1.What is data warehouse?
Ans: A data warehouse is a electronic storage of an Organization’s historical data for the purpose of Data Analytics, such as reporting, analysis and other knowledge discovery activities.
Other than Data Analytics, 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.
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.
2.What is meant by Data Analytics?
Ans: Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. A data warehouse is often built to enable Data Analytics
3.What are the benefits of data warehouse?
Ans: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.
4.Why Data Warehouse is used?
Ans: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. Today we often call this whole process of reporting data from data warehouses as “Data Analytics”. Data warehouses also help to integrate data from different sources and show a single-point-of-truth values about the business measures (e.g. enabling Master Data Management).Data warehouse can be further used for data mining which helps trend prediction, forecasts, pattern recognition etc.
5.What is the difference between OLTP and OLAP?
Ans: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.
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.
6.What is data mart?
Ans:Data marts are generally designed for a single subject area. An organization may have data pertaining to different departments like Finance, HR, Marketing 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.
7.What is ER model?
Ans: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.
8.What is dimensional modeling?
Ans: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 achieve 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.
9. What is dimension?
Ans: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.
10.What is Fact?
Ans:A fact is something that is quantifiable (Or measurable). Facts are typically (but not always) numerical values that can be aggregated.