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Monday, 16 June 2014

Data WareHouse Interview Questions and Answers


What is data warehouse?
A data warehouse is a electronic storage of an Organization's historical data for the purpose of reporting, analysis and data mining or knowledge discovery.
Other than that a data warehouse can also be used for the purpose of data integration, master data management etc.
According to Bill Inmon, a datawarehouse should be subject-oriented, non-volatile, integrated and time-variant.
Explanatory Note
Note here, Non-volatile means that the data once loaded in the warehouse will not get deleted later. Time-variant means the data will change with respect to time.

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 including, 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?

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 prediction, forecasts, pattern recognition etc. Check this article to know more about data mining

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.
Explanatory Note:
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-of-stock 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?

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.
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.
If you want to read a quick and simple guide on dimensional modeling, please check our Guide to dimensional modeling.

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 are additive, semi-additive and non-additive measures?

Non-additive Measures

Non-additive measures are those which can not be 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.

Additive Measures

Additive measures can be used with any aggregation function like Sum(), Avg() etc. Example is Sales Quantity etc.
At this point, I will request you to pause and make some time to read this article on "Classifying data for successful modeling". This article helps you to understand the differences between dimensional data/ factual data etc. from a fundamental perspective

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.
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.
If you are not very familiar about Star Schema design or its use, we strongly recommend you read our excellent article on this subject - different schema in dimensional modeling

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 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)
You may also read, Modeling for various slowly changing dimension and Implementing Rapidly changing dimension to know more about SCD, RCD dimensions etc.

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. Read this article to gather in-depth knowledge on various SCD tables.

SCD-Slowly Changing Dimension

What is SCD?

SCD is Slowly Changing Dimension. As the name suggests, a dimension which changes slowly. For Example, say there is a table Employee, which stores information regarding employee as below:

BusinessEntityID, NationalIDNumber, First_Name, last_Name LoginID, OrganizationNode
OrganizationLevel, JobTitle, BirthDate, MaritalStatus, Gender, HireDate, SalariedFlag
CurrentFlag, ModifiedDate

In this Employee table, the data for an employee doesn't change very often, but yes we can’t say that the changes won’t be there. The changes, which may happen, are

·         Mistakenly spelling of First_Name is stored incorrect.
·         The employee gets married and marital status changes.
·         Last_Name changes.
·         The employee gets promotion and job designation changes and organization level changes.
·         The columns which doesn't change except if we assume that no mistake happens while data entry are HireDate, Gender, NationalIDNumber

The changes discussed don’t happen frequently, but may happen after certain time.

SCD supports four types of changes
changing attribute, historical attribute, fixed attribute, and inferred member.

Type 1 (changing attribute): When the changes in any attribute or column overwrites the existing records.

For example; as discussed first name of employee is misspelled and wrong spelling is stored in first name of that employee. For making the first name correct, we don’t need to add one more record for the same employee, so we can overwrite the first name. SCD which does this kind of changes comes into type 1 category. This SCD transformation directs these rows to an output named Changing Attributes Updates Output.

Emp ID
First Name
Last Name

This SCD transformation directs these rows to an output named Changing Attributes Updates Output.

Type 2 (historical attribute): when we need to maintain the history of records, whenever some particular column value changes.

For example the employee gets promotion, designation changes and organization level changes. In such case we need to maintain the history of the employee, that with which designation he joined, and when his designation and organizational level changes.

For these kinds of changes, there will be multiple records for the same employee with different designation. Then to indentify the current records, we can either add a column as current flag, which will be ‘y’ for the current or latest records, Or else we can add two column as start date and end date (expiry date), through which we can maintain history of employees records. This SCD directs these rows to two outputs: Historical Attribute Inserts Output and New Output.

Graduate Engineer
Analyst Programmer
Business Analyst

Fixed attribute: when the attribute must not change.

For example HireDate, Gender, NationalIDNumber should never change. So whenever changes will occur in these columns value then either it should throw error or the changes can be saved in some other destination. But changes should not be applied in the columns.

This SCD transformation detects changes and directs the rows with changes to an output named Fixed Attribute Output.

Inferred member:  are those records of the dimension, which are found missing during fact load. 

For example, say there is a fact table which contains employee and department information. While generating the fact table from employee_stg table and department_stg table , sometimes happenes that employee_stg contains some departments name which has no records in department table , and during fact table generation those records are found missing from department table, these kind of member of dimension department are called inferred member. It’s like ‘Fact arriving earlier than dimensions’.

 This SCD transformation directs these rows to an output named Inferred Member Updates. When data for the inferred member is loaded, you can update the existing record rather than create a new one.

What is rapidly changing dimension?

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 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.