Define the terminology and explain the basic concepts of data warehousing. Define the decision support purpose and end goal of a data warehouse. Develop familiarity with the various technologies required to implement a data warehouse. Identify the technology and tools from Oracle to implement a successful data warehouse. Describe methods and tools for extracting, transforming, and loading data. Identify the tools for accessing and analyzing warehouse data. Identify the new features of
Oracle Database 10g that aid in implementing the data warehouse. Describe the OLAP and Data mining techniques and tools. Explain the implementation and organizational issues surrounding a data warehouse project
Data Warehousing and Business Intelligence
Understanding the evolution of data warehouses from MIS
Describing the differences between OLTP and OLAP
Identifying the business drivers for data warehouses
Identifying the role of business intelligence in today's market
Recognizing the tools and technology from Oracle
Identifying the components of Oracle E-business Intelligence
Defining Data Warehouse Concepts and Terminology
Identifying a common, broadly accepted definition of a data warehouse
Describing the differences between dependent and independent data marts
Identifying some of the main warehouse development approaches
Recognizing some of the operational properties and common terminology of a data warehouse
Exploring the case study introduced
Business, Logical, and Dimensional Modeling
Describing the data warehouse modeling issues
Identifying the data structures for data warehouses
Defining business and logical models
Defining dimensional model
Physical Modeling: Sizing, Storage, Performance, and Security Considerations
Describing how to translate the dimensional model to physical model
Explaining data warehouse sizing techniques and test load sampling
Describing data warehouse partitioning methods
Understanding indexing types and strategies
Explaining parallelism in data warehouse operations
Explaining the importance of security in data warehouses
Identifying the tools and technologies provided by Oracle
The ETL Process: Extracting Data
Outlining the ETL (Extraction, Transformation, and Loading) processes for building a data warehouse
Identifying ETL tasks, importance, and cost
Explaining how to examine data sources
Identifying extraction techniques and methods
Identifying analysis issues and design options for extraction processes
Listing the selection criteria for the ETL tools
Describing Oracle's solution for ETL process
The ETL Process: Transforming Data
Defining transformation
Identifying possible staging models
Identifying data anomalies and eliminate them
Describing the importance of data quality
Describing techniques for transforming data
Listing Oracle's features and tools that can be used to transform data
The ETL Process: Loading Data
Explaining key concepts in loading warehouse data
Outlining how to build the loading process for the initial load
Identifying loading techniques
Describing the loading techniques provided by Oracle
Identifying the tasks that take place after data is loaded
Explaining the issues involved in designing the transportation, loading, and scheduling processes
Refreshing Warehouse Data
Describing methods for capturing changed data
Explaining techniques for applying the changes
Describing the Change Data Capture mechanism and refresh mechanisms supported in Oracle10g
Describing the techniques for purging and archiving data and outlining techniques supported by Oracle
Outlining the final tasks, such as publishing the data, controlling
access, and automating processes
Summary Management
Discussing summary management and Oracle implementation of summaries
Describing materialized views
Identifying the types, build modes, and refresh methods for materialized views
Explaining the query rewrite mechanism in Oracle
Describing the significance of Oracle dimensions
Leaving Metadata Trail
Defining warehouse metadata, its types, and its role in a warehouse environment
Developing a metadata strategy
Outlining the Common Warehouse Meta-model (CWM)
Describing Oracle Warehouse Builder's compliance with OMG-CWM)
OLAP and Data Mining
Defining Online Analytical Processing
Comparing ROLAP and MOLAP
Describing the benefits of OLAP and RDBMS integration
Describing the benefits of OLAP for end users and IT
Define data mining
Describe the tools and technology from Oracle for OLAP and data mining
Data Warehouse Implementation Considerations
Describing the project management plan
Specifying the requirements for the implementation
Describing the metadata repository, technical architecture and other considerations
Describing post implementation change management considerations
