datawarehouse org
DESCRIPTION
Data warehousing PPTTRANSCRIPT
DATA WAREHOUSE
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 based2.Relational/multidimensional3.Client/Server4.Real time(recent trends)5.Include metadata
Types of Data warehouseDATA 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
• 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.
Data warehousing process overview
Major components
• Data sources
• Data extraction
• Data loading
• Comprehensive database
• Metadata
• Middleware tools
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
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.
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.
ETL Process
Transient Data
source DataWarehouse
DataMart
Packagedapplication
Legacysystem
Extract
Other Internal
applications
Transform Cleanse Load
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.
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.
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
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.