© 2010 SAP AG
Module BI1-M2 Data Warehouse Architecture
BI Platform: Data Warehousing
Architecture Alternatives
Conceptual Layers
SAP University Alliances Version 1.0
Author Lorraine Gardiner
Paul Hawking
Robert Jovanovic
ProductNone
FocusData warehousing overview
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 2© 2010 SAP AG
Agenda
• BI Platform: Data Warehousing• Architecture Alternatives• Conceptual Layers
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 3© 2010 SAP AG
Common BI Architecture
Source: Eckerson, W. (May 2006). Business intelligence 2006 – only the beginning. What Works: Best Practices in Business Intelligence and Data Warehousing, 21.
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 4© 2010 SAP AG
Data Warehousing: Single Version of the Truth
“The truth, the whole truth, and nothing but the truth …”
Source: Inmon, B. (September 9, 2006). The single version of the truth. Business Intelligence Network. Retrieved February 22, 2008 from http://www.b-eye-network.com/view/282
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 5© 2010 SAP AG
Extraction – Transformation – Load (ETL)
Source: Data warehouse framework. BiPM Institute. Retrieved February 22, 2008 from http://bipminstitute.com/template/topic.php?topic_id=DAC
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 6© 2010 SAP AG
Agenda
• BI Platform: Data Warehousing• Architecture Alternatives• Conceptual Layers
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 7© 2010 SAP AG
Source: Sen, A., & Sinha, A. P. (January 2007). Toward developing data warehousing standards: an ontology-based review of existing methodologies. IEEE Transactions on Systems, Man, and Cybernetics. 37, 17-31.
Data Warehousing Architecture Alternatives
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 8© 2010 SAP AG
Source: Sen, A., & Sinha, A. P. (January 2007). Toward developing data warehousing standards: an ontology-based review of existing methodologies. IEEE Transactions on Systems, Man, and Cybernetics. 37, 17-31.
Standard Practices for Architecture Design
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 9© 2010 SAP AG
Source: Sen, A., & Sinha, A. P. (January 2007). Toward developing data warehousing standards: an ontology-based review of existing methodologies. IEEE Transactions on Systems, Man, and Cybernetics. 37, 17-31.
Standard Practices for Data Modeling
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 10© 2010 SAP AG
Agenda
• BI Platform: Data Warehousing• Architecture Alternatives• Conceptual Layers
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 11© 2010 SAP AG
Data Warehousing Conceptual Layers
Source: Architecture of a data warehouse. SAP AG. Retrieved February 22, 2008 from http://help.sap.com/saphelp_nw70/helpdata/en/43/4a86b4224847b6e10000000a11466f/content.htm
Any Source
(Persistent) Staging Area
Data Warehouse
(Architected) Data Marts
OperationalData Store
Access to Information
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 12© 2010 SAP AG
Persistent Staging Area (PSA)
• Storage area for data extracted from sources
• Requested data is saved directly from its source (without changes)
• First step in loading data into the operational data store (ODS) or data warehouse
Any Source
(Persistent) Staging Area
Data Warehouse
(Architected) Data MartsOperationalData Store
Access to Information
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 13© 2010 SAP AG
Operational Data Store (ODS)
• Operational reporting• Granular data• Volatile, near real
time• May feed Data
Warehouse layer at set intervals
Any Source
(Persistent) Staging Area
Data Warehouse
(Architected) Data MartsOperationalData Store
Access to Information
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 14© 2010 SAP AG
Data Warehouse
• Historical foundation for BI
• Granular data• Integrated• Nonvolatile• Application neutral Any Source
(Persistent) Staging Area
Data Warehouse
(Architected) Data MartsOperationalData Store
Access to Information
SAP BI Curriculum
BI1-M2 Data Warehouse Architecture
SAP University Alliances
Page 15© 2010 SAP AG
(Architected) Data Marts
• Focus: Information needs of a business unit or function
• Often aggregated, may be granular
• Often dimensional data models Any Source
(Persistent) Staging Area
Data Warehouse
(Architected) Data MartsOperationalData Store
Access to Information
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