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Data Warehouse Architecture.pptx
1.
Chapter 9 1 Copyright
© 2014 Pearson Education, Inc. Data Warehouse Architecture
2.
Chapter 9 2 Copyright
© 2014 Pearson Education, Inc. Definitions •Data Warehouse • A subject-oriented, integrated, time-variant, non- updatable collection of data used in support of management decision-making processes • Subject-oriented: e.g. customers, patients, students, products • Integrated: consistent naming conventions, formats, encoding structures; from multiple data sources • Time-variant: can study trends and changes • Non-updatable: read-only, periodically refreshed •Data Mart • A data warehouse that is limited in scope
3.
Chapter 9 3 Copyright
© 2014 Pearson Education, Inc. Organizational Trends Motivating Data Warehouses •No single system of records •Multiple systems not synchronized •Organizational need to analyze activities in a balanced way •Customer relationship management •Supplier relationship management
4.
Chapter 9 4 Copyright
© 2014 Pearson Education, Inc. Separating Operational and Informational Systems • Operational system – a system that is used to run a business in real time, based on current data; also called a system of record • Informational system – a system designed to support decision making based on historical point-in-time and prediction data for complex queries or data-mining applications
5.
Chapter 9 5 Copyright
© 2014 Pearson Education, Inc. Data Warehouse Architectures •Independent Data Mart •Dependent Data Mart and Operational Data Store •Logical Data Mart and Real-Time Data Warehouse •Three-Layer architecture All involve some form of extract, transform and load (ETL)
6.
Chapter 9 6 Copyright
© 2014 Pearson Education, Inc. Independent data mart data warehousing architecture Data marts: Mini-warehouses, limited in scope E T L Separate ETL for each independent data mart Data access complexity due to multiple data marts
7.
Chapter 9 7 Copyright
© 2014 Pearson Education, Inc. Dependent data mart with operational data store: a three-level architecture E T L Single ETL for enterprise data warehouse (EDW) Simpler data access ODS provides option for obtaining current data Dependent data marts loaded from EDW 7
8.
Chapter 9 8 Copyright
© 2014 Pearson Education, Inc. E T L Near real-time ETL for Data Warehouse ODS and data warehouse are one and the same Data marts are NOT separate databases, but logical views of the data warehouse Easier to create new data marts Figure 9-4 Logical data mart and real time warehouse architecture
9.
Chapter 9 9 Copyright
© 2014 Pearson Education, Inc. Chapter 9 9 Copyright © 2014 Pearson Education, Inc.
10.
Chapter 9 10 Copyright
© 2014 Pearson Education, Inc. Three-layer data architecture for a data warehouse 10
11.
Chapter 9 11 Copyright
© 2014 Pearson Education, Inc. Derived Data • Objectives • Ease of use for decision support applications • Fast response to predefined user queries • Customized data for particular target audiences • Ad-hoc query support • Data mining capabilities • Characteristics • Detailed (mostly periodic) data • Aggregate (for summary) • Distributed (to departmental servers) Most common data model = dimensional model (usually implemented as a star schema)
12.
Chapter 9 12 Copyright
© 2014 Pearson Education, Inc. Online Analytical Processing (OLAP) Tools • The use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques •Relational OLAP (ROLAP) • Traditional relational representation •Multidimensional OLAP (MOLAP) • Cube structure •OLAP Operations • Cube slicing–come up with 2-D view of data • Drill-down–going from summary to more detailed views
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