Data Warehouse Architectures

11,062 views

Published on

Published in: Technology
1 Comment
5 Likes
Statistics
Notes
No Downloads
Views
Total views
11,062
On SlideShare
0
From Embeds
0
Number of Embeds
843
Actions
Shares
0
Downloads
0
Comments
1
Likes
5
Embeds 0
No embeds

No notes for slide

Data Warehouse Architectures

  1. 1. Data Warehouse Architectures
  2. 2. <ul><li>Data warehouses and their architectures vary depending upon the specifics of an organization's situation. Three common architectures are: </li></ul><ul><li>Data Warehouse Architecture (Basic) </li></ul><ul><li>Data Warehouse Architecture (with a Staging Area) </li></ul><ul><li>Data Warehouse Architecture (with a Staging Area and Data Marts) </li></ul>Types
  3. 3. <ul><li>End users directly access data derived from several source systems through the data warehouse. </li></ul>Data Warehouse Architecture (Basic)
  4. 5. <ul><li>The figure illustrates three things: </li></ul><ul><li>Data Sources (operational systems and files) </li></ul><ul><li>Warehouse (metadata, summary data, and raw data) </li></ul><ul><li>Users (analysis, reporting, and mining) </li></ul>Data Warehouse Architecture (Basic)
  5. 6. <ul><li>You need to clean and process your operational data before putting it into the warehouse. You can do this programmatically, although most data warehouses use a staging area instead. A staging area simplifies building summaries and general warehouse management. </li></ul><ul><li>Staging area - A place where data is processed before entering the warehouse. </li></ul>Data Warehouse Architecture (with a Staging Area)
  6. 8. <ul><li>This illustrates four things: </li></ul><ul><li>Data Sources (operational systems and files) </li></ul><ul><li>Staging Area (where data sources go before the warehouse) </li></ul><ul><li>Warehouse (metadata, summary data, and raw data) </li></ul><ul><li>Users (analysis, reporting, and mining) </li></ul>Data Warehouse Architecture (with a Staging Area)
  7. 9. <ul><li>To customize your warehouse's architecture for different groups within your organization. </li></ul><ul><li>This by adding data marts , which are systems designed for a particular line of business. </li></ul><ul><li>The following example illustrates an example where purchasing, sales, and inventories are separated. In this example, a financial analyst might want to analyze historical data for purchases and sales. </li></ul>Data Warehouse Architecture (with a Staging Area and Data Marts)
  8. 11. <ul><li>This illustrates five things: </li></ul><ul><li>Data Sources (operational systems and flat files) </li></ul><ul><li>Staging Area (where data sources go before the warehouse) </li></ul><ul><li>Warehouse (metadata, summary data, and raw data) </li></ul><ul><li>Data Marts (purchasing, sales, and inventory) </li></ul><ul><li>Users (analysis, reporting, and mining) </li></ul>Data Warehouse Architecture (with a Staging Area and Data Marts)
  9. 12. <ul><li>Data Marts – A data mart is a focused subset of a data warehouse that deals with a single area of data and is organized for quick analysis. </li></ul><ul><li>Flat files - Flat files are data files that contain records with no structured relationships unlike relational database </li></ul><ul><li>Meta Data – Information about the data </li></ul><ul><li>Data stores – Data Sources </li></ul>Appendix

×