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课程大纲
   
 
  • 课程介绍
    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

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

  • 课程大纲:

            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

 

 
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