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.

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.
• Improve performance or manageability in a data warehouse using various Oracle Database features.

Oracle’s Database Partitioning Architecture

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.

Improve Data Warehouse Performance

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.

Use Data Warehousing Tools

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.

Course Topics

• Introduction
• Data Warehousing, Business Intelligence, OLAP, and Data Mining 
• Defining Data Warehouse Concepts and Terminology
• Business, Logical, Dimensional, and Physical Modeling
• Database Sizing, Storage, Performance, and Security Considerations
• The ETL Process: Extracting Data
• The ETL Process: Transforming Data
• The ETL Process: Loading Data
• Refreshing the Warehouse Data
• Materialized Views
• Leaving a Metadata Trail
• Data Warehouse Implementation Considerations

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This course will help you understand the basic concepts of administering a data warehouse. You’ll learn to use various Oracle Database features to improve performance and manageability in a data warehouse.

Learn To:

• Implement partitioning.
• Use parallel operations to reduce response time.
• Extract, transform and load data.
• Create, use and refresh materialized views to improve data warehouse performance.
• Use query rewrite to quickly answer business queries using materialized views.
• Use SQL access advisor and PL/SQL procedures to tune materialized views for fast refresh and query rewrites.
• Identify the benefits of partitioning, in addition to using parallel operations to reduce response time for data-intensive operations.

Benefits To You

Expert instructors will also teach you how to extract, transform and load data into an Oracle database warehouse. Discover how to use SQL Access Advisor to optimize the entire workload. Use materialized views to improve data warehouse performance and learn how query rewrites can improve performance.

Course Topics

• Introduction
• Data Warehouse Design: Overview
• Data Warehouse Tuning Considerations
• Partitioning Basics
• Parallelism Concepts
• Parallel Operations in Data Warehouses
• ETL: Extraction and Transportation
• ETL: Loading
• ETL: Transformation
• Materialized Views
• Refreshing Materialized Views
• Working With Dimensions
• Query Rewrite
• Using the SQL Access Advisor, Compression, and Resumable Sessions

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In this course, participants learn about the best practices for implementing an enterprise data warehouse. Participants identify the hardware elements that influence the throughput and learn how to build a balanced hardware configuration. Participants also review the two classic models: Third normal form (3NF) and star schema and learn how to optimize both models. Participants learn how to convert the logical model into a suitable physical model. Participants also learn how to load data efficiently into the data warehouse and how to use partition exchange load and data compression during the ETL process to improve performance. Finally, participants learn to manage the system workload and resources, to ensure an Enterprise Data Warehouse can run at optimal performance, and explains how parallel execution enables them to fully utilize the system. In addition, participants describe how to gather statistics efficiently.Learn To:

• Describe how to build a balanced hardware configuration
• Discuss and apply the two classic models, the third normal form (3NF) or star schema
• Describe how to convert the logical model into the suitable physical model
• Describe the various methods to load data efficiently into the data warehouse
• Use partition exchange load and data compression during staging to improve performance
• Manage the system workload and resources

A Live Virtual Class (LVC) is exclusively for registered students; unregistered individuals may not view an LVC at any time. Registered students must view the class from the country listed in the registration form. Unauthorized recording, copying, or transmission of LVC content may not be made.

Course Topics

Introduction

• Prerequisites and Suggested Prerequisites
• Questions About You
• Course Objectives
• Course Agenda
• Sample Schemas Used in the Course
• The Sales History (SH) Schema
• Oracle 11g SQL and Data Warehousing Guide Documentation
• Additional Resources: Oracle By Example (OBE)

Building a Balanced Hardware Configuration

• Maintaining High Throughput
• Balanced System
• Components of Hardware Configuration
• Example of a Balanced System: 4-Node RAC Environment
• Data Warehouse Hardware Configuration: Best Practices
• Disk Layout and Design
• Implementing S.A.M.E. Using ASM and Hardware Mirroring

Data Warehouse Logical and Physical Design

• Data Warehouse Models: 3NF and Star
• Industry Best Practices: When to Use Each Model
• The Typical Blueprint of Physical Layers in a Data Warehouse Environment
• Optimizing 3rd Normal Form: Power, Partitioning, Parallelism
• Partitioning Strategies Example: Composite Range-Hash Partitioning
• Partition Pruning
• Partition Wise Join
• Optimizing Star Queries: Star Transformation

Loading Data: Best Practices

• Loading Data into the Warehouse
• The Three Layers of ETL
• Data Loading Methods in a Data Warehouse
• Loading Data from Flat Files into the Data Warehouse Using External Tables
• Pre-Processing in an External Table
• Using Direct Path Load
• Partition Exchange Loading
• Tips for Data Loading: File Size and Formats, Compression, and Statistics 

Managing the System Workload and Resources

• System Management: Keeping the Lights On for Your Data Warehouse
• Scanning a Table in Parallel
• SQL Parallel Execution: Overview
• Parallel Execution In Action
• General Rules on When to Use Parallel Execution
• Workload Monitoring Using (G)V$ Views or Oracle EM 11g
• Oracle Resource Manager
• Gathering Statistics

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