2013
DATAWAREHOUSE [Type the document subtitle] [Type the abstract of the document here. The abstract is typically a short summary of the contents of the document. Type the abstract of the document here. The abstract is typically a short summary of the contents of the document.]
[Type the author name] [Type the company name] 2/6/2013
What is data warehouse?
A data warehouse is a electronic storage of an Organization's historical data for the purpose of analysis and reporting. According to Bill Inmon, a datawarehouse should be subject-oriented, non-volatile, integrated and time-variant. Explanation on the classic definition of data warehouse
Subject Oriented
This means a data warehouse has a defined scope and it only stores data under that scope. So for example, if the sales team of your company is creating a d ata warehouse - the data warehouse by definition is required to contain data related to sales (and not the data related to production management for example)
Non-volatile
This means that data once stored in the data warehouse are not removed or deleted from it and always stay there no matter what.
Integrated
This means that the data stored in a data wareho use make sense. Fact and figures are related t o each other and they are integrable and projects a single point of truth.
Time variant
This means that data is not constant, as new and new data gets loaded in the warehouse, data warehouse also grows in size.
What is the benefits of data warehouse?
A data warehouse helps to integrate data (see Data integration) and store them historically so that we can analyze different aspects of business incl uding, performance analysis, trend, prediction etc. over a given time frame and use the result of our analysis to improve the efficiency of business processes. Why Data Warehouse is used?
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For a long time in the past and also even today, Data warehouses are built to facilitate reporting on different key business processes of an organization, known as KPI. Data warehouses also help to integrate data from different sources and show a single-point-of-truth values about the business measures. Data warehouse can be further used for data mining which helps trend pred iction, forecasts, pattern recognition etc. What is the difference between OLTP and OLAP?
OLTP is the transaction system that collects business data. Whereas OLAP is the reporting and analysis system on that data. OLTP systems are optimized for INSERT, UPDATE operations and therefore highly normalized. On the other hand, OLAP systems are deli berately denormalized for fast data retrieval through SELECT operations.
In a departmental shop, when we pay the prices at the check-out counter, the sales person at the counter keys-in all the data into a "Point-Of-Sales" machine. That data is transaction data and the related system is a OLTP system. On the other hand, the manager of the store might want to view a report on out-ofstock materials, so that he can place purchase order for them. Such report will come out from OLAP system
What is data mart?
Data marts are generally designed for a single subject area. An organization may have data pertaining to different departments like Finance, HR, Marketting etc. stored in data warehouse and each department may have separate data marts. These data marts can be built on top of the data warehouse. What is ER model?
ER model or entity-relationship model is a particular methodology of data modeling wherein the goal of modeling is to normalize the data by reducing redundancy. This is different than dimensional modeling where the main goal is to improve the data retrieval mechanism. What is dimensional modeling?
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Dimensional model consists of dimension and fact tables. Fact tables store different transactional measurements and the foreign keys from dimension tables that qualifies the data. The goal of Dimensional model is
to achive high degree of normalization but to facilitate
easy and faster data retrieval. Ralph Kimball is one of the strongest proponents of this very popular data modeling technique which is often used in many enterprise level data warehouses. What is dimension?
A dimension is something that qualifies a quantity (measure). For an example, consider this: If I just say… “20kg”, it does no t mean anything. But if I say, "20kg of Rice (Product) is sold to Ramesh (customer) on 5th April (date)", then that gives a meaningful sense. These product, customer and dates are some dimension that qualified the measure - 20kg. Dimensions are mutually independent. Technically speaking, a di mension is a data element that categorizes each item in a data set into non-overlapping regions. What is Fact?
A fact is something that is quantifiable (Or measurable). Facts are typically (but not always) numerical values that can be aggregated. What are additive, semi-additive and non-additive measures?
Non-additive Measures
Non-additive measures are those which can not b e used inside any numeric aggregation function (e.g. SUM(), AVG() etc.). One example of non-additive fact is any kind of ratio or percentage. Example, 5% profit margin, revenue to asset ratio etc. A non -numerical data can also be a non-additive measure when that data is stored in fact tables, e.g. some kind of varchar flags in the fact table.
Semi Additive Measures
Semi-additive measures are those where only a subset of aggregation function can be applied . Let’s say account balance. A sum() function on balance does not give a useful result but max() or min() balance might be useful. Consider price rate or currency rate. Sum is meaningless on rate; however, average function might be useful.
