Knowledge is power

Business Object Data Integration

Module 1: Introduction to BI and ETL

Module ObjectivesAfter completing this unit , you should be able to :

•Describe about Business Intelligence

•Describe about Databwarefeatures, Datawarehouseschemas

•List and describe about OLTP and DWH

•Describe about ETL Process

•List and Describe about Various BI tools including ETL,Database, Reporting and design tools

What is BI?

•Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.

•BI applications include the activities of decision support, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

Examples : Business Objects : www.businessobjects.com

BI-Nutshell

Data, Data everywhere yet ...

•I can’t find the data I need

–data is scattered over the network

–many versions, subtle differences

I can’t get the data I need

need an expert to get the data

I can’t understand the data I found

available data poorly documented

I can’t use the data I found

results are unexpected

data needs to be transformed from one form to other

What is a Data Warehouse?

A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context.

[Barry Devlin]

What are the users saying...

•Data should be integrated across the enterprise

•Summary data has a real value to the organization

•Historical data holds the key to understanding data over time

•What-if capabilities are required

What is Data Warehousing?

A processof transforming data into information and making it available to users in a timely enough manner to make a difference

Data Warehouse

•A data warehouse is a

–subject-oriented

–integrated

–time-varying

–non-volatile

–Accessible

collection of data that is used primarily in organizational decision making.

--Bill Inmon, Building the Data Warehouse 1996

OLTP vs Data Warehouse
•OLTP	•Warehouse (DSS)
–Application Oriented	–Subject Oriented
–Used to run business	–Used to analyze business
–Detailed data	–Summarized and refined
–Current up to date	–Snapshot data
–Isolated Data	–Integrated Data
–Repetitive access	–Ad-hoc access
–Clerical User	–Knowledge User (Manager)

OLTP vs Data Warehouse
•OLTP	•Data Warehouse
–Performance Sensitive	–Performance relaxed
–Few Records accessed at a time (tens)	–Large volumes accessed at a time(millions)
–Read/Update Access	–Mostly Read (Batch Update)
–No data redundancy	–Redundancy present
–Database Size 100MB -100 GB	–Database Size 100 GB -few terabytes
–Thousands of users	–Hundreds of users