Counter of Festivals

Ashok Blog for SQL Learners and Beginners and Experts

Monday, 7 January 2013

Data Warehousing Q & A

             DatawareHousing Questions and Answers

what is datawarehouse?
A data warehouse is a electronic storage of an Organization's historical data for the purpose of analysis and reporting
a datawarehouse should be subject-oriented, non-volatile, integrated and time-variant.

What is ETL(Extract Transform Load)?

ETL is the abbreviation f extract, transform, load. ETL  is  software that enables businesses to consolidate their deisparate data while moving it from place to place, and it doesn’t really matter that that data is in different forms or formats. The data can come from any source. 

First  the Extract functions reads data from a specified source database and extracts a desired subset of data. Next, the  transform function works with the acquired data using rules or lookup tables or creating combinations with other data to convert it to the desired state. Finally, the load function is ued to write the resulting data to a target database.

what is fact table?
A fact table is the central table in a star schema of a data warehouse. A fact table stores quantitative information for analysis

A fact table works with dimension tables. A fact table holds the data to be analyzed, and a dimension table stores data about the ways in which the data in the fact table can be analyzed. Thus, the fact table consists of two types of columns. The foreign keys column allows joins with dimension tables, and the measures columns contain the data that is being analyzed.
Suppose that a company sells products to customers. Every sale is a fact that happens, and the fact table is used to record these facts. For example:

Time ID Product ID Customer ID Unit Sold
4 17 2 1
8 21 3 2
8 4 1 1

 Now we can add a dimension table about customers:

Customer ID
1 Brian Edge M 2 3 4
2 Fred Smith M 3 5 1
3 Sally Jones F 1 7 3

In this example, the customer ID column in the fact table is the foreign key that joins with the dimension table. By following the links, you can see that row 2 of the fact table records the fact that customer 3, Sally Jones, bought two items on day 8. The company would also have a product table and a time table to determine what Sally bought and exactly when.
When building fact tables, there are physical and data limits. The ultimate size of the object as well as access paths should be considered. Adding indexes can help with both. However, from a logical design perspective, there should be no restrictions. Tables should be built based on current and future requirements, ensuring that there is as much flexibility as possible built into the design to allow for future enhancements without having to rebuild the data.

what is dimension table?
A dimension table is a table in a star schema of a data warehouse. A dimension table stores attributes, or dimensions, that describe the objects in a fact table.

In data warehousing, a dimension is a collection of reference information about a measurable event. These events are known as facts and are stored in a fact table. Dimensions categorize and describe data warehouse facts and measures in ways that support meaningful answers to business questions.  They form the very core of dimensional modeling.

A data warehouse organizes descriptive attributes as columns in dimension tables.  For example, a customer dimension’s attributes could include first and last name, birth date, gender, etc., or a website dimension would include site name and URL attributes.

A dimension table has a primary key column that uniquely identifies each dimension record (row).  The dimension table is associated with a fact table using this key.  Data in the fact table can be filtered and grouped (“sliced and diced”) by various combinations of attributes.  For example, a Login fact with Customer, Website, and Date dimensions can be queried for “number of males age 19-25 who logged in to more than once during the last week of September 2010, grouped by day.”
Dimension tables are referenced by fact tables using keys. When creating a dimension table in a data warehouse, a system-generated key is used to uniquely identify a row in the dimension. This key is also known as a surrogate key. The surrogate key is used as the primary key in the dimension table. The surrogate key is placed in the fact table and a foreign key is defined between the two tables. When the data is joined, it does so just as any other join within the database.

Like fact tables, dimension tables are often highly de-normalized, because these structures are not built to manage transactions they are built to enable users to analyze data as easily as possible.

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 deliberately denormalized for fast data retrieval through SELECT operations.

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?

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 not to achive high degree of normalization but to facilitate easy and faster data retrieval.

What is dimension?

A dimension is something that qualifies a quantity (measure).
For an example, consider this: If I just say… “20kg”, it does not 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 dimension 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 is Star-schema?

