Oracle Database 11g: Data Warehousing Fundamentals (Self-Study Course)

 

Quién debería asistir

  • Data Warehouse administrators
  • Data Warehouse analysts
  • Developers
  • Project managers

Objetivos del curso

  • Define the terminology and explain the basic concepts of data warehousing
  • Describe methods and tools for extracting, transforming, and loading data
  • Identify some of the tools for accessing and analyzing warehouse data
  • Identify the technology and some of the tools from Oracle to implement a successful data warehouse
  • Define the decision support purpose and end goal of a data warehouse
  • 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
  • Use materialized views and query rewrite to improve the data warehouse performance
  • Develop familiarity with some of the technologies required to implement a data warehouse

Descripción del Producto

  • 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
  • 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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