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

        Application Developers
        Data Warehouse Administrator
        Data Warehouse Analyst
        Data Warehouse Developer
        Functional Implementer
        Project Manager
        Support Engineer






               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
               Optimizing Star Queries: Tuning Star Queries
               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
               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