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ICTD International Centre for Training and Development

Data Warehousing

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Data Warehousing has become an essential business tool for making informed decisions. This course introduces students to data warehousing concepts and emphasizes a hand on approach to reinforce the theory. A large project is used to design and develop a data warehouse. Star schema, fact tables and dimension tables will be examined. Multi-dimensional databases are emphasized. A team project will be used to handle the process of moving data from an OLTP system to a DW with management reports through the cube and pivotal tables. Microsoft SQL server Analysis Services will be used to develop OLAP cubes and Microsoft Excel for OLAP reporting.

## Course Objectives

  • Explain accepted data warehouse terminology
  • Explain the goals of data warehousing
  • Identify the stages of the data warehousing lifecycle
  • Apply the star schema model to a business case problem
  • De-normalize relational tables into high level summary tables
  • Design and Implement a multi-dimensional data cube using SQL Server Analysis Services

Course Outline

  • Reasons for data warehousing
  • Terminology
  • Data warehousing compared to OLTP
  • Components of a data warehouse
  • The data warehouse lifecycle
  • Managing the data warehouse

Data Warehouse Design -

  • Discussion of how DW database design differs from transactional database design
  • Grain
  • Fact tables and Dimension tables
  • Slowly changing dimension
  • Time dimension
  • Star and snowflake schema
  • De-normalize from OLTP, planned redundancy
  • Data marts and conformed dimensions
  • Strategies for maintaining the data warehouse
  • Metadata in a DW
  • Security in a DW

OLAP Reports with Microsoft Excel -

  • Using PivotTable Service (PTS) to see the business from different perspectives
  • Create a PivotTable from an OLAP cube
  • Drill down, filter, sort, change dimensions
  • Change calculations of a data field
  • Add calculated fields Create a PivotChart from an OLAP cube
  • Change PivotChart type
  • Add a data table to a PivotChart
  • Create Local Cubes
  • Create OLAP-enabled web pages

OLAP Cubes Design with SQL Server Analysis Services -

  • Analysis Services components and architecture
  • Define dimensions with Dimension Editor
  • Dimension levels and hierarchies
  • Define cubes and measure with Cube Editor
  • Process dimensions and cubes
  • Cube storage design
  • Create calculated members
  • Virtual cubes
  • Data Warehouse Partitions
  • Implement drill-through and cube write-back
  • Understand Data Transformation Services (DTS)
  • Understand Data Extraction, Transformation and Loading Services (ETL)

Data Mining -

  • Introduction to data mining terminology and concepts

Course Methodology

A variety of methodologies will be used during the course that includes:

  • (30%) Based on Case Studies
  • (30%) Techniques
  • (30%) Role Play
  • (10%) Concepts
  • Pre-test and Post-test
  • Variety of Learning Methods
  • Lectures
  • Case Studies and Self Questionaires
  • Group Work
  • Discussion
  • Presentation

Who should attend

  • Those who want to review the fundamentals of data warehousing from a best practices standpoint
  • Business and systems managers who are evaluating data warehousing


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