You are here
Course: Dimensional Data Modeling
Because of a lack of offerings in the Belgian market, element61 has developed a training on Dimensional Data Modeling. Details on the training objectives and target audience can be found below. The training is an ideal start for people new to Data Warehousing.
Dimensional models constitute the beating heart of any sound Business Intelligence & Data Warehousing system. In other words the quality and the final acceptance of our Business Intelligence solution is uttermost dependent on the quality of the dimensional models.
In essence dimensional modeling boils down to logically modeling the business requirements. It is however quite different from normalized modeling as we are used to in an ERP/OLTP-like situation.
This 2-day course on dimensional modeling is based on Ralph Kimball’s book, the Data Warehouse Toolkit. The course will take off by explaining the fundamentals in terms of dimensions & facts and the corresponding dimensional process. It will offer a view on tools & templates which can be used to accelerate & document. But it will also delve deeper into amongst others history tracking, the various existing fact-types, multi-valued dimensions, heterogeneous dimensions and facts. Throughout the course special situations are discussed and a number of exercises, based on real-world cases, are given. In short, a full understanding of dimensional modeling techniques, which are required to design a successful relational dimensional model based on the Kimball star schema techniques, is offered.
To avoid confusion, this course will not deal with matters such as architecture or Data Vault techniques.
· Module 1 : Dimensional Modeling for Beginners – 1 day
· Module 2 : Advanced Dimensional Modeling – 1 day
The dimensional training will handle all of the underneath topics.
- Data warehouse introduction
- OLTP versus dimensional modeling
- Dimensional process + methodology
- Facts & dimensions characteristics
- Use of surrogate keys
- Visual aids: information matrix, standardization, naming conventions, data modeling tools
- Dimension examples : De-generate dimensions, aggregated facts as attributes, dimension outriggers, role-playing dimensions, conformed dimensions, junk dimensions, multiple currencies, behavior study groups, variable-depth hierarchies, multi-valued-dimensions, heterogeneous dimensions, factual dimensions
- Fact examples : transactional facts, periodic snapshots, accumulating snapshots, factless facts, aggregates, consolidated facts, normalized facts, heterogeneous dimensions
- Slowly changing dimensions : Type 1, type 2, type 3, type 6
- Star schema versus snowflake schema
- Screening technique
This course is intended towards professionals with very little to no knowledge of modeling in the context of Data Warehousing & Business Intelligence. This includes data architects, data modelers, business analysts, business intelligence designers, project managers, DW/BI developers.
Suggested prerequisites: General Data Warehousing & Business Intelligence knowledge. No dimensional modeling prerequisites.
The training consists mainly of plenary lecturing with a limited set of additional exercises. The course can be taught in both English or Dutch, also on-site at the customers' premises.
Philippe Purnelle holds a Master Degree in Computer & Human Sciences from the ULB. He has been working in Business Intelligence & Performance Management ever since 1999. In his 18 years in Business Analytics, he has been actively involved in providing end-to-end BI and CPM solutions to customers, including Project and Team Management, BI roadmap definition, user requirements analysis, dimensional modelling, data quality, ETL development, database design, Business Intelligence front-end suites, planning and budgeting.
Sven Bracke graduated in 1997 as Bachelor in Computer Sciences at the IndustriÃ«le Hogeschool Brussel. He started his career as Information Analyst at EDS where he designed Oracle applications and developed strong SQL skills on various projects. While getting more interested in Data Warehousing and Business Intelligence, he took up roles as Data Warehouse designer, ETL developer and Business Intelligence analyst. Sven has over 15 years of experience in this area and a proven track record at IBM Cognos, LACO and Deloitte. In April 2017 Sven joined element61 as Principal Business Analytics Architect.
More information ?
For more information, contact Stijn Vermeulen at firstname.lastname@example.org or +32 477 788 033.