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Additive Measures
Additive measures can be used with any aggregation function like Sum(), Avg() etc. Example is Sales Quantity etc. What is Star-schema?
This schema is used in data warehouse models w here one centralized fact table references number of dimension tables so as the keys (primary key) from all the dimension tables flow into the fact table (as foreign key) where measures are stored. This entity-relationship diagram looks like a star, hence the name.
Consider a fact table that stores sales quantity for each product and customer on a certain ti me. Sales quantity will be the measure here and keys from customer, product and time dimension tables will flow into the fact table. What is snow-flake schema?
This is another logical arrangement of tables in dimensional modeling where a centralized fact table references number of other dimension tables; however, those dimension tables are further normalized into multiple related tables. Consider a fact table that stores sales quantity for each product and customer on a certain ti me. Sales quantity will be the measure here and keys from customer, product and time dimension tables will flow into the fact table. Additionally all the products can be further grouped under different product families stored in a different table so that primary key of product family tables
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also goes into the product table as a foreign key. Such construct will be called a snow-flake schema as product table is further snow-flaked into product family.
Snow-flake increases degree of normalization in the design. What are the different types of dimension?
In a data warehouse model, dimension can be of following types, 1. Conformed Dimension 2. Junk Dimension 3. Degenerated Dimension 4. Role Playing Dimension Based on how frequently the data inside a dimension changes, we can further classify dimension as 1. Unchanging or static dimension (UCD) 2. Slowly changing dimension (SCD) 3. Rapidly changing Dimension (RCD) What is a 'Conformed Dimension'?
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A conformed dimension is the dimension that is shared across multiple subject area. Consider 'Customer' dimension. Both marketing and sales department may use the same customer dimension table in their reports. Similarly, a 'Time' or 'Date' dimension will be shared by different subject areas. These dimensions are conformed dimension. Theoretically, two dimensions which are either i dentical or strict mathematical subsets of one another are said to be conformed. What is degenerated dimension?
A degenerated dimension is a dimension that is derived from fact table and does not have its own dimension table. A dimension key, such as transaction number, re ceipt number, Invoice number etc. d oes not have any more associated attributes and hence can not be designed as a dimension table. What is junk dimension?
A junk dimension is a grouping of typically low-cardinality attributes (flags, indicators etc.) so that those can be removed from other tables and can be junked into an abstract dimension table. These junk dimension attributes might not be rel ated. The only purpose of this table is to store all the combinations of the di mensional attributes which you could not fit i nto the different dimension tables otherwise. Junk dimensions are o ften used to implement Rapidly Changing Dimensions in data warehouse. What is a role-playing dimension?
Dimensions are often reused for multiple applications within the same database with dif ferent contextual meaning. For instance, a "Date" dimension can be used for "Date of Sale", as well as "Date of Delivery", or "Date of Hire". This is often referred to as a 'role-playing dimension' What is SCD?
SCD stands for slowly changing dimension, i.e . the dimensions where data is slowly changing. These can be of many types, e.g. Type 0, Type 1, Type 2, Type 3 and Type 6, although Type 1, 2 and 3 are most common. Read this article to gather in-depth knowledge on various SCD tables. What is rapidly changing dimension?
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This is a dimension where data changes rapidly. Read this article to know how to implement RCD. Describe different types of slowly changing Dimension (SCD)
Type 0: A Type 0 dimension is where dimensional changes are not considered. This do es not mean that the attributes of the dimension do not change in actual business situation. It j ust means that, even if the value of the attributes change, history is not kept and the table holds all the previous data. Type 1: A type 1 dimension is where hi story is not maintained and the table always shows the recent data. This effectively means that such dimension table is always updated with recent data whenever there is a change, and b ecause of this update, we lose the previous values. Type 2: A type 2 dimension table tracks the historical changes by creating separate rows in the table with different surrogate keys. Consider there is a customer C1 under group G1 first and later on the customer is changed to group G2. Then there will be two separate records in dimension table like below, Key
Customer
Group
Start Date
End Date
1
C1
G1
1st Jan 2000
31st Dec 2005
2
C1
G2
1st Jan 2006
NULL
Note that separate surrogate keys are generated for the two records. NULL end date in the second row denotes that the record i s the current record. Also note that, instead of start and end dates, one could also keep version number column (1, 2 … etc.) to denote different versions of the record. Type 3: A type 3 dimension stored the history in a separate column instead of separate ro ws. So unlike a type 2 dimension which is vertically growing, a type 3 dimension is horizontally growing. See the example below, Key
Customer
Previous Group
Current Group
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1
C1
G1
G2
This is only good when you need not store many consecutive histories and when date of change is not required to be stored. Type 6: A type 6 dimension is a hybrid of type 1, 2 and 3 (1+2+3) which acts very similar to type 2, but only you add one extra column to denote which record is the current record. Key Customer Group Start Date
End Date
Current Flag
1
C1
G1
1st Jan 2000 31st Dec 2005 N
2
C1
G2
1st Jan 2006 NULL
Y
What is a mini dimension?
