Week 6 Lecture The Data Warehouse Samuel Conn, Asst. Professor

Week 6 Lecture

The Data Warehouse

Samuel Conn, Asst. Professor

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In this lecture, you will learn: How operational data and decision

support differ What a data warehouse is and how its

data are prepared What star schemas are and how they

are constructed What steps are required to implement a

data warehouse successfully What data mining is and what role it

plays in decision support

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External and internal forces require tactical and strategic decisions

Search for competitive advantage Business environments are dynamic Decision-making cycle time is reduced Different managers require different

decision support systems (DSS)

The Need for Data Analysis

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Decision Support Is a methodology Extracts information from data Uses information as basis for decision

making

Decision Support Systems

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Decision Support Systems Decision support system (DSS)

Arrangement of computerized tools Used to assist managerial decision Extensive data “massaging” to produce

information Used at all levels in organization Tailored to focus on specific areas and needs Interactive Provides ad hoc query tools

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DSS Components

Figure 13.1

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Operational data Relational, normalized database Optimized to support transactions Real time updates

DSS Snapshot of operational data Summarized Large amounts of data

Data analyst viewpoint Timespan Granularity Dimensionality

Operational vs. Decision Support Data

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Database schema Support complex (non-normalized) data Extract multidimensional time slices

Data extraction and filtering End-user analytical interface Database size

Very large databases (VLDBs) Contains redundant and duplicated data

The DSS Database Requirements

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Integrated Centralized Holds data retrieved from entire organization

Subject-Oriented Optimized to give answers to diverse questions Used by all functional areas

Time Variant Flow of data through time Projected data

Non-Volatile Data never removed Always growing

Data Warehouse

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Creating a Data Warehouse

Figure 13.3

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Single-subject data warehouse subset

Decision support to small group Can be test for exploring potential

benefits of Data warehouses Address local or departmental

problems

Data Marts

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DSS Architectural Styles

Traditional mainframe-based OLTP Managerial information system

(MIS) with 3GL First-generation departmental DSS First-generation enterprise data

warehouse using RDMS Second-generation data

warehouse using MDBMS

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Advanced data analysis environment Supports decision making, business

modeling, and operations research activities

Characteristics of OLAP Use multidimensional data analysis

techniques Provide advanced database support Provide easy-to-use end-user interfaces Support client/server architecture

Online Analytical Processing (OLAP)

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OLAP Client/Server Architecture

Figure 13.6

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OLAP Server Arrangement

Figure 13.7

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OLAP Server with Multidimensional Data Store Arrangement

Figure 13.8

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OLAP Server with Local Mini-Data-Marts

Figure 13.9

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OLAP functionality Uses relational DB query tools Extensions to RDBMS

Multidimensional data schema support Data access language and query

performance optimized for multidimensional data

Support for very large databases (VLDBs)

Relational OLAP (ROLAP)

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Typical ROLAP Client/Server Architecture

Figure 13.10

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OLAP functionality to multidimensional databases (MDBMS)

Stored data in multidimensional data cube

N-dimensional cubes called hypercubes

Cube cache memory speeds processing

Affected by how the database system handles density of data cube called sparsity

Multidimensional OLAP (MOLAP)

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MOLAP Client/Server Architecture

Figure 13.11

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Data-modeling technique Maps multidimensional decision support into

relational database Yield model for multidimensional data

analysis while preserving relational structure of operational DB

Four Components: Facts Dimensions Attributes Attribute hierarchies

Star Schema

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Simple Star Schema

Figure 13.12

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Slice and Dice View of Sales

Figure 13.14

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Facts and dimensions represented by physical tables in data warehouse DB Fact table related to each dimension table (M:1) Fact and dimension tables related by foreign keys Subject to the primary/foreign key constraints

Star Schema Representation

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Star Schema for Sales

Figure 13.17

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Normalization of dimensional tables Multiple fact tables representing different aggregation levels

Denormalization of the fact tables Table partitioning and replication

Performance-Improving Techniques for Star Schema

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Data Warehouse Implementation Road Map

Figure 13.21

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Seeks to discover unknown data characteristics

Automatically searches data for anomalies and relationships

Data mining tools Analyze data Uncover problems or opportunities Form computer models based on findings Predict business behavior with models Require minimal end-user intervention

Data Mining

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Extraction of Knowledge from Data

Figure 13.22

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Data Mining Process

Figure 13.23