Data warehousing PPT

1. DATA WAREHOUSE

2. Data Warehousing
• DW: Pool of data produced to support decision making.
• Structured to be available in ready to use form
• Subject Oriented
• Integrated
• Time-variant
• Nonvolatile
• Additional characteristics like
1.Web based
2.Relational/multidimensional
3.Client/Server
4.Real time(recent trends)
5.Include metadata

3. Types of Data warehouse
DATA Mart
• Dependent
– Created from warehouse
– Replicated
• Functional subset of warehouse
• Independent
– Scaled down, less expensive version of data warehouse
– Designed for a department or SBU
– Organization may have multiple data marts
• Difficult to integrate

4. • Operational DATA Stores: Provides a fairly
recent form of customer information file(CIF)
• Enterprise DATA Warehouses: Used across the
enterprise for decision support
• METADATA: Describes the structure of and
meaning about data, contributing to their
effective use.

5. Data warehousing process overview
Major components
• Data sources
• Data extraction
• Data loading
• Comprehensive database
• Metadata
• Middleware tools

7. Data Warehousing Architectures
• May have one or more tiers
– Determined by warehouse, data acquisition (back
end), and client (front end)
• One tier, where all run on same platform, is rare
• Two tier usually combines DSS engine (client) with
warehouse
– More economical
• Three tier separates these functional parts

9. Data Integration, Extraction And Load
process
1.DATA INTEGRATION
Comprises three major processes
• Data Access: ability to access & extract data
from any data source
• Data federation: Integration of business views
across multiple data store
• Change capture: Based on the identification,
capture, and delivery of the changes made to
enterprise data source.

10. 2.Extraction, Transformation And Load(ETL)
• Is an integral component in any data-centric
project.
• ETL consists:
Extraction-From all relevant sources
Transformation-Converting extracted data in the
form so it can place in data warehouse or
another database
Load-Putting the data in the data warehouse.

11. ETL Process
Transient
Data
source Data
Warehouse
Data
Mart
Packaged
application
Legacy
system
Extract
Other
Internal
applications
Transform Cleanse Load

12. Benefits of Data Warehouse
• Allows extensive analysis in numerous ways.
• A consolidated view of corporate data.
• Better and more timely information.
• Enhance system performance.
• Simplification of data access.
• Enhance business knowledge, enhance
customer service and satisfaction, facilitate
decision making.

13. Data Warehouse development
Approaches
The Inmon Model: The EDW Approach
• Emphasizes top-down development
• Employing established database development
methodologies and tools
The Kimball Model: The Data Mart Approach
• Plan big, build small
• Subject oriented or department oriented
• Focus on the requests of a specific department.

14. Successful Implementation of Data
warehouse
• Establishment of service-level agreements and data-refresh
requirements.
• Identification of data sources and their governance
policies.
• Data quality planning & model designing.
• ETL tool selection.
• Relational database software and platform selection.
• Data transport and data conversion.
• Reconciliation process
• End-user support

15. Real-Time Data warehousing
• Also knows as active data warehousing.
• Process of loading & providing data via the
data warehouse.
• Evolved from EDW (Enterprise Data Warehousing)
concept.
• Allows information-based decision making at
finger tips.
• Positively affect almost all aspects of customer
service, SCM, logistics.