Mini dimensions can be used to handle rapidly changing dimension scenario. If a dimension has a huge number of rapidly changing attributes it is better to separate those attributes in different table called mini dimension. This is done because if the main dimension table is designed as SCD type 2, the table will soon outgrow in size and create performance issues. It is better to segregate the rapidly changing members in different table thereby keeping the main dimension table small and performing. What is a fact-less-fact?
A fact table that does not contain any measure is called a fact-less fact. This table will only contain keys from different dimension tables. This is often used to resolve a many-to-many cardinality issue.
Consider a school, where a single student may be taught by many teachers and a single teacher may have many students. To model this situation in dimensional model, one might introduce a fact-less-fact table joining teacher and student keys. Such a fact table will then be able to answer queries like, 1. Who are the students taught by a specific teacher. 2. Which teacher teaches maximum students. 3. Which student has highest number of teachers.etc. etc. What is a coverage fact?
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A fact-less-fact table can only answer 'optimistic' queries (positive query) but can not answer a negative query. Again consider the illustration in the above example. A fact-less fact containing the keys of tutors and students can not answer a qu ery like below, 1. Which teacher did 2. Which student was
teach any student? taught by any teacher?
Why not? Because fact-less fact table only stores the po sitive scenarios (like student being taught by a tutor) but if there i s a student who is
being taught by a teacher, then that
student's key does not appear in this table, thereby reducing the coverage of the table. Coverage fact table attempts to answer this - often by adding an extra flag column. Flag = 0 indicates a negative condition and flag = 1 indicates a positive condition. To understand this better, let's consider a class where there are 100 students and 5 teachers. So coverage fact table will ideally store 100 X 5 = 500 records (all combinations) and if a certain teacher is not teaching a certain student, the corresponding flag for that record will be 0. What are incident and snapshot facts
A fact table stores some kind of measurements. Usually these measurements are stored (or captured) against a specific time and these measurements vary wi th respect to time. Now it might so happen that the business might not able to capture all of its measures always for every point in time. Then those unavailable measurements can be kept empty (Null) or can be filled up with the last avail able measurements. The first case is the example of incident fact and the second one is the example of snapshot fact. What is aggregation and what is the benefit of aggregation?
A data warehouse usually captures data with same degree of details as available in source. The "degree of detail" is termed as granularity. But all reporting requirements from that data warehouse do not need the same degree of details. To understand this, let's consider an example from retail business. A certain retail chain has 500 shops accross Europe. All the shops record detail level transactions regarding the products they sale and those data are captured in a data warehouse. Each shop manager can access the data warehouse and they can see which products are sold by whom and in what quantity on any giv en date. Thus the data warehouse helps the shop managers with the detail level data that can be used for inventory management, trend prediction etc. Now think about the CEO of that retail chain. He does not really care about which certain sales girl in London sold the highest number of chopsticks or which shop is the best seller of 'brown 10
breads'. All he is interested i s, perhaps to check the percentage increase o f his revenue margin accross Europe. Or may be year to year sales growth on eastern Europe. Such data is aggregated in nature. Because Sales of goods in East Europe is derived by summing up the individual sales data from each shop in East Europe. Therefore, to support different levels of data warehouse users, data aggregation is needed. What is slicing-dicing?
Slicing means showing the slice of a data, given a certain set of dimension (e.g. Product) and value (e.g. Brown Bread) and measures (e.g. sales). Dicing means viewing the slice with respect to different dimensions and in different level of aggregations. Slicing and dicing operations are part of pivoting. What is drill-through?
Drill through is the process of going to the detail level data from summary data. Consider the above example on retail shops. If the CEO finds out that sales in East Europe has declined this year compared to la st year, he then might want to know the r oot cause of the decrease. For this, he may start drilling through his report to more detail level and eventually find out that even though individual shop sales has actually increased, the overall sales figure has decreased because a certain shop in Turkey h as stopped operating the business. The detail level of data, which CEO was not much interested on earlier, has this time helped him to pin point the root cause of declined sales. And the method he has followed to obtain the details from the aggregated data is called drill through. What is Normalization ? Why should we use it?
