Oracle - Course Details

OraDDWF11g:SSC Oracle Database 11g: Data Warehousing Fundamentals Self-Study Course

Oracle Course 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.
Oracle Course Duration:
Self Paced
Oracle Course Target Audience:
Application Developers,Data Warehouse Administrator,Data Warehouse Analyst,Data Warehouse Developer,Developer,Functional Implementer,Project Manager,Support Engineer
Oracle Course Prerequisite:
Knowledge of general data warehousing concepts,Knowledge of client-server technology,Knowledge of relational server technology
Course Content:
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