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Data Warehousing

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Lecture Handout
Data Warehousing
Lecture No. 03
It is a blend of many technologies, the basic concept being:
Take all data from different operational systems.
If necessary, add relevant data from industry.
Transform all data and bring into a uniform format.
Integrate all data as a single entity.
Store data in a format supporting easy access for decision support.
Create performance enhancing indices.
Implement performance enhancement joins.
Run ad-hoc queries with low selectivity.
A Data Warehouse is not something shrink-wrapped i.e. you take a set of CDs and install into a
box and soon you have a Data Warehouse up and running. A Data Warehouse evolves over time,
you don't buy it. Basically it is about taking/collecting data from different heterogeneous sources.
Heterogeneous means not only the operating system is different but so is the underlying file
format, different databases, and even with same database systems different representations for the
same entity. This could be anything from different columns names to different data types for the
same entity.
Companies collect and record their own operational data, but at the same time they also use
reference data obtained from external sources such as codes, prices etc. This is not the only
external data, but customer lists with their contact information are also obtained from external
sources. Therefore, all this external data is also added to the data warehouse.
As mentioned earlier, even the data collected and obtained from within the company is not
standard for a host of different reasons. For example, different operational systems being used in
the company were developed by different vendors over a period of time, and there is no or
minimal evenness in data representation etc. When that is the state of affairs (and is normal)
within a company, then there is no control on the quality of data obtained from external sources.
Hence all the data has to be transformed into a uniform format, standardized and integrated before
it c an go into the data warehouse.
In a decision support environment, the end user i.e. the decision maker is interested in the big
picture. Typical DSS queries do not involve using a primary key or asking questions about a
particular customer or account. DSS queries deal with number of variables spanning across
number of tables (i.e. join operations) and looking at lots of historical data. As a result large
number of records are processed and retrieved. For such a case, specialized or different database
architectures/topologies are required, such as the star schema. We will cover this in detail in the
relevant lecture.
Recall that a B-Tree is a data structure that supports dictionary operations. In the context of a
database, a B-Tree is used as an index that provides access to records without actually scanning
the entire table. However, for very large databases the corresponding B Trees becomes very
large. Typically the node of a B-Tree is stored in a memory block, and traversing a B-Tree
involves O(log n) page faults. This is highly undesirable, because by default the height of the B -
Tree would be very large for very large data bases. Therefore, new and unique indexing
techniques are required in the DWH or DSS environment, such as bitmapped indexes or cluster
index etc. In some cases the designers want such powerful indexing techniques, that the queries
are satisfied from the indexes without going to the fact tables.
In typical OLTP environments, the size of tables are relatively small, and the rows of interes t are
also very small, as the queries are confined to specifics. Hence traditional joins such as nested -
loop join of quadratic time complexity does not hurt the performance i.e. time to get the answer.
However, for very large databases when the table sizes are in millions and the rows of interest are
also in hundreds of thousands, nested-loop join becomes a bottle neck and is hardly used.
Therefore, new and unique forms of joins are required such as sort -merge join or hash based join
Run Ad-Hoc queries with low Selectivity
Have already explained what is meant by ad -hoc queries. A little bit about selectivity is in order.
Selectivity is the ratio between the number of unique values in a column divided by the total
number of values in that column. For example the selectivity of the gender column is 50%,
assuming gender of all customers is known. If there are N records in a table, then the selectivity
of the primary key column is 1/N. Note that a query consists of retrieving records based on a a
combination of different columns, hence the choice of columns determine the selectivity of the
query i.e. the number of records retrieved divided by the total number of records present in the
In an OLTP (On-Line Transaction Processing) or MIS (Management Information System)
environment, the queries are typically Primary Key (PK) based, hence the number of records
retrieved is not more than a hundred rows. Hence the selectivity is very high. For a Data
Warehouse (DWH) environment, we are interested in the big picture and have queries that are not
very specific in nature and hardly use a PK. As a result hundreds of thousands of records (or
rows) are retrieved from very large tables. Thus the ratio of records retrieved to the total number
of records present is high, and hence the selectivity is low.
