MySql Interview Questions and Answers – Part 8
71 Can you explain the SELECT INTO Statement?
SELECT INTO statement is used mostly to create backups. The below SQL backsup the Employee table in to the EmployeeBackUp table. One point to be noted is that the structure of pcdsEmployeeBackup and pcdsEmployee table should be same. SELECT * INTO pcdsEmployeeBackup FROM pcdsEmployee
72 What is a View?
View is a virtual table which is created on the basis of the result set returned by the select statement.
CREATE VIEW [MyView] AS SELECT * from pcdsEmployee where LastName = ‘singh’
In order to query the view
SELECT * FROM [MyView]
73 What is SQl injection ?
It is a Form of attack on a database-driven Web site in which the attacker executes unauthorized SQL commands by taking advantage of insecure code on a system connected to the Internet, bypassing the firewall. SQL injection attacks are used to steal information from a database from which the data would normally not be available and/or to gain access to an organization’s host computers through the computer that is hosting the database.
SQL injection attacks typically are easy to avoid by ensuring that a system has strong input validation.
As name suggest we inject SQL which can be relatively dangerous for the database. Example this is a simple SQL
SELECT email, passwd, login_id, full_name
FROM members WHERE email = ‘x’
Now somebody does not put “x” as the input but puts “x ; DROP TABLE members;”.
So the actual SQL which will execute is :-
SELECT email, passwd, login_id, full_name FROM members WHERE email = ‘x’ ; DROP TABLE members;
Think what will happen to your database.
74 What is Data Warehousing ?
Data Warehousing is a process in which the data is stored and accessed from central location and is meant to support some strategic decisions. Data Warehousing is not a requirement for Data mining. But just makes your Data mining process more efficient.
Data warehouse is a collection of integrated, subject-oriented databases designed to support the decision-support functions (DSF), where each unit of data is relevant to some moment in time.
75 What are Data Marts?
Data Marts are smaller section of Data Warehouses. They help data warehouses collect data. For example your company has lot of branches which are spanned across the globe. Head-office of the company decides to collect data from all these branches for anticipating market. So to achieve this IT department can setup data mart in all branch offices and a central data warehouse where all data will finally reside.
76 What are Fact tables and Dimension Tables ? What is Dimensional Modeling and Star Schema Design
When we design transactional database we always think in terms of normalizing design to its least form. But when it comes to designing for Data warehouse we think more in terms of denormalizing the database. Data warehousing databases are designed using Dimensional Modeling. Dimensional Modeling uses the existing relational database structure and builds on that.
There are two basic tables in dimensional modeling:-
Fact tables are central tables in data warehousing. Fact tables have the actual aggregate values which will be needed in a business process. While dimension tables revolve around fact tables. They describe the attributes of the fact tables.
77 What is Snow Flake Schema design in database? What’s the difference between Star and Snow flake schema?
Star schema is good when you do not have big tables in data warehousing. But when tables start becoming really huge it is better to denormalize. When you denormalize star schema it is nothing but snow flake design. For instance below customeraddress table is been normalized and is a child table of Customer table. Same holds true for Salesperson table.
78 What is ETL process in Data warehousing? What are the different stages in “Data warehousing”?
ETL (Extraction, Transformation and Loading) are different stages in Data warehousing. Like when we do software development we follow different stages like requirement gathering, designing, coding and testing. In the similar fashion we have for data warehousing.
In this process we extract data from the source. In actual scenarios data source can be in many forms EXCEL, ACCESS, Delimited text, CSV (Comma Separated Files) etc. So extraction process handle’s the complexity of understanding the data source and loading it in a structure of data warehouse.
This process can also be called as cleaning up process. It’s not necessary that after the extraction process data is clean and valid. For instance all the financial figures have NULL values but you want it to be ZERO for better analysis. So you can have some kind of stored procedure which runs through all extracted records and sets the value to zero.
After transformation you are ready to load the information in to your final data warehouse database.
79 What is Data mining ?
Data mining is a concept by which we can analyze the current data from different perspectives and summarize the information in more useful manner. It’s mostly used either to derive some valuable information from the existing data or to predict sales to increase customer market.
There are two basic aims of Data mining:-
From the given data we can focus on how the customer or market will perform. For instance we are having a sale of 40000 $ per month in India, if the same product is to be sold with a discount how much sales can the company expect.
To derive important information to analyze the current business scenario. For example a weekly sales report will give a picture to the top management how we are performing on a weekly basis?
80 Compare Data mining and Data Warehousing ?
“Data Warehousing” is technical process where we are making our data centralized while “Data mining” is more of business activity which will analyze how good your business is doing or predict how it will do in the future coming times using the current data. As said before “Data Warehousing” is not a need for “Data mining”. It’s good if you are doing “Data mining” on a “Data Warehouse” rather than on an actual production database. “Data Warehousing” is essential when we want to consolidate data from different sources, so it’s like a cleaner and matured data which sits in between the various data sources and brings then in to one format. “Data Warehouses” are normally physical entities which are meant to improve accuracy of “Data mining” process. For example you have 10 companies sending data in different format, so you create one physical database for consolidating all the data from different company sources, while “Data mining” can be a physical model or logical model. You can create a database in “Data mining” which gives you reports of net sales for this year for all companies. This need not be a physical database as such but a simple query.