Introduction
Course Objectives
Course Schedule
Course Pre-requisites and Suggested Pre-requisites
The sh and dm Sample Schemas and Appendices Used in the
Course
Class Account Information
SQL Environments and Data Warehousing Tools Used in this
Course
Oracle 11g Data Warehousing and SQL Documentation and
Oracle By Examples
Continuing Your Education: Recommended Follow-Up Classes
Data Warehousing, Business Intelligence, OLAP, and Data
Mining
Data Warehouse Definition and Properties
Data Warehouses, Business Intelligence, Data Marts, and
OLTP
Typical Data Warehouse Components
Warehouse Development Approaches
Extraction, Transformation, and Loading (ETL)
The Dimensional Model and Oracle OLAP
Oracle Data Mining
Defining Data Warehouse Concepts and Terminology
Data Warehouse Definition and Properties
Data Warehouse Versus OLTP
Data Warehouses Versus Data Marts
Typical Data Warehouse Components
Warehouse Development Approaches
Data Warehousing Process Components
Strategy Phase Deliverables
Introducing the Case Study: Roy Independent School
District (RISD)
Business, Logical, Dimensional, and Physical Modeling
Data Warehouse Modeling Issues
Defining the Business Model
Defining the Logical Model
Defining the Dimensional Model
Defining the Physical Model: Star, Snowflake, and Third
Normal Form
Fact and Dimension Tables Characteristics
Translating Business Dimensions into Dimension Tables
Translating Dimensional Model to Physical Model
Database Sizing, Storage, Performance, and Security
Considerations
Database Sizing and Estimating and Validating the
Database Size
Oracle Database Architectural Advantages
Data Partitioning
Indexing
Optimizing Star Queries: Tuning Star Queries
Parallelism
Security in Data Warehouses
Oracle’s Strategy for Data Warehouse Security
The ETL Process: Extracting Data
Extraction, Transformation, and Loading (ETL) Process
ETL: Tasks, Importance, and Cost
Extracting Data and Examining Data Sources
Mapping Data
Logical and Physical Extraction Methods
Extraction Techniques and Maintaining Extraction Metadata
Possible ETL Failures and Maintaining ETL Quality
Oracle’s ETL Tools: Oracle Warehouse Builder, SQL*Loader,
and Data Pump
The ETL Process: Transforming Data
Transformation
Remote and Onsite Staging Models
Data Anomalies
Transformation Routines
Transforming Data: Problems and Solutions
Quality Data: Importance and Benefits
Transformation Techniques and Tools
Maintaining Transformation Metadata
The ETL Process: Loading Data
Loading Data into the Warehouse
Transportation Using Flat Files, Distributed Systems, and
Transportable Tablespaces
Data Refresh Models: Extract Processing Environment
Building the Loading Process
Data Granularity
Loading Techniques Provided by Oracle
Postprocessing of Loaded Data
Indexing and Sorting Data and Verifying Data Integrity
Refreshing the Warehouse Data
Developing a Refresh Strategy for Capturing Changed Data
User Requirements and Assistance
Load Window Requirements
Planning and Scheduling the Load Window
Capturing Changed Data for Refresh
Time- and Date-Stamping, Database triggers, and Database
Logs
Applying the Changes to Data
Final Tasks
Materialized Views
Using Summaries to Improve Performance
Using Materialized Views for Summary Management
Types of Materialized Views
Build Modes and Refresh Modes
Query Rewrite: Overview
Cost-Based Query Rewrite Process
Working With Dimensions and Hierarchies
Leaving a Metadata Trail
Defining Warehouse Metadata
Metadata Users and Types
Examining Metadata: ETL Metadata
Extraction, Transformation, and Loading Metadata
Defining Metadata Goals and Intended Usage
Identifying Target Metadata Users and Choosing Metadata
Tools and Techniques
Integrating Multiple Sets of Metadata
Managing Changes to Metadata
Data Warehouse Implementation Considerations
Project Management
Requirements Specification or Definition
Logical, Dimensional, and Physical Data Models
Data Warehouse Architecture
ETL, Reporting, and Security Considerations
Metadata Management
Testing the Implementation and Post Implementation Change
Management
Some Useful Resources and White Papers
Description:
In this course, students learn the basic concepts of a
data warehouse and study the issues involved in planning,
designing, building, populating, and maintaining a
successful data warehouse. Students learn to improve performance or
manageability in a data warehouse using various Oracle
Database features.
Students also learn the basics about Oracle’s Database
partitioning architecture and identify the benefits of partitioning.
Students review the benefits of parallel operations to
reduce response time for data-intensive operations. Students learn
about the extract, transform, and load of data phase
(ETL) into an Oracle database warehouse. Students learn the
basics about the benefits of using Oracle’s materialized
views to improve the data warehouse performance. Students
also learn at a high level how query rewrite can improve
a query’s performance. Students review OLAP and Data Mining
and identify some data warehouse implementations considerations.
Students briefly use some of the available data
warehousing tools such as Oracle Warehouse Builder, Analytic
Workspace Manager, and Oracle Application Express.
Learn To:
Define the terminology and explain basic concepts of data
warehousing
Identify the technology and some of the tools from Oracle
to implement a successful data warehouse
Describe methods and tools for extracting, transforming,
and loading data
Identify some of the tools for accessing and analyzing
warehouse data
Describe the benefits of partitioning, parallel
operations, materialized views, and query rewrite in a data warehouse
Explain the implementation and organizational issues
surrounding a data warehouse project
