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Data Warehouse Fundamentals-Part 1


Database- Database is nothing but collection of data.
Data warehouse is a RDBMS which is used for taking decision about profits, losses etc which contains previous and current data.


A Data Warehouse (DW) is simply a consolidation of data from a variety of sources that is designed to support strategic and tactical decision making.  Its main purpose is to provide a coherent picture of the business at a point in time.  


Business Intelligence refers to a set of methods and techniques that are used by organizations for tactical and strategic decision making. It leverages technologies that focus on counts, statistics and business objectives to improve business performance.


BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and predictive analytics. 

Example: We know KFC as example is operating in almost 100 plus countries. If KFC wants to know to analyze its profits and losses, it needs help of data warehousing.
By using data warehousing, It can come to conclusions like what is been sold more in particular region and based on sale, it can improve and attract customers by introducing different varieties of dishes, as instance if at some region, if students are major customers in a particular region, KFC can improve to attract students more and lessen their concentration on other items they prepare so that profit increases.

The process of Extracting data from different sources like oracle, SQL Server, DB2, IMS, etc and changing those inconsistent data to consistent format, is called ETL Process.














Staging Area:
The staging Area has four main steps:

1) Data Merging: We use join concepts and union operators to join different sources.
2) Data Cleansing: Removing unwanted data using trim ( ), Substring ( ) functions etc.
3) Data Scrubbing: Creating a new structure using old structure.  
Example: If salary column is present in a table, then creating annual salary column by writing queries like ASAL= SAL*12.
Or creating columns like FULL NAME=FIRST NAME+LAST NAME.
4) Data Aggregation: Using functions like SUM ( ), AVG ( ) Etc.

DATA MART: Data Mart is subset of Data Warehouse. It contains only one department information. It is also called as HPQS-High Performance Query Structure.




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Comments

  1. Arup Das3/01/2013 12:46 pm

    Nice Work dude...keep it up!!

    ReplyDelete
  2. Alfred Avina11/05/2019 11:01 am

    The supervising team of the best data warehouse consulting companies has great knowledge due to which I had chosen this blog. The experts program in this article helped me in transforming the large quantity of data efficiently. This blog helped me in assisting the topmost solutions of the proper design scoped and risk management, which get directly linked with the data transformation.

    ReplyDelete

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