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Lecture Handout
Data Warehousing
Lecture No. 01
Why Data Warehousing?
The world is changing (actually changed), either change or be left behind.
Missing the opportunities or going in the wrong direction has prevented us from
What is the right direction?
Harnessing the data, in a knowledge driven economy.
The world economy has moved form the industrial age into information driven knowledge
economy. The information age is characterized by the computer technology, modern
communication technology and Internet technology; all are popular in the world today.
Governments around the globe have realized potential of information, as a "multi-factor" in the
development of their economy, which not only creates wealth for the society, but also affects the
future of the country. Thus, many countries in the world have placed the modern information
technology into their strategic plans. They regard it as the most important strategic resource for
the development their society, and are trying their best to reach and occupy the peak of the
modern information driven knowledge economy.
What is the right direction?
Ever since the IT revolution that happened more than a decade ago every government has been
trying and tried to increase our software exports. But have persistently failed to get the desired
results. I happened to meet a gentleman who got venture capital of several million US dollars and
I asked him why our software export has not gone up? His answer was simple, "we have been
investing in outgoing or outdated tools and technologies". We have also been just following
India, without thinking for a moment, what India is today, started maybe a decade ago. So my
next question was "what should we be doing today?" His answer was "we have captured and
stored data for a long time, now it is time to explore and make use of that data". There is a saying
that "a fool and his money are soon parted", since that gentleman was rich and is still rich, hence
he does qualify to be a wise man, and his words of wisdom to be paid attention to.
The Need for a Data Warehouse
"Drowning in data and starving for information"
"Knowledge is power, Intelligence is absolute power!"
Figure-1.1: Relationship between Data, Information, Knowledge & Intelligence
Data is defined as numerical or other facts represented or recorded in a form suitable for
processing by computers. Data is often the record or result of a transaction or an operation that
involves modification of the contents of a database or insertion of rows in tables. Information in
its simplest form is processed data that is meaningful. By processing, summarizing or analyzing
data, organizations create information. For example the current balance, items sold, money made
etc. This information should be designed to increase the knowledge of the individual, therefore,
ultimately being tailored to the needs of the recipient. Information is processed data so that it
becomes useful and provides answers to questions such as "who", "what", "where", and "when".
Knowledge, on the other hand is an application of information and data, and gives an insight by
answering the "how" questions. Knowledge is also the understanding gained through experience
or study. Intelligence is appreciation of "why", and finally wisdom (not shown in the figure -1.1)
is the application of intelligence and experience toward the attainment of common goals, and
wise people are powerful. Remember knowledge is power.
Historical Overview
It is interesting to note that DSS (Decision Support System) processing as we know it today has
reached this point after a long and complex evolution, and yet it continues to evolve. The origin
of DSS goes back to the very early days of computers.
Figure -1.2 shows the historical overview or the evolution of data processing from the early 1960s
upto 1980s. In the early 1960s, the world of computation consisted of exclusive applications that
were executed on master files. The applications featured reports and programs, using languages
like COBOL and punched cards i.e. the COBOLian era. The master files were stored on magnetic
tapes, which were good for storing a large volume of data cheaply, but had the drawback of
needing to be accessed sequentially, and being very unreliable (ask your system administrator
even today about tape backup reliability). Experience showed that for a single pass of a magnetic
tape that scanned 100% of the records, only 5% of the records, sometimes even less were actually
required. In addition, reading an entire tape could take anywhere from 20-30 minutes, depending
on the data and the processing required.
Master Files & Reports
Lots of Master files!
Direct Access Memory & DBMS
Online high performance transaction processing
PCs and 4GL Technology (MIS/DSS)
Extract programs, extract processing
The legacy system's web
Figure-1.2: Historical Overview of use of Computers for Data Processing
Around the mid-1960s, the growth of master files and magnetic tapes exploded. Soon master files
were used at every computer installation. This growth in usage of master files, resulted in huge
amounts of redundant data. The spreading of master files and massive redundancy of data
presented some very serious problems, such as:
Data coherency i.e. the need to synchronize data upon update.