How is it different?
Decision making is Ad-Hoc
Figure-3.1: Running in circles
Consider a decision maker or a business user who wants some of his questions to be answered.
He/she sets a meeting with the IT people, and explains the requirements. The IT people go over
the cycle of system analysis and design, that takes anywhere from couple of weeks to couple of
months and they finally design and develop the system. Happy and proud with their achievement
the IT people go to the business user with the reporting system or MIS system. After a learning
curve the business users spends some time with the brand new system, and may get some answers
to the required questions. But then those answers results in more questions. The business user has
no choice to meet the IT people with a new set of requirements. The business user is frustrated
that his questions are not getting answered, while the IT people are frustrated that the business
user always changes the requirements. Both are correct in their frustration.
Different patterns of hardware utilization
Figure-3.2: Different patterns of CPU Usage
Although there are peaks and valleys in the operational processing, but ultimately there is
relativ ely static pattern of utilization. There is an essentially different pattern of hardware
utilization in the data warehouse environment i.e. a binary pattern of utilization, either the
hardware is utilized fully or not at all. Calculating a mean utilization for a DWH is not a
meaningful activity. Therefore, trying to mix the two environments is a recipe for disaster. You
can optimize the machine for the performance of one type of application, not for both.
Bus vs. Train Analogy
Consider the analogy of a bus and train. I believe you can find dozens of buses operating between
Lahore and Rawalpindi almost every 30 minutes. As a consequence, literally there are buses
moving between Lahore and Rawalpindi almost continuously through out the day. But how many
times a dedicated train moves between the two cities? Only twice a day and carries a bulk of
passengers and cargo. Binary operation i.e. either traveling or not. The train can NOT be
optimized for every 30-min travel, it will never fill to capacity and run into loss. A bus can not be
optimized to travel only twice, it will stand idle and passengers would take vans etc. Bottom line:
Two modes of transportation, can not be interchanged.
Combines historical & Operational Data
Don't do data entry into a DWH, OLTP or ERP are the source systems.
OLTP systems don't keep history, cant get balance statement more than a year old.
DWH keep historical data, even of bygone customers. Why?
In the context of bank, want to know why the customer left?
What were the events that led to his/her leaving? Why?
Customer retention.
Why keep historical data?
The data warehouse is different because, again it's not a database you do data entry. You are
actually collecting data from the operational systems and loading into the DWH. So the
transactional processing systems like the ERP system are the source systems for the data
warehouse. You feed the data into the data warehouse. And the data warehouse typically collects
data over a period of time. So if you look at your transactional processing OLTP systems,
normally such systems don't keep very much history. Normally if a customer leaves or expired,
the OLTP systems typically purge the data associated with that customer and all the transactions
off the database after some amo unt of time. So normally once a year most business will purge the
database of all the old customers and old transactions. In the data warehouse we save the
historical data. Because you don't need historical data to do business today, but you do need the
historical data to understand patterns of business usage to do business tomorrow, such why a
customer left?
How much History?
Depends on:
Cost of storing historical data.
Economic value of historical data.
Industries and history
§  Telecomm calls are much much more as compared to bank transactions - 18
months of historical data.
Retailers interested in analyzing yearly seasonal patterns - 65 weeks of historical
Insurance companies want to do actuary analysis, use the historical data in order
to predict risk- 7 years of historical data.
Hence, a DWH NOT a complete repository of data
How back do you look historically? It really depends a lot on the industry. Typically it's an
economic equation. How far back depends on how much dose it cost to store that extra years
work of data and what is it's economic value? So for example in financial organizations, they
typically store at least 3 years of data going backward. Again it's typical. It's not a hard and fast
In a telecommunications company, for example, typically around 18 months of data is stored.
Because there are a lot more call details records then there are deposits and withdrawals from a
bank account so the storage period is less, as one can not afford to store as much of it typica lly.
