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Effective Data Warehouse Construction

  • Price £1,995.00
  • Duration 4 day(s)
All major credit cards accepted


A Data Warehouse has the potential to revolutionise your business. Increased profitability, customer retention, and market penetration are all possible applications of an effective Data Warehouse implementation but first of all you have to build it.
The approach, method and design techniques required to build a Data Warehouse differ significantly from those required to build a database to support on-line transaction processing.

This practical course introduces the delegate to all the necessary terminology, architectures, approaches, skills, tools, techniques, and infrastructure required to design and implement a successful Data Warehouse solution. This is largely achieved by guiding the delegate through an easy to understand, incremental, and iterative process, and also through a series of practical exercises.

Being vendor-neutral, the extensive practical element of this course reflects the natural heterogeneity developers usually face in the real world. The numerous exercises and demonstrations – using technology from some of the world’s leading business intelligence software vendors – make this course far more useful than one which solely focuses on the product set of just a single vendor.

This course is made up of a mix of theory and practicals that allow delegates to apply the principles they have learnt using a variety of industry leading tools. In this rapidly changing area the specific tools may change from time to time. Please contact us for the latest details.

The course is ideal for developers, software engineers, database administrators, data analysts, system analysts or application designers who will be involved in designing, building or maintaining a Data Warehouse.


•Basic understanding of IT and how business systems use IT; this would be gained by at least a year’s experience in IT or business systems development.
•Some exposure to Relational Databases and Database Modelling. Systems/Application programming experience would be an advantage. Database modelling skills can be acquired on the Database Analysis and Design course.


Delegates will learn how to:

•Make a business case, assemble a project team, and lay the infrastructure necessary for the Data Warehouse construction to begin
•Design, implement, utilise, maintain, and administer the Data Warehouse
•Build models using the dimensional (star schema) modelling technique
•Extract data from one or more operational systems
•Transform, condition, and cleanse the data
•Populate the Data Warehouse using a variety of mechanisms
•Query, drill-down, and report the data
•Understand the role of extract tools such as OLAP and data mining
•Understand the importance of metadata and issues surrounding its integration
•Assess your organisation’s readiness to embark on a Data Warehousing project

Course Content

Historical background; What is a Data Warehouse and why do we need it? OLTP versus DSS; Benefits to the business; Benefits to IT; Reasons for failure

The Project
Skills required; Top down vs. bottom up development; Ownership & funding; Methodology; Scoping and requirements gathering; Questions to ask in determining requirements

Review of modelling terminology and techniques; How a Warehouse is different; Logical and physical modelling; 3NF vs. denormalisation

Dimensional Modelling Basics
The dimensional model; The fact table; The dimension tables; Using the dimensional model; Modelling Attributes in the Dimensions; Steps in design

Dimensional Modelling Further Considerations
Conformed Dimensions; Synonym Dimensions; Mini Dimensions; Snowflaking; Changing Dimension Values; Handling Hierarchies; The importance of Surrogate Keys; Aggregates

Hardware Architectures and Database Architectures
SMP; Clusters; MPP; RDBMS, Things to look out for; Database Parallelism; TPC benchmarks

Software Architectures
Centralised Data Warehouse; Independent or Federated Data Marts; Hybrid approach; Standards

Data Extraction
Extraction method; Tool selection guidelines; Metadata; dependencies; The Data Quality Assessment Process

Data Transformation
The Case for data quality; Transformation; Cleansing; Conditioning; Specific data type issues; Transformation methods

Data Loading
How to identify what has changed; Snapshot vs. detail data; Full Refresh vs. Delta Capture; Load methodology; Load techniques
Querying the Data
Canned queries; Report writers; ROLAP and OLAP tools; Data visualisation; Drilldown analysis; End user training; Performance

Data Warehousing and the Internet
Technology overview; Web reporting; Web marts; The Internet as a data source; Extranets; Intelligent agents

Data Mining and Exploration
Data mining methodology; Data minining algorithms; Interpreting the output; Formulating a strategy

Operating and Maintaining the System
Processes and procedures; Change control; User and privilege administration; Service level agreements; Archive and recovery

Being ready for a Data Warehouse; Establishing a plan; Getting the mandate; Training; Feasibility study; Choosing a pilot project

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