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

In this course, participants study the issues involved in planning, designing, building, populating, and maintaining a

successful data warehouse. Participants learn the reasons why data warehousing is a compelling decision support

solution in today's business climate. During the course, participants examine warehouse technologies; they also

examine Oracle’s approach to a successful data warehouse implementation by identifying proven Data Warehouse and

Business Intelligence (DW and BI) technologies and tools provided by Oracle. Students are provided tours of Oracle

Warehouse Builder through viewlets. Participants identify features of Oracle10g database, which aid the data

warehouse implementation. Students are given a set of data warehouse implementation templates (documents such as

project management plan, data models, and so on.), they analyze the case study provided to them, and answer the

questions based on the case study. In addition, students will also perform self-guided practices on Analytical Workspace

Manager (AWM) and Oracle Warehouse Builder. Learn To:Define the terminology and explain the basic concepts of

data warehousingIdentify the technology and tools from Oracle to implement a successful data warehouseDefine the

decision support purpose and end goal of a data warehouseDescribe the OLAP and Data mining techniques and

toolsDevelop familiarity with the various technologies required to implement a data warehouseExplain the

implementation and organizational issues surrounding a data warehouse project using a case study

Business Analysts

Business Intelligence Developer

Data Modelers

Data Warehouse Administrator

Data Warehouse Analyst

Data Warehouse Developer

Knowledge of database technologies, client-server

Knowledge relational server technology is suggested

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

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

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

Describing the data warehouse modeling issues

Identifying the data structures for data warehouses

Defining business and logical models

Defining dimensional model

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

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

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

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

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

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

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)

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

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