The document provides an overview of data warehousing concepts including: 1) A data warehouse is a subject-oriented collection of integrated data used to support management decisions. It contains current and historical data. 2) A data warehouse architecture typically includes source systems, a staging area, and presentation layer for querying and reporting. 3) Data marts are focused subsets of a data warehouse tailored for specific business units or departments. There are dependent, independent, and hybrid approaches to building data marts.

1. DWH Session1

2. Agenda
• Business Challenges
• BI
• Why DWH/EDWH
• What is DWH
• DWH Architecture
• Popular ETL & Reporting tool names
• What is OLTP
• OLTP vs OLAP
• Data Mart
• Types of Data Marts
• DWH vs Data Mart
• ODS
• DWH vs ODS

3. Business Challenges
 Ensuring availability of ‘clean’ data
 Multiple sources of data and reporting systems
 Difficulty in locating and addressing information
 Technology Islands
 Information requests take days or weeks to fulfill
 Rapidly changing business strategies
 Pressures to lower costs and improve services

4. Business Intelligence (BI)
• ‘BI is a broad category of applications and technologies for
gathering, storing, analyzing, and providing access to data to
help enterprise users make better business decisions’
• DWH contains Current Data + Complete History

5. What is a Data Warehouse ?
• A data warehouse is a subject-oriented, integrated,
nonvolatile, time-variant collection of data in support of
management's decisions.

6. Why DWH
• Single view of data
• Effective decision making
• Profit maximization & Cost reduction
• Resource Optimization
• Greater customer insight
• Better business insight
• Business Forecasting
• On-line indicators of business trends

7. Data Warehouse Architecture

8. DW Components
 Operational Source systems
– Single/Multiple applications
– Flat files/Database
 Data Staging
– Temporary storage
– Little or no history
– Structure almost resembles source
– No query and reporting
 Presentation layer
– Query/reporting tools
– OLAP (Online Analytical Processing)
– Data Mining

9. Data Warehousing - Benefits
 Increase customer profitability
 Cost effective decision making
 Manage customer and business partner relationships
 Link multiple locations and geographies
 Identify developing trends and reduce time to market
 Improve quality assurance programs
 Production & Performance awareness

10. Popular ETL & Reporting Tool Names
ETL Tool Name Owner Name
Business Objects Data Services (BODS) SAP
Informatica Informatica
Data Stage IBM
Sql Server Integration Services (SSIS) Micro Soft
Oracle Data Integrator (ODI) Oracle
Talend Talend
Reporting Tool Name Owner Name
Business Objects (BO) SAP
Qlikview Qlikview
Cognos IBM
Sql Server Reporting Services (SSRS) Micro Soft
Power BI Micro Soft
Tableau Tableau

11. Transactional System VS Data Warehouse
OLTP vs OLAP
Online Transactional Processing vs Online Analytical Processing
Transactional System Data Warehouse/OLAP
System Usage Regular Business Analysis
Data Modifications Read/Write Read Only
Type of Queries Predefined Predefined / Ad-hoc/Customized
Data Normalized De-Normalized
Historical Data Few weeks Months to Years
Size MB – GB GB - TB
Indexes Few Many
Joins Many Some
Derived data and Aggregates Rare Common

12. DataMart & Types of Datamarts (Approach)
Data Mart :-
• A data mart is a structure / access pattern specific to data warehouse environments, used to retrieve
client-facing data. The data mart is a subset of the data warehouse and is usually oriented to a specific
business line or team.(Means more subject oriented)
• A DATA MART is focused on a single functional area of an organization and contains a subset of data
stored in a Data Warehouse. A Data Mart is a condensed version of Data Warehouse and is designed
for use by a specific department, unit or set of users in an organization. E.g., Marketing, Sales, HR or
finance. It is often controlled by a single department in an organization.
• Type of Data Mart
There are three main types of data marts are:
• Dependent: Dependent data marts are created by drawing data directly from DWH.
• Independent: Independent data mart is created without the use of a central data warehouse.
• Hybrid: This type of data marts can take data from data warehouses or operational systems.

13. Types of Approaches
• Top Down Approach (Inmon) Dependent Data Mart
• Build Enterprise Data warehouse
• Data Marts to be derived from Warehouse
• Bottom Up Approach (Kimball) Independent Approach
• Marts to be built for different departments
• Data warehouse derived by linking data marts

14. Operational system
Operational system
Sales
Purchase
Inventory
Operational system
• Data Sources Staging Area Warehouse Data mart
Top Down Approach(Dependent Data Mart)

15. Source Staging Data Mart Data warehouse
Operational system
Operational system
Sales
Purchase
Inventory
Operational system
Bottom up Approach (Independent Data Mart)

16. DWH vs Data Mart
Data Warehouse First Data Mart first
Expensive Relatively cheap
Large development cycle Delivered in < 6 months
Change management is difficult Easy to manage change
Difficult to obtain continuous corporate support Can lead to independent and incompatible marts
Technical challenges in building large databases Cleansing, transformation, modeling techniques
may be incompatible

17. Process
 Identification of Business process
 Requirements Gathering
 Design
– Dimensional Model
– Staging Area
– ETL Process
– Granularity
– DB Objects
– Indexing
– Parallelism
– Partition
– Metadata
 Build
 Deploy
 Manage

18. Operational Data Store (ODS)
• Backup for Source System
• obtaining current data

21. Q & A