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