Oracle Database 11g: Data Warehousing Fundamentals Training

Live Online & Classroom Enterprise Certification Training

This Database 11G Data Warehousing training teaches data warehousing concepts and technologies, while examining Oracle’s approach to data warehouse implementation. Review partitioning, parallel operations, materialized views and more.

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What is 11g: Data Warehousing Fundamentals Certification Training about?

This Oracle Database 11g: Data Warehousing Fundamentals training will teach you about the basic concepts of a data warehouse. Explore the issues involved in planning, designing, building, populating and maintaining a successful data warehouse.

You'll also explore the basics of Oracle’s Database partitioning architecture, identifying the benefits of partitioning. Review the benefits of parallel operations to reduce response time for data-intensive operations. Learn how to extract, transform and load data (ETL) into an Oracle database warehouse.

Learn the benefits of using Oracle’s materialized views to improve the data warehouse performance. Instructors will give a high-level overview of how query rewrites can improve a query’s performance. Explore OLAP and Data Mining and identify some data warehouse implementations considerations.

During this training, you'll briefly use some of the available data warehousing tools. These tools include Oracle Warehouse Builder, Analytic Workspace Manager and Oracle Application Express.

What are the objectives of 11g: Data Warehousing Fundamentals Certification Training ?

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
Define the terminology and explain the basic concepts of data warehousing
Develop familiarity with some of the technologies required to implement a data warehouse

Who is 11g: Data Warehousing Fundamentals Certification Training for?

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

What are the prerequisites for 11g: Data Warehousing Fundamentals Certification Training?

Suggested Prerequisite

Knowledge of client-server technology
Knowledge of relational server technology
Knowledge of general data warehousing concepts

Available Training Modes

Live Online Training

24 Hours

Classroom Training

3 Days

Course Outline Expand All

Expand All

Introduction

The sh and dm Sample Schemas and Appendices Used in the Course

Class Account Information

Course Schedule

Course Objectives

SQL Environments and Data Warehousing Tools Used in this Course

Oracle 11g Data Warehousing and SQL Documentation and Oracle By Examples

Course Pre-requisites and Suggested Pre-requisites

Continuing Your Education: Recommended Follow-Up Classes

Data Warehousing, Business Intelligence, OLAP, and Data Mining

Data Warehouse Definition and Properties

Extraction, Transformation, and Loading (ETL)

Warehouse Development Approaches

The Dimensional Model and Oracle OLAP

Data Warehouses, Business Intelligence, Data Marts, and OLTP

Oracle Data Mining

Typical Data Warehouse Components

Defining Data Warehouse Concepts and Terminology

Data Warehouse Definition and Properties

Data Warehouses Versus Data Marts

Strategy Phase Deliverables

Typical Data Warehouse Components

Warehouse Development Approaches

Introducing the Case Study: Roy Independent School District (RISD)

Data Warehousing Process Components

Data Warehouse Versus OLTP

Business, Logical, Dimensional, and Physical Modeling

Defining the Business Model

Fact and Dimension Tables Characteristics

Translating Business Dimensions into Dimension Tables

Translating Dimensional Model to Physical Model

Defining the Physical Model: Star, Snowflake, and Third Normal Form

Defining the Logical Model

Defining the Dimensional Model

Data Warehouse Modeling Issues

Database Sizing, Storage, Performance, and Security Considerations

Indexing

Oracle’s Strategy for Data Warehouse Security

Security in Data Warehouses

Optimizing Star Queries: Tuning Star Queries

Database Sizing and Estimating and Validating the Database Size

Oracle Database Architectural Advantages

Parallelism

Data Partitioning

The ETL Process: Extracting Data

Extraction Techniques and Maintaining Extraction Metadata

Possible ETL Failures and Maintaining ETL Quality

Extraction, Transformation, and Loading (ETL) Process

Extracting Data and Examining Data Sources

Mapping Data

Logical and Physical Extraction Methods

Oracle’s ETL Tools: Oracle Warehouse Builder, SQL*Loader, and Data Pump

ETL: Tasks, Importance, and Cost

The ETL Process: Transforming Data

Remote and Onsite Staging Models

Transformation

Maintaining Transformation Metadata

Quality Data: Importance and Benefits

Data Anomalies

Transformation Routines

Transformation Techniques and Tools

Transforming Data: Problems and Solutions

The ETL Process: Loading Data

Data Granularity

Loading Data into the Warehouse

Transportation Using Flat Files, Distributed Systems, and Transportable Tablespaces

Loading Techniques Provided by Oracle

Indexing and Sorting Data and Verifying Data Integrity

Data Refresh Models: Extract Processing Environment

Building the Loading Process

Postprocessing of Loaded Data

Refreshing the Warehouse Data

Time- and Date-Stamping, Database triggers, and Database Logs

Developing a Refresh Strategy for Capturing Changed Data

Planning and Scheduling the Load Window

Final Tasks

Capturing Changed Data for Refresh

User Requirements and Assistance

Load Window Requirements

Applying the Changes to Data

Materialized Views

Using Summaries to Improve Performance

Working With Dimensions and Hierarchies

Types of Materialized Views

Build Modes and Refresh Modes

Using Materialized Views for Summary Management

Query Rewrite: Overview

Cost-Based Query Rewrite Process

Leaving a Metadata Trail

Integrating Multiple Sets of Metadata

Managing Changes to Metadata

Defining Warehouse Metadata

Identifying Target Metadata Users and Choosing Metadata Tools and Techniques

Metadata Users and Types

Extraction, Transformation, and Loading Metadata

Examining Metadata: ETL Metadata

Defining Metadata Goals and Intended Usage

Data Warehouse Implementation Considerations

Project Management

Data Warehouse Architecture

Some Useful Resources and White Papers

ETL, Reporting, and Security Considerations

Logical, Dimensional, and Physical Data Models

Testing the Implementation and Post Implementation Change Management

Metadata Management

Requirements Specification or Definition

Who is the instructor for this training?

The Trainer is Oracle certified Instructor with extensive domain experience, including years of experience training & mentoring professionals in the industry.

11g: Data Warehousing Fundamentals Certification Training - Certification & Exam

SpringPeople is the Authorized Training Partner of Oracle.
The training fees is exclusive of exam cost.
For any queries; feel free to reach us at oracle@springpeople.com

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