This schema is used in data warehouse models where 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.
Star-Schema Consider a fact table that stores sales quantity for each product and customer on a certain time. 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 time. 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 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 is surrogate key ? Where we use it explain with examples?

Surrogate key is a unique identification key, it is like an artificial or alternative key to production key, bz the production key may be alphanumeric or composite key but the surrogate key is always single numeric key. Assume the production key is an alphanumeric field if you create an index for this fields it will occupy more space, so it is not advisable to join/index, bz generally all the datawarehousing fact table are having historical data. These factable are linked with so many dimension table. if it's a numerical fields the performance is high


Surrogate key is a substitution for the natural primary key.
It is just a unique identifier or number for each row that can be used for the primary key to the table. The only requirement for a surrogate primary key is that it is unique for each row in the table.
Data warehouses typically use a surrogate, (also known as artificial or identity key), key for the dimension tables primary keys. They can use Infa sequence generator, or Oracle sequence, or SQL Server Identity values for the surrogate key.
It is useful because the natural primary key (i.e. Customer Number in Customer table) can change and this makes updates more difficult.
Some tables have columns such as AIRPORT_NAME or CITY_NAME which are stated as the primary keys (according to the business users) but, not only can these change, indexing on a numerical value is probably better and you could consider creating a surrogate key called, say, AIRPORT_ID. This would be internal to the system and as far as the client is concerned you may display only the AIRPORT_NAME.
Another benefit you can get from surrogate keys (SID) is :
Tracking the SCD - Slowly Changing Dimension.
Let me give you a simple, classical example:
On the 1st of January 2002, Employee 'E1' belongs to Business Unit 'BU1' (that's what would be in your Employee Dimension). This employee has a turnover allocated to him on the Business Unit 'BU1' But on the 2nd of June the Employee 'E1' is muted from Business Unit 'BU1' to Business Unit 'BU2.' The entire new turnovers have to belong to the new Business Unit 'BU2' but the old one should belong to the Business Unit 'BU1.'
If you used the natural business key 'E1' for your employee within your datawarehouse everything would be allocated to Business Unit 'BU2' even what actually belongs to 'BU1.'
If you use surrogate keys, you could create on the 2nd of June a new record for the Employee 'E1' in your Employee Dimension with a new surrogate key.
This way, in your fact table, you have your old data (before 2nd of June) with the SID of the Employee 'E1' + 'BU1.' All new data (after 2nd of June) would take the SID of the employee 'E1' + 'BU2.'
You could consider Slowly Changing Dimension as an enlargement of your natural key: natural key of the Employee was Employee Code 'E1' but for you it becomes
Employee Code + Business Unit - 'E1' + 'BU1' or 'E1' + 'BU2.' But the difference with the natural key enlargement process, is that you might not have all part of your new key within your fact table, so you might not be able to do the join on the new enlarge key -> so you need another id.

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'?

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 identical 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, receipt number, Invoice number etc. does 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 related. The only purpose of this table is to store all the combinations of the dimensional attributes which you could not fit into the different dimension tables otherwise. Junk dimensions are often 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 different 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.

What is rapidly changing dimension?

This is a dimension where data changes rapidly.

Describe different types of slowly changing Dimension (SCD)

Type 0:
A Type 0 dimension is where dimensional changes are not considered. This does not mean that the attributes of the dimension do not change in actual business situation. It just 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 history 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 because 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,
KeyCustomerGroupStart DateEnd Date
1C1G11st Jan 200031st Dec 2005
2C1G21st Jan 2006NULL
Note that separate surrogate keys are generated for the two records. NULL end date in the second row denotes that the record is 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 rows. So unlike a type 2 dimension which is vertically growing, a type 3 dimension is horizontally growing. See the example below,
KeyCustomerPrevious GroupCurrent Group
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.
KeyCustomerGroupStart DateEnd DateCurrent Flag
1C1G11st Jan 200031st Dec 2005N
2C1G21st Jan 2006NULLY

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.
Explanatory Note:
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?

