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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