Whitepaper : MongoDB and JSON Data Modeling | IDERA

Traditionally, data modeling produces a set of structures for a Relational Database Management System (RDBMS). First, we build the Conceptual Data Model (CDM) to capture the common business language for the initiative (e.g., “What’s a Customer?”). Next, we create the Logical Data Model (LDM) using the CDM’s common business language to precisely define the business requirements (e.g., “I need to see the customer’s name and address on this report.”). Finally, inthe Physical Data Model (PDM), we design these business requirements specific for a particular technology such as Oracle, Teradata, or SQL Server (e.g. “Customer Last Name is a variable length not null field with a non-unique index…”). Our PDM represents the RDBMS design for an application. We then generate the Data Definition Language (DDL) from the PDM, which we can run within a RDBMS environment to create the set of tables that will store the application’s data. To summarize, we go from common business language to business requirements to design to tables.

About the presenter:

Steve Hoberman’s first word was “data”. He has been a data modeler for over 30 years, and thousands of business and data professionals have completed his Data Modeling Master Class. His latest version of the class includes sections on modeling for NoSQL. Steve is the author of nine books on data modeling, including The Rosedata
Stone and Data Modeling Made Simple. Steve is also the author of Blockchainopoly. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, creator of the Data Modeling Institute’s Data Modeling Certification exam, Conference Chair of the Data Modeling Zone conferences, director of Technics Publications, lecturer at Columbia University, and recipient of the Data Administration Management Association (DAMA) International Professional Achievement Award.

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