2017-11-06 Kurs Data Modeling: Operational, Data Warehouse, Data Marts & Big Data (3 dagar, Stockholm)
Data Modeling for Operational Systems, Data Warehousing, and Data Marts
Teacher: Hans Hultgren.
This course covers the core principles of data modeling through lectures and hands-on labs and exercises. Providing a solid overview of current techniques for modeling operational systems, data warehouses, and data marts.
In covering these areas, the course considers data modeling and design using normalized data modeling 3rdNormal Form for Operational Systems, Ensemble Data Vault modeling for the Data Warehouse, and Dimensional modeling (Star Schema) for Data Marts.
3NF Modeling. The course will start with the core fundamentals of database design including identifying the core entities and relationships, creating logical model designs, defining attributes and key structures, and developing entity relationship diagrams (ERDs). Within the scope of this modeling approach, the lessons will cover business rules, normalization, validation rules, reference tables, key constraints, identifying and non-identifying relationships, recursive relationships, redundancy, subtypes and supertypes, and relationship cardinality. Students will become comfortable with the core rules and best practices approach to 3NF modeling.
DataVault Modeling. The training will continue with the fundamentals of data warehouse modeling for the enterprise data warehouse (EDW). Within this section the core concepts of data warehousing are presented including the focus on integrated, non-volatile, time-variant and subject oriented data. Ensemble Modeling techniques are optimized for these requirements. The most popular of these, the Data Vault modeling approach, is presented including business keys (Hubs), relationships (Links) and context/history (Satellites). The course continues with core business concepts, ensembles, unified decomposition, concept constellations, and natural business relationships. The course considers how to model your enterprise data warehouse, modeling techniques for agility, operational support, auditability, and enterprise data integration. Lessons, exercises and labs are focused on best practices for architecting and modeling your data warehouse for long term success.
Dimensional Modeling. Next the training will cover dimensional data modeling based on current best practice interpretations of the Kimball Star Schema dimensional modeling approach. First lessons will begin with the fundamentals of dimensional modeling including the purpose and structure of Facts and Dimensions, denormalization, the concept of Slowly Changing Dimensions (SCDs) and the main Dimension Types (Type 2 and also covering Type 0, 1, 3, 4 and 6), and then the modeling and design of solid dimensions and encouraged forms of Star Schemas. The course continues with defining and designing Snow Flake models, the encouraged and acceptable practices for deploying these concepts. Lastly, the course will cover physical data model considerations and DW/BI deployment topics.
Modern Data Modeling. Applying these techniques now and into 2020 includes a fresh discussion on Conceptual Modeling, Logical Modeling and Physical Data Modeling/Management. These topics are presented including how they relate to modern architectures such as Big Data platforms and Cloud deployments. This includes a discussion of Ensemble Logical Form (ELF) as applied to these new architectures. This topic also addresses the role of Data Modeling for the Data Scientist.
The Target Audience for this class includes Data Modelers, Business Analysts, Data Analysts, DWBI Managers, Data Architects, Database/Data Warehouse Designers, and Data Scientists. Note that unlike other data modeling courses, this course takes a global view of database across the organization to include the enterprise initiatives (big data, data warehousing and business intelligence).
Course Fee: 21500 SEK (18500 Early Bird, sign up six weeks in advance) excluding VAT.
Associated labs, exercises and assessments are included with the course materials