Business Intelligence and Data Warehousing.

Business Intelligence and Data Warehousing :

Business Intelligence is a process and methodology to convert raw data in to meaningful information for business use.

     Business Intelligence Tools can be Categorized Into :
  • ETL Tools :
For Extracting Transforming and Loading the data to Data warehouse.
  • Reporting Tools :
Read data, process the data and format the data in to structured reports
and dashboards those are delivered to business users.
  • Data Mining Tools :
Data mining is commonly used for marketing, surveillance, and fraud detection.
  • Knowledge Management Tools :
          Knowledge management tools are used capture, develop, share, and effectively                                          using organizational knowledge


What is Data Warehousing ?

A data warehouse is a collection of corporate information, derived directly from operational systems and some external data sources. Its specific purpose is to support business decisions, not business operations.

Characteristics of a Data Warehouse:
  • Subject-oriented Data
 •        Collects all data  for a subject,  from different sources
       Read-only Requests
        Loaded during off-hours, read-only during day hours
  • Interactive Features, Ad-hoc query.
       Flexible design to handle spontaneous user queries.
       Pre-aggregated data
       To improve runtime performance
  •  Highly demoralized data structures
Data Mart :

         It’s a smallest level or Subset of the data warehouse and is specific to a business.
          Example : Sales Data Mart, Orders Data Mart, Finance Data Mart.

Staging Area  :
  •  Is an Intermediate storage area between the source and data warehouse.
  • It’s an area where the cleansing of  raw data takes place
  • It might be UNIX storage mount or relation staging table based on the requirement.
Sources :

It might any OLTP relational or file sources:
   Example : ERP, Mainframe, Oracle, Teradata , DB2.

Available BI Tools In market ?

ETL Tools 
  • Informatica
  • Abintio
  • Data Stage
  • Talend 
Reporting Tools.
  • Cognos
  • Business Objects (BOXI)
  • MicroStrategy
Data Bases and Operating Systems used for DW Design

Popular Relational Databases.

• Oracle
• Teradata
• Sybase
• Greenplum
• VectorWise

Popular OS Platforms.

• Red Hat Linux
• Solaris Unix
• Window 

Difference Between OLTP and OLAP

OLTP is for Business process or Business Operations.
OLAP is for Business Decisions and Analytics
OLTP involves short and fast inserts and updates initiated by end users.
Periodic long-running batch jobs refresh the data
Highly normalized with many tables
De-normalized with fewer tables
Clerical Users
Managerial/Business Users
Detailed Oriented Data
Subject Oriented Data
Entity Attribute Relationship Model
Dimensional Modeling
Contains Instant Data
Contains Historical Data

 Types of OLAP


·         Relational Online Analytical Processing.
·         ROLAP data will be stored in Standard Relational Database as a result it doesn’t need any pre computation.
·         ROLAP is scalable to handle large volumes of data.
                Ex : Business Object       

·         Multi Dimensional Online Analytical Processing.         
·         MOLAP is alternative to ROLAP
·         Store pre-computed data in an optimized multidimensional array storage we call it as  cube
·         Query performance will be very high due to the optimized storage.
                 Ex : Cognos.


·         Hybrid Online Analytical Processing.
·         It’s a combination of ROLAP and MOLAP.
·         Increased Aggregation and Query performance.
                Ex : Micro strategy.


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