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.
Read data, process the data and format
the data in to structured reports
and dashboards those are delivered to
business users.
- Data Mining Tools :
- Knowledge Management Tools :
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.
• 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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