Normalization is a database design technique which organizes tables in a manner that reduces redundancy and dependency of data. It divides larger tables to smaller tables and link them using relationships. The inventor of the relational model Edgar Codd proposed the theory of normalization with the introduction of FirstNormal Form and he continued to extend theory with Second and Third Normal Form. Later he joined with Raymond F. Boyce to develop the theory of Boyce-Codd Normal Form.
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Theory of Normalization is still being developed further. For example there are discussions even on 6th Normal Form. . The evolution of Normalization theories is illustrated below-
Let’s learn Normalization with practical example -
Assume a video library maintains a database of movies rented out. Wi thout any normalization all information is stored in one table as shown below.
Table 1 Here you see Movies Rented column has multiple values. Now let’s move in to 1st Normal Form
1NF Rules
Each table cell should contain single value. Each record needs to be unique.
The above table in 1NF-
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Table 1 : In 1NF Form
Before we proceed lets understand a few things -What is a KEY ? A KEY is a value used to uniquely identify a record in a table. A KEY could be a single column or combination of multiple columns Note: Columns in a table that are NOT used to uniquely identify a record are called non-key columns.
What is a primary Key?
A primary is a single column values used to uniquely identify a database record. It has following attributes A primary key cannot be NULL A primary key value must be unique The primary key values can not be changed The primary key must be given a value when a new record is inserted.
What is a composite Key? A composite key is a primary key composed of multiple columns used to identify a record uniquely In our database , we have two people with the same name Robert Phil but they li ve at different places.
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Hence we require both Full Name and Address to uniquel y identify a record. This is a composite key. Let’s move into 2NF
2NF Rules Rule 1- Be in 1NF Rule 2- Single Column Primary Key
It is clear that we can’t move forward to make our simple database in 2 nd Normalization form unless we partition the table above.
Table 1
Table 2
We have divided our 1NF table into two tables viz. Table 1 and Table2. Table 1 contains member information. Table 2 contains i nformation on movies rented. We have introduced a new column called Membership_id which is the primary key for table 1. Records can be uniquely identified i n Table 1 using membership id
Introducing Foreign Key! In Table 2, Membership_ID is the foreign Key
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Foreign Key references primary key of another Table!It helps connect your Tables
A foreign key can have a different name from its primary key It ensures rows in one table have corresponding rows in another Unlike Primary key they do not have to be unique. Most often they aren’t Foreign keys can be null even though primary keys can not
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Why do you need a foreign key ? Suppose an idiot inserts a record in Table B such as You will only be able to insert values into your foreign key that exist in the unique key in the parent table. This helps in referential integrity.
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The above problem can be overcome by declaring membership id from Table2 as foreign key of membership id from Table1 Now , if somebody tries to insert a value in the membership id field that does not exist in the parent table , an error will be shown! What is a transitive functional dependencies? A transitive functional dependency is when changing a non-key column , might cause any of the other non-key columns to change Consider the table 1. Changing the non-key column Full Name , may change Salutation.
Let’s move ito 3NF
3NF Rules Rule 1- Be in 2NF Rule 2- Has no transitive functional dependencies To move our 2NF table into 3NF we again need to need divide our table.
TABLE 1
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Table 2
Table 3
We have again divided our tables and created a new table which stores Salutations. There are no transitive functional dependencies and hence our table is in 3NF In Table 3 Salutation ID is primary key and in Table 1 Salutation ID i s foreign to primary key in Table 3 Now our little example is in a level that cannot further be decomposed to attain higher forms of normalization. In fact it is already in higher normalization forms. Separate efforts for moving in to next levels of normalization are normally needed in complex databases. However we will be discussing about next levels of n ormalizations in brief in the following.
Boyce-Codd Normal Form (BCNF) Even when a database is in 3 rd Normal Form, still there would be anomalies resulted if i t has more than oneCandidate Key. Sometimes is BCNF is also referred as 3.5 Normal Form.
4th Normal Form If no database table instance contains two or more, independent and multivalued data describing the relevant entity , then it is in 4 th Normal Form. 5th Normal Form A table is in 5 th Normal Form only if it is in 4NF and it cannot be decomposed in to any number of smaller tables without loss of data. 6th Normal Form 6th Normal Form is not standardized yet however it is being discussed by database experts for some time. Hopefully we would have clear standardized definition for 6 th Normal Form in near future.
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