Program maintenance complexity.
Program development complexity.
Requirement of additional hardware to support many tapes.
In a nut -shell, the inherent problems of master files because of the limitations of the medium
used started to become a bottleneck. If we had continued to use only the magnetic tapes, we may
not have had an Information revolution! Consequently, there would have never been large, fast
MIS (Management Information Systems) systems, ATM systems, Airline Flight reservation
systems, maybe not even Internet as we know it. As one of my teachers very rightly said, "every
problem is an opportunity" therefore, the ability to store and manage data on diverse media (other
than magnetic tapes) opened up the way for a very different and more powerful type of
processing i.e. bringing the IT and the business user together as never before.
The advent of DASD
By 1970s, a new technology for the storage and access of data had had been introduced. The
1970s saw the advent of disk storage, or DASD (Direct Access Storage Device). Disk storage
was fundamentally different from magnetic tape storage in the sense that data could be accessed
directly on DASD i.e. non-sequentially. There was no need to go all the way through records 1,
2, 3, . . . k so as to reach the record k + 1. Once the address of record k + 1 was known, it was a
simple matter to go to record k + 1 directly. Furthermore, the time required to go to record k + 1
was significantly less than the time required to scan a magnetic tape. Actually it took
milliseconds to locate a record on a DASD i.e. orders of magnitude better performance than the
magnetic tape.
With DASD came a new type of system software known as a DBMS (Data Base Management
System). The purpose of the DBMS was to facilitate the programmer to store and access data on
DASD. In addition, the DBMS took care of such tasks as storing data on DASD, indexing data,
accessing it etc. With the winning combination of DASD and DBMS came a technological
solution to the problems of magnetic tape based master files. When we look back at the mess that
was created by master files and the mountains of redundant data aggregated on them, it is no
wonder that database is defined as a single source of data for all processing and a prelude to a
data warehouse i.e. "a single source of truth".
PC & 4GL
By the 1980s, more and new hardware/software, such as PCs and 4GLs (4th Generation
Languages) began to come out. The end user began to take up roles previously unimagined i.e.
directly controlling data and systems, outside the domain of the classical data center. With PCs
and 4GL technology the notion dawned that more could be done with data than just servicing
high-performance online transaction processing i.e. MIS (Management Information Systems)
could be developed to run individual database applications for managerial decis ion making i.e.
forefathers of today's DSS. Previously, data and IT were used exclusively to direct detailed
operational decisions. The combination of PC and 4GL introduced the notion of a new paradigm
i.e. a single database that could serve both operational high performance transaction processing
and (limited) DSS, analytical processing, all at the same time.
The extract program
Shortly after the advent of massive online high-performance transactions, an innocent looking
program called "extract" processing, began to show up.
The extract program was the simplest of all programs of its time. It scanned a file or database,
used some criteria for selection, and, upon finding qualified data, transported the data into
another file or database. Soon the extract program became very attractive, and flooded the
information processing environment.
The spider web
Figure 1.2 shows that a "spider web" of extract processing programs began to form. First, there
were extracts. Then there were extracts of extracts, then extracts of extracts of extracts, and it
went on. It was common for large companies to be doing tens of thousands of extracts per day.
This pattern of extract processing across the organization soon became a routine activity, and
even a name was coined for it. Extract processing gone out of control produced what was called
the "naturally evolving architecture". Such architectures occurred when an organization had a
relaxed approach to handling the whole process of hardware and software architecture. The larger
and more mature the organization; the worse was the problems of the naturally evolving
Taken jointly, the extract programs or naturally evolving systems formed a spider web, also
called "legacy systems" architecture.
Crisis of Credi bility
What is the financial health of my company?
Figure-1.3: Crisis of Credibility: Who is right?
Consider the CEO of an organization who is interested in the financial health of his company. He
asks the relevant departments to work on it and present the results. The organization is
maintaining different legacy systems, employs different extract programs and uses different
external data sources. As a consequence, Department -A which uses a different set of data sources,
external reports etc. as compared to Department-B (as shown in Figure -1.3) comes with a
different answer (say) sales up by 10%, as compared to the Department -B i.e. sales down by 10%.
