Check-In: 08:30 - 09:00
Program:  09:00 - 17:00

Registration

TDWI Data Modeling: Data Analysis and Design for BI and Data Warehousing Systems (Part 1)

Business intelligence and data warehousing systems challenge the proven data modeling techniques of the past. From requirements to implementation, new roles, uses, and kinds of data demand updated modeling skills. The data modeler’s toolbox must address relational data, dimensional data, unstructured data, and master data. For those with data modeling experience, this course extends their skills to meet today’s modeling challenges. Those new to data modeling are introduced to the broad range of modeling skills needed for BI/DW systems. Those who need to understand data models, but not necessarily develop them, will learn about the various forms of models and what they are intended to communicate.

You Will Learn

• The role of business requirements in BI data modeling
• Differences in modeling techniques for business transactions, business events, and business metrics
• The role of source data analysis in data modeling
• Use of relational modeling techniques for data warehouse analysis and design
• Use of dimensional modeling techniques for data warehouse analysis and design
• Implications of unstructured data
• The roles of normalization and abstraction in data warehouse design
• The roles of identity and hierarchy management in data warehouse design
• How time-variant data is represented in data models
• Implementation and optimization considerations for warehousing data stores

Geared To

• Data architects
• Data modelers
• BI program and project managers
• BI/DW system developers

Check-In: 08:30 - 09:00
Program:  09:00 - 17:00

Registration

TDWI Data Modeling: Data Analysis and Design for BI and Data Warehousing Systems (Part 2)

Business intelligence and data warehousing systems challenge the proven data modeling techniques of the past. From requirements to implementation, new roles, uses, and kinds of data demand updated modeling skills. The data modeler’s toolbox must address relational data, dimensional data, unstructured data, and master data. For those with data modeling experience, this course extends their skills to meet today’s modeling challenges. Those new to data modeling are introduced to the broad range of modeling skills needed for BI/DW systems. Those who need to understand data models, but not necessarily develop them, will learn about the various forms of models and what theyare intended to communicate.

You will learn:

• The role of business requirements in BI data modeling
• Differences in modeling techniques for business transactions, business events, and business metrics
• The role of source data analysis in data modeling
• Use of relational modeling techniques for data warehouse analysis and design
• Use of dimensional modeling techniques for data warehouse analysis and design
• Implications of unstructured data
• The roles of normalization and abstraction in data warehouse design
• The roles of identity and hierarchy management in data warehouse design
• How time-variant data is represented in data models
• Implementation and optimization considerations for warehousing data stores

Geared To

• Data architects
• Data modelers
• BI program and project managers
• BI/DW system developers

Check-In: 08:30 - 09:00
Program:  09:00 - 17:00

Registration

TDWI Advanced Data Modeling Techniques

Whether you are a business data modeler who represents data requirementsand entities and relationships, or a physical data modeler more concerned with tables, columns, and indexes, you know that the hard stuff lies beneath the surface. Every data design, whether logical or technical, is challenged by one or more complex considerations— scalability, adaptability, performance, legacy and package databases, etc. Every data model raises questions. Advanced modeling techniques provide many of the answers.

You will learn:

When, where, and how to apply advanced modeling techniques, including:

• Normalization and denormalization
• Abstraction, patterns, and universal models
• Generalization, specialization, and inheritance
• Time and time dependency in the data model
• States and state dependency in the data model
• Recursion for lists, trees, and networks
• Complementary models—process, state-transition, use cases, and event maps
• Advanced indexing and outer join optimization
• Data model validation and testing

Geared To

• Data modelers with some practical experience
• Data architects
• Database developers

Maritim Hotel Frankfurt                                                                                   
Theodor-Heuss-Allee 3
60486 Frankfurt/Main (Germany)
Phone: +49 (0) 697578-0
Room Rate: € 135,00