Implementing data warehouse methodology

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This course is designed to answer to all main questions in dimensional modeling. Dimensional database is designed and tuned to support the analysis of business trends and projections. It is optimized for data retrieval and analysis and it’s used in data warehouse design.

SUMMARY

Almost every company in a world is using some kind of a transactional database. Transactional databases are designed to meet the day-to-day operational needs of the business and the database performance is tuned for those operational needs. Since transactional database is more oriented on data entry than analysis, it can be very slow and inadequate if you need to retrieve a large number of records and summarize data on the fly especially from different modules, business segments and data sources. When the need for reporting and data analysis occurs, transactional databases provide too slow and complicated data access so in this case, a different approach is applied.

Dimensional modeling is focused on how to design proper data warehouse structure based on dimensional modeling principles and best practices? What are the key design steps?

There are different approaches to the modeling and the question is how to decide which one is better in a given situation? What to look for and how to solve some most common design issues such as how to track slowly changing dimensions?

This course is designed to answer to all main questions in dimensional modeling. Dimensional database is designed and tuned to support the analysis of business trends and projections. It is optimized for data retrieval and analysis and it’s used in data warehouse design. This course explains how to transform transactional database to a dimensional one and what steps to follow. You will get an insight into different modeling approaches and various practical ways to handle common designing issues.

 

OBJECTIVES

This course will give you a basic knowledge of dimensional data modeling with different concepts and methodologies. It contains definitions of all terms necessary for the understanding dimensional model and modeling principles. Also, it describes how to create star scheme specifying main advantages and disadvantages of star scheme. Different approaches to modeling will be described focusing on Kimball’s and Inmon’s modeling techniques. Some practical issues will be also covered – such as definition and treatment of slowly changing dimensions. In the end, we will explain Data Vault modeling method that supports an agile approach to development. During the training, we will cover topics which can help in DWH design process as well as some practical examples and recommendations.

 

AUDIENCE

Everyone who wants to have overview and understanding of the key concepts and methodology of dimensional moThe targetedTargeted audience includes Analytics Professionals, Project Managers, Business intelligence application developers, ETL developers and all others who are looking to get basic knowledge of Dimensional modeling.

PREREQUISITES: None. No programming experience is required for this training.

CONTENT

  • Introduction
  • Star and snowflake schema
  • Different approaches to dimensional modeling
  • Steps of dimensional modeling
  • Slowly changing dimensions
  • Data Vault
  • Practical examples
  • End of module workshop


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