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Oracle Database: Data Warehousing Fundamentals

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Lerninhalte:

  • Begriffsbestimmung und grundlegende Data Warehousing Konzepte
  • Technologie und Werkzeuge von Oracle für erfolgreiche DWH Implementierungen
  • Methoden für Datenextraktion, Datentransformation und Datenladen
  • Kennenlernen einiger Werkzeuge für Zugriff und Analyse von Data Warehouse Daten
  • Vorteile von Partitionierung, paralleler Verarbeitung, Materialized Views und Query Rewrite in einem Data Warehouse
  • Einführung in Implementierung und Organisation von Data Warehouse Projekten


Teilnehmerkreis:

  • Technischer Data Warehouse Berater
  • Data Warehouse Analyst
  • Data Warehouse Entwickler
  • Data Warehouse Administrator


Empfohlene Vorkenntnisse:

  • Wissen über allgemeine Data Warehouse Konzepte
  • Wissen über Client-Server Technologie
  • Wissen über Technologie relationaler Datenbanken

 

Kursziele:

  • 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


Kursthemen:

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 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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