Because Department-B used another set of operational systems, data bases and external data
sources. When CEO receives the two reports, he does not know what to do. CEO is faced with the
option of making decisions based on politics and personalities i.e. very subjective and non -
scientific. This is a typical example of the crisis in credibility in the naturally evolving
archit ecture. The question is which group is right? Going with either of the findings could spell
disaster, if the finding turns about to be incorrect. Hence the second important question, result of
which group is credible? This is very hard to judge, since neit her had malicious intensions but
both got a different view of the business using different sources.
Table of Contents:
  1. Need of Data Warehousing
  2. Why a DWH, Warehousing
  3. The Basic Concept of Data Warehousing
  4. Classical SDLC and DWH SDLC, CLDS, Online Transaction Processing
  5. Types of Data Warehouses: Financial, Telecommunication, Insurance, Human Resource
  6. Normalization: Anomalies, 1NF, 2NF, INSERT, UPDATE, DELETE
  7. De-Normalization: Balance between Normalization and De-Normalization
  8. DeNormalization Techniques: Splitting Tables, Horizontal splitting, Vertical Splitting, Pre-Joining Tables, Adding Redundant Columns, Derived Attributes
  9. Issues of De-Normalization: Storage, Performance, Maintenance, Ease-of-use
  10. Online Analytical Processing OLAP: DWH and OLAP, OLTP
  11. OLAP Implementations: MOLAP, ROLAP, HOLAP, DOLAP
  12. ROLAP: Relational Database, ROLAP cube, Issues
  13. Dimensional Modeling DM: ER modeling, The Paradox, ER vs. DM,
  14. Process of Dimensional Modeling: Four Step: Choose Business Process, Grain, Facts, Dimensions
  15. Issues of Dimensional Modeling: Additive vs Non-Additive facts, Classification of Aggregation Functions
  16. Extract Transform Load ETL: ETL Cycle, Processing, Data Extraction, Data Transformation
  17. Issues of ETL: Diversity in source systems and platforms
  18. Issues of ETL: legacy data, Web scrapping, data quality, ETL vs ELT
  19. ETL Detail: Data Cleansing: data scrubbing, Dirty Data, Lexical Errors, Irregularities, Integrity Constraint Violation, Duplication
  20. Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing
  21. Introduction to Data Quality Management: Intrinsic, Realistic, Orrs Laws of Data Quality, TQM
  22. DQM: Quantifying Data Quality: Free-of-error, Completeness, Consistency, Ratios
  23. Total DQM: TDQM in a DWH, Data Quality Management Process
  24. Need for Speed: Parallelism: Scalability, Terminology, Parallelization OLTP Vs DSS
  25. Need for Speed: Hardware Techniques: Data Parallelism Concept
  26. Conventional Indexing Techniques: Concept, Goals, Dense Index, Sparse Index
  27. Special Indexing Techniques: Inverted, Bit map, Cluster, Join indexes
  28. Join Techniques: Nested loop, Sort Merge, Hash based join
  29. Data mining (DM): Knowledge Discovery in Databases KDD
  31. Data Structures, types of Data Mining, Min-Max Distance, One-way, K-Means Clustering
  32. DWH Lifecycle: Data-Driven, Goal-Driven, User-Driven Methodologies
  33. DWH Implementation: Goal Driven Approach
  34. DWH Implementation: Goal Driven Approach
  35. DWH Life Cycle: Pitfalls, Mistakes, Tips
  36. Course Project
  37. Contents of Project Reports
  38. Case Study: Agri-Data Warehouse
  39. Web Warehousing: Drawbacks of traditional web sear ches, web search, Web traffic record: Log files
  40. Web Warehousing: Issues, Time-contiguous Log Entries, Transient Cookies, SSL, session ID Ping-pong, Persistent Cookies
  41. Data Transfer Service (DTS)
  42. Lab Data Set: Multi -Campus University
  43. Extracting Data Using Wizard
  44. Data Profiling