Programming is a complex and challenging field, and solving programming problems can often be difficult. In this report, we will examine the difficulties that I faced while attempting to solve a programming problem and the strategies I used to overcome them. Lack of Understanding of the Problem: One of the biggest challenges I faced while solving this programming problem was a lack of understanding of the problem itself. I was unsure of what the problem was asking for and what the expected outcome should be. To overcome this challenge, I spent time reading the problem statement multiple times and seeking clarification from the person who assigned the problem. Inadequate Knowledge of the Programming Language: Another challenge I faced was a lack of familiarity with the programming language required to solve the problem. I had to spend time researching and studying the language in order to understand how to use it effectively. To overcome this challenge, I used online resources such as tutorials and forums, and I practiced writing code in the language. Difficulty in Debugging the Code: One of the most frustrating challenges I faced was debugging the code. I encountered multiple errors in my code and struggled to identify the root cause of the problem. To overcome this challenge, I used a debugger to step through my code and identify the source of the errors. I also reviewed my code line by line to ensure that it was syntactically correct and semantically sound. Limited Time: Finally, I faced the challenge of limited time. I was under a tight deadline to complete the programming problem, and I was concerned that I would not be able to finish in time. To overcome this challenge, I prioritized the most pressing tasks and focused on finding a solution as quickly as possible. I also asked for an extension on the deadline if necessary. Conclusion: Solving a programming problem can be challenging, but with perseverance and the right approach, it is possible to overcome the difficulties. In this report, we have examined some of the challenges I faced while solving a programming problem and the strategies I used to overcome them. Through this experience, I have gained a deeper understanding of the programming process and Programming is a complex and challenging field, and solving programming problems can often be difficult. In this report, we will examine the difficulties that I faced while attempting to solve a programming problem and the strategies I used to overcome them. Lack of Understanding of the Problem: One of the biggest challenges I faced while solving this programming problem was a lack of understanding of the problem itself. I was unsure of what the problem was asking for and what the expected outcome should be. To overcome this challenge, I spent time reading the problem statement multiple times and seeking clarification from the person who assigned the problem.

1. Copyright © 2014 Pearson Education, Inc.
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CHAPTER 9:
DATA WAREHOUSING
Essentials of Database Management
Jeffrey A. Hoffer, Heikki Topi, V. Ramesh

2. Chapter 9 2
Copyright © 2014 Pearson Education, Inc.
OBJECTIVES
 Define terms
 Explore reasons for information gap
between information needs and availability
 Understand reasons for need of data
warehousing
 Describe three levels of data warehouse
architectures
 Describe two components of star schema
 Estimate fact table size
 Design a data mart
 Develop requirements for a data mart
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Copyright © 2014 Pearson Education, Inc.
DEFINITIONS
 Data Warehouse
 A subject-oriented, integrated, time-variant, non-
updatable collection of data used in support of
management decision-making processes
 Subject-oriented: e.g. customers, patients,
students, products
 Integrated: consistent naming conventions,
formats, encoding structures; from multiple data
sources
 Time-variant: can study trends and changes
 Non-updatable: read-only, periodically refreshed
 Data Mart
 A data warehouse that is limited in scope
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HISTORY LEADING TO DATA
WAREHOUSING
 Improvement in database technologies,
especially relational DBMSs
 Advances in computer hardware, including
mass storage and parallel architectures
 Emergence of end-user computing with
powerful interfaces and tools
 Advances in middleware, enabling
heterogeneous database connectivity
 Recognition of difference between
operational and informational systems
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NEED FOR DATA WAREHOUSING
 Integrated, company-wide view of high-
quality information (from disparate
databases)
 Separation of operational and
informational systems and data (for
improved performance)
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ISSUES WITH COMPANY-WIDE VIEW
Inconsistent key structures
Synonyms
Free-form vs. structured fields
Inconsistent data values
Missing data
See figure 9-1 for example
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Figure 9-1
Examples of
heterogeneous
data
Chapter 9 7
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ORGANIZATIONAL TRENDS
MOTIVATING DATA WAREHOUSES
 No single system of records
 Multiple systems not synchronized
 Organizational need to analyze
activities in a balanced way
 Customer relationship management
 Supplier relationship management
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9. Chapter 9 9
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SEPARATING OPERATIONAL AND
INFORMATIONAL SYSTEMS
 Operational system – a system that is used
to run a business in real time, based on
current data; also called a system of record
 Informational system – a system designed
to support decision making based on
historical point-in-time and prediction data for
complex queries or data-mining applications
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DATA WAREHOUSE ARCHITECTURES
Independent Data Mart
Dependent Data Mart and
Operational Data Store
Logical Data Mart and Real-Time
Data Warehouse
Three-Layer architecture
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All involve some form of extract, transform and load (ETL)

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Figure 9-2 Independent data mart
data warehousing architecture
Data marts:
Mini-warehouses, limited in scope
E
T
L
Separate ETL for each
independent data mart
Data access complexity
due to multiple data marts
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Copyright © 2014 Pearson Education, Inc.

