Breza Software Engineering - Breza Erp, Breza Gas, Oracle partner, Trening centar, IT resenja, IT Outsourcing

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

Application Developers

Data Warehouse Administrator

Data Warehouse Analyst

Data Warehouse Developer

Developer

Functional Implementer

Project Manager

Support Engineer

Knowledge of general data warehousing concepts

Knowledge of client-server technology

Knowledge of relational server technology

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

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 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

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)

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 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

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

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

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

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

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

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

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