance metrics.
Obviously, the need to handle heterogeneous products is not unique to financial
services. We also discuss the complicated relationships among accounts,
customers, and households.
Chapter 10: Telecommunications and Utilities
This
new chapter is structured somewhat differently to highlight considerations
when performing a data model design review. In addition, we explore the
idiosyncrasies of geographic location dimensions, as well as opportunities for
leveraging geographic information systems.
Chapter 11: Transportation
In
this case study we take a look at related fact tables at different levels of
granularity. We discuss another approach for handling small dimensions, and we
take a closer look at date and time dimensions, covering such concepts as
country-specific calendars and synchronization across multiple time zones.
Chapter 12: Education
We
look at several factless fact tables in this chapter and discuss their importance
in analyzing what didn’t happen. In addition, we explore the student
application pipeline, which is a prime example of an accumulating snapshot fact
table.
Chapter 13: Health Care
Some
of the most complex models that we have ever worked with are from the health
care industry. This new chapter illustrates the handling of such complexities,
including the use of a bridge table to model multiple diagnoses and providers
associated with a patient treatment.
Chapter 14: Electronic Commerce
This
chapter provides an introduction to modeling clickstream data. The concepts
are derived from The Data Webhouse Toolkit (Wiley 2000), which Ralph Kimball
coauthored with Richard Merz. Introduction
Chapter 15: Insurance
The
final case study serves to illustrate many of the techniques we discussed
earlier in the book in a single set of interrelated schemas. It can be viewed
as a pulling-it-all-together chapter because the modeling techniques will be
layered on top of one another, similar to overlaying overhead projector
transparencies.
Chapter 16: Building the Data Warehouse
Now
that you are comfortable designing dimensional models, we provide a high-level
overview of the activities that are encountered during the lifecycle of a
typical data warehouse project iteration. This chapter could be considered a
lightning tour of The Data Warehouse Lifecycle Toolkit (Wiley 1998) that we
coauthored with Laura Reeves and Warren Thornthwaite.
Chapter 17: Present Imperatives and Future Outlook
In
this final chapter we peer into our crystal ball to provide a preview of what
we anticipate data warehousing will look like in the future.
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