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Figure 9-3 Dependent data mart with
operational data store: a three-level architecture
E
T
L
Single ETL for
enterprise data warehouse (EDW)
Simpler data access
ODS provides option for
obtaining current data
Dependent data marts
loaded from EDW
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Copyright © 2014 Pearson Education, Inc.

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E
T
L
Near real-time ETL for
Data Warehouse
ODS and data warehouse
are one and the same
Data marts are NOT separate databases,
but logical views of the data warehouse
 Easier to create new data marts
Figure 9-4 Logical data mart and real
time warehouse architecture
Chapter 9 14
Copyright © 2014 Pearson Education, Inc.

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Copyright © 2014 Pearson Education, Inc.

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Figure 9-5 Three-layer data architecture for a data warehouse
Chapter 9 16
Copyright © 2014 Pearson Education, Inc.

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DATA CHARACTERISTICS
STATUS VS. EVENT DATA
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Status
Status
Event = a
database action
(create/ update/
delete) that
results from a
transaction
Figure 9-6
Example of DBMS
log entry

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Copyright © 2014 Pearson Education, Inc.
With transient
data, changes
to existing
records are
written over
previous
records, thus
destroying the
previous data
content
Figure 9-7
Transient
operational data
DATA CHARACTERISTICS
STATUS VS. EVENT DATA

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Periodic data
are never
physically
altered or
deleted once
they have been
added to the
store
Figure 9-8 Periodic
warehouse data
DATA CHARACTERISTICS
STATUS VS. EVENT DATA

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Copyright © 2014 Pearson Education, Inc.
OTHER DATA WAREHOUSE CHANGES
 New descriptive attributes
 New business activity attributes
 New classes of descriptive attributes
 Descriptive attributes become more
refined
 Descriptive data are related to one
another
 New source of data
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Copyright © 2014 Pearson Education, Inc.
DERIVED DATA
 Objectives
 Ease of use for decision support applications
 Fast response to predefined user queries
 Customized data for particular target audiences
 Ad-hoc query support
 Data mining capabilities
 Characteristics
 Detailed (mostly periodic) data
 Aggregate (for summary)
 Distributed (to departmental servers)
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Most common data model = dimensional model
(usually implemented as a star schema)

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Figure 9-9 Components of a star schema
Fact tables contain factual
or quantitative data
Dimension tables contain descriptions
about the subjects of the business
1:N relationship between
dimension tables and fact tables
Excellent for ad-hoc queries, but bad for online transaction processing
Dimension tables are denormalized to
maximize performance
Chapter 9 22
Copyright © 2014 Pearson Education, Inc.

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Figure 9-10 Star schema example
Fact table provides statistics for sales
broken down by product, period and
store dimensions
Chapter 9 23
Copyright © 2014 Pearson Education, Inc.

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Figure 9-11 Star schema with sample data
Chapter 9 24
Copyright © 2014 Pearson Education, Inc.

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Copyright © 2014 Pearson Education, Inc.
SURROGATE KEYS
 Dimension table keys should be surrogate
(non-intelligent and non-business related),
because:
Business keys may change over time
Helps keep track of nonkey attribute
values for a given production key
Surrogate keys are simpler and shorter
Surrogate keys can be same length and
format for all key
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Copyright © 2014 Pearson Education, Inc.
GRAIN OF THE FACT TABLE
 Granularity of Fact Table–what level of
detail do you want?
Transactional grain–finest level
Aggregated grain–more summarized
Finer grains  better market basket
analysis capability
Finer grain  more dimension tables,
more rows in fact table
In Web-based commerce, finest
granularity is a click
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Copyright © 2014 Pearson Education, Inc.
DURATION OF THE DATABASE
 Natural duration–13 months or 5 quarters
 Financial institutions may need longer duration
 Older data is more difficult to source and cleanse
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Copyright © 2014 Pearson Education, Inc.
SIZE OF FACT TABLE
 Depends on the number of dimensions and the grain
of the fact table
 Number of rows = product of number of possible
values for each dimension associated with the fact
table
 Example: assume the following for Figure 9-11:
 Total rows calculated as follows (assuming only half
the products record sales for a given month):
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Figure 9-12 Modeling dates
Fact tables contain time-period data
 Date dimensions are important

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Copyright © 2014 Pearson Education, Inc.
VARIATIONS OF THE STAR SCHEMA
 Multiple Facts Tables
 Can improve performance
 Often used to store facts for different combinations of
dimensions
 Conformed dimensions
 Hierarchies
 Sometimes a dimension forms a natural, fixed depth
hierarchy
 Design options
 Include all information for each level in a single denormalized
table
 Normalize the dimension into a nested set of 1:M table
relationships 30

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Figure 9-13 Conformed dimensions
Conformed
dimension
Associated with
multiple fact
tables
Two fact tables  two (connected) start schemas.

