1
1. What is Data warehousing?
Explanation: A data warehouse can be considered as a storage area where interest specific or relevant data is stored irrespective of the source. What actually is required to create a data warehouse can be considered as Data Warehousing. Data warehousing merges data from multiple sources into an easy and complete form.

Directing to forums.. wait...!

2. What are fact tables and dimension tables?
Explanation: As mentioned, data in a warehouse comes from the transactions. Fact table in a data warehouse consists of facts and/or measures. The nature of data in a fact table is usually numerical.On the other hand, dimension table in a data warehouse contains fields used to describe the data in fact tables. A dimension table can provide additional and descriptive information (dimension) of the field of a fact table.

Directing to forums.. wait...!

3. What is ETL process in data warehousing?
Explanation: ETL is Extract Transform Load. It is a process of fetching data from different sources, converting the data into a consistent and clean form and load into the data warehouse. Different tools are available in the market to perform ETL jobs

Directing to forums.. wait...!

4. Explain the difference between data mining and data warehousing.
Explanation: Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse. Where as data mining aims to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc.E.g. a data warehouse of a company stores all the relevant information of projects and employees. Using Data mining, one can use this data to generate different reports like profits generated etc.

Directing to forums.. wait...!

5. What is an OLTP system and OLAP system?
Explanation: OLTP: Online Transaction and Processing helps and manages applications based on transactions involving high volume of data. Typical example of a transaction is commonly observed in Banks, Air tickets etc. Because OLTP uses client server architecture, it supports transactions to run cross a network. OLAP: Online analytical processing performs analysis of business data and provides the ability to perform complex calculations on usually low volumes of data. OLAP helps the user gain an insight on the data coming from different sources (multi dimensional).

Directing to forums.. wait...!

6. What are cubes?
Explanation: A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily.E.g. using a data cube A user may want to analyze weekly, monthly performance of an employee. Here, month and week could be considered as the dimensions of the cube.

Directing to forums.. wait...!

7. What is snow flake scheme design in database?
Explanation: A snowflake Schema in its simplest form is an arrangement of fact tables and dimension tables. The fact table is usually at the center surrounded by the dimension table. Normally in a snow flake schema the dimension tables are further broken down into more dimension table.E.g. Dimension tables include employee, projects and status. Status table can be further broken into status_weekly, status_monthly.

Directing to forums.. wait...!

8. What is analysis service?
Explanation: An integrated view of business data is provided by analysis service. This view is provided with the combination of OLAP and data mining functionality. Analysis Services allows the user to utilize a wide variety of data mining algorithms which allows the creation and designing data mining models.

Directing to forums.. wait...!

9. Explain sequence clustering algorithm
Explanation: Sequence clustering algorithm collects similar or related paths, sequences of data containing events.E.g. Sequence clustering algorithm may help finding the path to store a product of “similar” nature in a retail ware house.

Directing to forums.. wait...!

10. Explain discrete and continuous data in data mining.
Explanation: Finite data can be considered as discrete data. For example, employee id, phone number, gender, address etc.If data changes continually, then that data can be considered as continuous data. For example, age, salary, experience in years etc.

Directing to forums.. wait...!