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 query like below,
  1. Which teacher did not teach any student?
  2. Which student was not taught by any teacher?
Why not? Because fact-less fact table only stores the positive scenarios (like student being taught by a tutor) but if there is a student who is not 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 with 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 available 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 given 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 breads'. All he is interested is, perhaps to check the percentage increase of 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 last year, he then might want to know the root 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 has 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. 

Data Warehousing Q & A

Data scrubbing is which of the following?
A process to reject data from the data warehouse and to create the necessary indexes
A process to load the data in the data warehouse and to create the necessary indexes
A process to upgrade the quality of data after it is moved into a data warehouse
A process to upgrade the quality of data before it is moved into a data warehouse

The @active data warehouse architecture includes which of the following?
At least one data mart
Data that can extracted from numerous internal and external sources
Near real-time updates
All of the above.

A goal of data mining includes which of the following?
To explain some observed event or condition
To confirm that data exists
To analyze data for expected relationships
To create a new data warehouse

An operational system is which of the following?
A system that is used to run the business in real time and is based on historical data.
A system that is used to run the business in real time and is based on current data.
A system that is used to support decision making and is based on current data.
A system that is used to support decision making and is based on historical data.

A data warehouse is which of the following?
Can be updated by end users.
Contains numerous naming conventions and formats.
Organized around important subject areas.
Contains only current data.

A snowflake schema is which of the following types of tables?
All of the above

The generic two-level data warehouse architecture includes which of the following?
At least one data mart
Data that can extracted from numerous internal and external sources
Near real-time updates
All of the above.

Fact tables are which of the following?
Completely denoralized
Partially denoralized
Completely normalized
Partially normalized

Data transformation includes which of the following?
A process to change data from a detailed level to a summary level
A process to change data from a summary level to a detailed level
Joining data from one source into various sources of data
Separating data from one source into various sources of data

Reconciled data is which of the following?
Data stored in the various operational systems throughout the organization.
Current data intended to be the single source for all decision support systems.
Data stored in one operational system in the organization.
Data that has been selected and formatted for end-user support applications.
The load and index is which of the following?
A process to reject data from the data warehouse and to create the necessary indexes
A process to load the data in the data warehouse and to create the necessary indexes
A process to upgrade the quality of data after it is moved into a data warehouse
A process to upgrade the quality of data before it is moved into a data warehouse

The extract process is which of the following?
Capturing all of the data contained in various operational systems
Capturing a subset of the data contained in various operational systems
Capturing all of the data contained in various decision support systems
Capturing a subset of the data contained in various decision support systems

A star schema has what type of relationship between a dimension and fact table?
All of the above.

Transient data is which of the following?
Data in which changes to existing records cause the previous version of the records to be eliminated
Data in which changes to existing records do not cause the previous version of the records to be eliminated
Data that are never altered or deleted once they have been added
Data that are never deleted once they have been added

A multifield transformation does which of the following?

A)Converts data from one field into multiple fields

B)Converts data from multiple fields into one field

C)Converts data from multiple fields into multiple fields 

D):All of the Above


A data mart is designed to optimize the performance for well-defined and predicable uses.

Successful data warehousing requires that a formal program in total quality management (TQM) be implemented.

Data in operational systems are typically fragmented and inconsistent.

Most operational systems are based on the use of transient data.

Independent data marts are often created because an organization focuses on a series of short-term business objectives.

Joining is the process of partitioning data according to predefined criteria.

The role of the ETL process is to identify erroneous data and to fix them.

Data in the data warehouse are loaded and refreshed from operational systems.
Star schema is suited to online transaction processing, and therefore is generally used in operational systems, operational data stores, or an EDW.

Periodic data are data that are physically altered once added to the store.
Both status data and event data can be stored in a database.

Static extract is used for ongoing warehouse maintenance.
Data scrubbing can help upgrade data quality; it is not a long-term solution to the data quality problem.

Every key used to join the fact table with a dimensional table should be a surrogate key.

Derived data are detailed, current data intended to be the single, authoritative source for all decision support applications.