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Copyright © 2014 Pearson Education, Inc.
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Figure 9-14 Fixed product hierarchy
Dimension hierarchies help to provide levels of
aggregation for users wanting summary information
in a data warehouse

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Copyright © 2014 Pearson Education, Inc.
SLOWLY CHANGING DIMENSIONS (SCD)
 How to maintain knowledge of the past
 Kimble’s approaches:
 Type 1: just replace old data with new (lose
historical data)
 Type 3: for each changing attribute, create a
current value field and several old-valued fields
(multivalued)
 Type 2: create a new dimension table row each
time the dimension object changes, with all
dimension characteristics at the time of change.
Most common approach.
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Figure 9-16 Example of Type 2 SCD Customer dimension table
The dimension table contains several records for the same
customer. The specific customer record to use depends on the
key and the date of the fact, which should be between start
and end dates of the SCD customer record.

35. Chapter 9 35
Copyright © 2014 Pearson Education, Inc.
10 ESSENTIAL RULES FOR
DIMENSIONAL MODELING
 Use atomic facts
 Create single-process
fact tables
 Include a date
dimension for each fact
table
 Enforce consistent
grain
 Disallow null keys in
fact tables
 Honor hierarchies
 Decode dimension tables
 Use surrogate keys
 Conform dimensions
 Balance requirements
with actual data
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Copyright © 2014 Pearson Education, Inc.
 Big Data = databases whose volume, velocity, and variety
strain the ability of relational DBMSs to capture, manage,
and process data in a timely fashion
 Issue of Big Data
 huge volume
 often unstructured (text, images, RFID, etc.)
 Columnar databases
 Column-based (rather than relational DB, which are row-based)
 optimize storage for summary data of few columns (different need than
OLTP)
 Data compression
 Sybase, Vertica, Infobright
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BIG DATA AND COLUMNAR DATABASES

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Copyright © 2014 Pearson Education, Inc.
 “Not only SQL”
 a class of database technology used to store and
access textual and other unstructured data, using
more flexible structures than the rows and columns
format of relational databases
 Example NoSql languages: Unstructured Query
Language (UQL), XQuery (for XML data)
 Example NoSql engines: Cassandra (used by
Facebook), Hadoop and MapReduce
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NOSQL

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Copyright © 2014 Pearson Education, Inc.
THE USER INTERFACE
METADATA (DATA CATALOG)
 Identify subjects of the data mart
 Identify dimensions and facts
 Indicate how data is derived from enterprise data
warehouses, including derivation rules
 Indicate how data is derived from operational data
store, including derivation rules
 Identify available reports and predefined queries
 Identify data analysis techniques (e.g. drill-down)
 Identify responsible people
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Copyright © 2014 Pearson Education, Inc.
ONLINE ANALYTICAL PROCESSING
(OLAP) TOOLS
 The use of a set of graphical tools that provides users with
multidimensional views of their data and allows them to
analyze the data using simple windowing techniques
 Relational OLAP (ROLAP)
 Traditional relational representation
 Multidimensional OLAP (MOLAP)
 Cube structure
 OLAP Operations
 Cube slicing–come up with 2-D view of data
 Drill-down–going from summary to more detailed
views
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Figure 9-18 Slicing a data cube
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Copyright © 2014 Pearson Education, Inc.

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Figure 9-19
Example of drill-down
a) Summary report
b) Drill-down with color attribute added
Starting with summary
data, users can obtain
details for particular
cells

42. Chapter 9 42
Copyright © 2014 Pearson Education, Inc.
BUSINESS PERFORMANCE MGMT (BPM)
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Figure 9-22
Sample Dashboard
BPM systems allow
managers to measure,
monitor, and manage
key activities and
processes to achieve
organizational goals.
Dashboards are often
used to provide an
information system in
support of BPM.
Charts like these are examples of data visualization, the representation
of data in graphical and multimedia formats for human analysis.

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Copyright © 2014 Pearson Education, Inc.
DATA MINING
 Knowledge discovery using a blend
of statistical, AI, and computer
graphics techniques
Goals:
Explain observed events or conditions
Confirm hypotheses
Explore data for new or unexpected
relationships
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All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
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Copyright © 2014 Pearson Education, Inc.