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Data modeling is a set of techniques that are fundamental to the processes of understanding, designing, implementing, and curating data. Despite the many declarations that “data modeling is dead” it continues to be an essential part of effective data management. Data Modeling is not dead, but the practices of data modeling are different today than in the past.
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Once the domain of database designers and developers, data modeling is now an important skill for data engineers, data scientists, data analysts, application developers, and data curators. Modeling for traditionally structured data is now extended to encompass the variety of big data and NoSQL data types. Semantic data modeling simplifies data integration and is essential to achieve data interoperability. In today’s data modeling practices, long-standing modeling techniques are combined with new and different techniques to adapt to variety in data and data management use cases.
This 4 hours 45 minutes course teaches practical data modeling skills ranging from traditional relational modeling to key-value, document, graph, and semantic data modeling.
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You will learn:
- What data modeling is and why it is important for modern data management
- Data modeling use cases and the roles of data modeling in data management
- Top-down data modeling for data requirements analysis and database design
- Data model reverse engineering to explore, understand, and describe existing data
- Entity-Relationship data modeling techniques
- Multi-dimensional modeling techniques
- NoSQL data modeling techniques including key-value, document, and graph data modeling
- Semantic data modeling techniques including modeling of ontologies and taxonomies
This course is geared towards:
- Data engineers responsible to design, build, and support databases of all types
- Data engineers responsible to design, build, and support data pipelines
- Data analysts, data scientists, and data engineers who need to investigate, understand, and document data
- Data architects responsible for data standards, data interoperability, and data integration
- Data warehouse and data lake architects, designers, developers, and implementers
- Master data management (MDM) architects, designers, developers, and implementers
- Application systems architects, designers, developers, and implementers
- Anyone with responsibility for or interest in data modeling
Module 0. About the Course (2 min)
Module 1. Introductions to Data Modeling (37 min)
- What is Data Modeling?
- Why Data Modeling is Needed?
- Levels of Data Modeling
- Kinds of Data Models
- Module Summary
Module 2. Entity-Relationship Modeling (59 min)
- Entity-Relationship Modeling Basis
- Conceptual Modeling
- Logical Modeling
- Physical Modeling
- Module Summary
Module 3. Multi-Dimensional Data Modeling (51 min)
- Multi-Dimensional Modeling Basics
- Conceptual Modeling
- Logical Modeling
- Physical Modeling
- Dimension Design Techniques
- Module Summary
Module 4. NoSQL Data Modeling (69 min)
- NoSQL Modeling Basics
- Key-Value Data Modeling
- Document Store Data Modeling
- Graph Data Modeling
- Module Summary
Module 5. Semantic Data Modeling (64 min)
- Semantic Modeling Basics
- Modeling Ontology
- Modeling Taxonomies
- The Enterprise Semantic Model
- Module Summary
Click
- here - to download a more detailed outline of this course.
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This exam tests knowledge and understanding of basic concepts, principles, and terminology of analytics.
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You will be tested in these areas:
- Kinds of data models and levels of data model abstraction
- Entity-Relationship data modeling techniques at conceptual, logical, and physical levels
- Identifying and modeling entities, relationships, and attributes
- Relationship cardinality
- Normalization and abstraction in relational data modeling
- Multi-dimensional data modeling techniques at conceptual, logical, and physical levels
- Identifying, modeling, and mapping measurement subjects and measurement categories
- Identifying and modeling meters, measures, dimensions, and dimension hierarchies
- Physical dimensional modeling and star-schema design
- NoSQL data modeling basics
- Key-value data modeling including kinds of key-value stores
- Graph data modeling techniques including identification and modeling of nodes, edges, and properties
- Semantic data modeling techniques including modeling of ontologies and taxonomies
- Enterprise semantic modeling and semantic data layer
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Additional Information
Number of Questions: 25
Time Limit: 50 minutes
Passing Score: 70%
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Once you pass the exam, you will receive a Certificate of Education
documenting that you have demonstrated mastery of the topic. Course
exams count towards eLC certification programs. Visit our
Certification page for more information about our various programs.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.
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Course Review 