Another important point is, the further back in history you store the data, the less value it has
normally. Most of the times, most of the access into the data is within that last 3 months to 6
months. That's the most predictive data.
In retail business, retailers typically store at least 65 weeks of data. Why do they do that? Because
they want to be able to look at this season's selling history to last season's selling history. For
example, if it is Eid buying season, I want to look at the transit-buying this Eid and compare it
with the year ago. Which means I need 65 weeks in order to get year going back, actually more
then a year. It's a year and a season. So 13 weeks are additionally added to do the analysis. So it
really depends a lot on the industry. But normally you expect at least 13 months.
Economic value of data
Storage cost
Data Warehouse a
complete repository of data?
This raises an interesting question, do we decide about storage of historical data using only time,
or consider space also, or both?
Usually (but not always) periodic or batch updates rather than real-time.
The boundary is blurring for active data warehousing.
For an ATM, if update not in real-time, then lot of real trouble.
DWH is for strategic decision making based on historical data. Wont hurt if transactions
of last one hour/day are absent.
Rate of update depends on:
§  Volume of data,
§  Nature of business,
§  Cost of keeping historical data,
§  Benefit of keeping historical data.
It's also true that in the traditional data warehouse the data acquisition is done on periodic or
batch based, rather then in real time. So think again about ATM system, when I put my ATM
card and make a withdrawal, the transactions are happening in real time, because if they don't the
bank can get into trouble. Someone can withdraw more money then they had in their account!
Obviously that is not acceptable. So in an online transaction processing (OLTP) system, the
records are updated, deleted and inserted in real-time as the business events take place, as the data
entry takes place, as the point of sales system at a super market captures the sales data and inserts
into the database.
In a traditional data warehouse that is not true. Because the traditional data warehouse is for
strategic decision-making not for running day to day business. And for strategic decision making,
I don't need to know the last hours worth of ATM deposits. Because strategic decisions take the
long term perspective. For this reason, and for efficiency reasons normally what happens is that in
the data warehouse you update on some predefined schedule basis. May be it's once a month,
maybe it's once a weak, maybe it's even once every night. It depends on the volume of data you
are working with, and how important the timings of the data are and so on.
Deviation from the PURIST approach
Let me first explain what/who a purist is. A purist is an idealist or traditionalist who wants
everything to be done by the book or the old arcane ways (only he/she knows), in short he/she is
not pragmatic or realist. Because the purist wants everything perfect, so he/she has good excuses
of doing nothing, as it is not a perfect world. When automobiles were first invented, it was the
purists who said that the automobiles will fa il, as they scare the horses. As Iqbal very rightly said
"Aina no sa durna Tarzay Kuhan Pay Arna..."
As data warehouses become mainstream and the corresponding technology also becomes
mainstream technology, some traditional attributes are being deviated in order to meet the
increasing demands of the user's. We have already discussed and reconciled with the fact that a
data warehouse is NOT the complete repository of data. The other most noticeable deviations
being time variance and nonvolatility.
Deviation from Time Variance and Nonvolatility
As the size of data warehouse grows over time (e.g., in terabytes), reloading and appending data
can become a very tedious and time consuming task. Furthermore, as business users get the "hang
of it" they start demanding that more up -to-date data be available in the data warehouse.
Therefore, instead of sticking to the traditional data warehouse characteristic of keeping the data
nonvolatile and time variant, new data is being added to the data warehouse on a daily basis, if
not on a real -time basis and at the same time historical data removed to make room for the "fresh"
data. Thus, new approaches are being made to tackle this task. Two possible methods are as
Perform hourly/daily batch updates from shadow tab les or log files. Transformation rules
are executed during the loading process. Thus, when the data reaches the target data
warehouse database, it is already transformed, cleansed and summarized.
Perform real-time updates from shadow tables or log files. Again, transformation rules are
executed during the loading process. Instead of batch updates, this takes place on a per
transaction basis that meets certain business selection criteria.
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, Orr’s 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