Data Warehousing

Data Warehousing

1. PROCESS OVERVIEW

   • Organizations, private and public, continuously collect data, information, and knowledge at an increasingly accelerated rate and store them in computerized systems.

2. DATA WAREHOUSING ARCHITECTURES

   • These architectures are commonly called client/ server or n-tier architectures, of which two-tier and three-tier architectures are the most common
   • Operational systems contain the data and the software for data acquisition in one tier
   • Data from the warehouse are processed twice and deposited in an additional multidimensional database, organized for easy multidimensional analysis and presentation, or replicated in data marts.

3. Alternative Data Warehousing Architectures

   • At the highest level, data warehouse architecture design viewpoints can be categorized into enterprise-wide data warehouse (EDW) design and data mart (DM) design
     • Independet Data Mart
       • The data marts are developed to operate independently of each other to serve for the needs of individual organizational units.
     • Data mart bus architecture
       • The individual marts are linked to each other via some kind of middleware.
     • Hub-and-spoke architecture
       • Here the attention is focused on building a scalable and maintainable infrastructure, that includes a centralized data warehouse and several dependent data marts
     • Centralized data warehouse
       • There is a gigantic enterprise data warehouse that serves for the needs of all organizational units.
     • Federated data warehouse.
       • It uses all possible means to integrate analytical resources from multiple sources to meet changing needs or business conditions

4. Data Integration

   • Permit data to be accessed and made accessible to an array of ETL and analysis tools and data warehousing environment

5. Extraction, Transformation, and Load (ETL}

   • The three database functions are integrated into one tool to pull data out of one or more databases and place them into another, consolidated database or a data warehouse.
   • ETL is extremely impo1tant for data integration as well as for data warehousing.