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)
  • OBIEE
  • MicroStrategy
Data Bases and Operating Systems used for DW Design

Popular Relational Databases.

• Oracle
• IBM DB2
• Teradata
• Sybase
• MySQL
• Greenplum
• VectorWise

Popular OS Platforms.

• Red Hat Linux
• Solaris Unix
• Window 

Difference Between OLTP and OLAP

OLTP
OLAP
1
OLTP is for Business process or Business Operations.
OLAP is for Business Decisions and Analytics
2
OLTP involves short and fast inserts and updates initiated by end users.
Periodic long-running batch jobs refresh the data
3
Highly normalized with many tables
De-normalized with fewer tables
4
Clerical Users
Managerial/Business Users
5
Detailed Oriented Data
Subject Oriented Data
6
Entity Attribute Relationship Model
Dimensional Modeling
7
Contains Instant Data
Contains Historical Data

 Types of OLAP

ROLAP :

·         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       
               
MOLAP :

·         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.

HOLAP :

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



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