How This Course Is Organised

The Oracle Database 12c: Certfied Associate Data Warehousing Essentials course provides introductory to intermediate lessons and instructions on how to perform a variety of Data Warehousing and ETL tasks using real-life work based scenarios.

This Oracle Database 12c: Data Warehousing Essentials  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.

The course begins with an introduction to fundamental concepts related to Data Warehousing, Business Intelligence, OLAP and Predictive Analytics / Data mining. You'll also explore the basics of Oracle’s Database partitioning architecture, identifying the benefits of partitioning. In addition, you will 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 using Oracle Data Integrator 12c.

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.

The WebLearning hands-on series reduces the time spent investigating what steps are required to perform a task. Because the step-by-step solutions are built for practical real world situations, not only is knowledge gained through valuable hands-on experience, but also the solutions presented may then be used as the foundation for production implementation, dramatically reducing time to deployment.

Audience

Application Developers

Data Warehouse Administrator

Data Warehouse Analyst

Data Warehouse Developer

Developer

Functional Implementer

Project Manager

Support Engineer

Suggested Prerequisites

WebLearning Business Intelligence Fundamentals

WebLearning  Oracle Data Integrator 11g ETL Fundamentals

Knowledge of relational server technology

Course Objectives: 

Upon completion of this Course, you should be able to: 

Define the terminology and explain the basic concepts of data warehousing

Understand the Oracle Data Warehouse Technology Stack

Understand the Oracle Data Warehouse Reference Architecture

Understand the Oracle Business Intelligence and Analytics Technology Stack

Describe methods and tools for extracting, transforming, and loading data

Use Oracle Data Integrator 12c ETL for extracting, transforming, and loading data

Identify some of the tools for accessing and analysing 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

Understand the implementation and organisational issues surrounding a data warehouse project

Use materialised views and query rewrite to improve the data warehouse performance

Develop familiarity with some of the technologies required to implement a data warehouse

Course Topics

Introduction to Oracle Database 12c

Oracle Database 12c Options

Database Management Packs

Oracle Enterprise Manager 12c Cloud Control

Oracle Cloud Architecture

Oracle Cloud Services

Oracle Multitenant Architecture

Oracle Database Development Tools

Oracle SQL Developer

Navigate SQL Developer

Exploring Database Objects

SQL*Plus and Oracle Enterprise Manager 12c

Connecting to the  SQL*Plus Interface

Connecting to Databases

Configuring Database Environment 

Exploring Oracle Enterprise Manager 12c

Introduction to Data Warehousing and Analytics Concepts

Understanding ETL and Data Warehousing

Understanding Business Intelligence

Understanding OLAP and Data Mining

BI and Warehouse Architecture Framework

Data Sources

Data Access, Transformation, Cleansing

Staged Data

Business Subject Data

Multidimensional Data

Backup, Archive Data

Structured Query Language and OLAP Engine

Business Intelligence Applications

Administration, Automation, Operation, Control

Metadata Repository

Visualisation and Publishing

Oracle Data Warehouse Components

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 Warehouse Builder

Oracle Data Integrator

Oracle Database OLAP Option

Oracle Data Mining

Oracle BIEE

Oracle BI Publisher

Data Warehouse Concepts and Terminology

Data Warehouse Definition and Properties

Data Warehouse Versus OLTP

Data Warehouses Versus Data Marts

Typical Data Warehouse Components

Data Warehousing Process Components

Data Warehousing and Modelling Concepts

Data Warehousing Concepts

Modelling for Analytics and Data Warehouses

Normalisation

3rd normal Form

Dimensional Modelling

Fact tables

Types of Fact Tables

Dimension Tables

Star Schema

Snowflake Models

Conformed Dimensions

Alternate Dimensions

Degenerate Dimensions

Junk Dimensions

Slowly Changing Dimensions

Surrogate keys

Data Warehousing Deployment Approaches

Warehouse Bus Architecture

Centralised Data Warehouse Approach

Hub and Spoke Approach

Approach to Oracle Data Warehouse and  ETL Projects

Strategy and Planning Phase

Discover Phase

Analyse Phase

Define Phase

Develop Phase

Deploy Phase

Oracle Data Warehouse Implementation Approach

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

Modelling the Data Warehouse:Logical Model

Business, Logical, Dimensional, and Physical Modeling

Data Warehouse Modeling Issues

Defining the Business Model

Defining the Logical Model

Defining the Dimensional Model

Modelling the Data Warehouse:Physical 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  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 Data Integrator

Data Oracle Warehouse Builder

SQL*Loader

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

Loading Techniques in Oracle Data Integrator 11g

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

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

Metadata  Management

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 

Choosing Metadata Tools and Techniques

Integrating Multiple Sets of Metadata

Managing Changes to Metadata

     

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