Geek Sync Webcast : ER/Studio Data Architect

Geek Sync | A Photographer and a Data Modeler Walk Into a Bar...

Presenter: Steve Hoberman

Creating an excellent data model involves guidance and considerations:

  • Understand the business requirements: Analyze the business needs and requirements, as this will help you create a data model that represents the domain and supports the desired functionality.
  • Use a consistent naming convention: Adopt a consistent naming convention for tables, columns, and other database objects. This makes it easier to understand the data model and maintain it.
  • Normalize the data: Apply normalization principles to reduce data redundancy and improve data integrity. This involves organizing the data into tables with well-defined relationships.
  • Use appropriate data types: Choose the correct data types for each attribute, considering the data and the required storage space.
  • Define primary and foreign keys: Define primary keys to identify each record in a table, and use foreign keys to establish relationships between tables.
  • Create indexes: Use indexes to improve query performance for large data sets. Be mindful of the trade-offs between indexing and write performance.
  • Implement constraints: Use constraints (e.g., unique, check, and not null constraints) to enforce data integrity rules and maintain data quality.
  • Document the data model: Provide clear documentation, including entity-relationship diagrams and data dictionaries, to help users and developers understand the data model.
  • Plan for scalability and performance: Consider the expected data volume and query patterns and design the data model to support efficient querying and data manipulation.
  • Iterate and refine: Review and refine the data model as the business requirements develop or as new insights emerge. This may involve denormalization, partitioning, or other optimization techniques.

Join IDERA and Steve Hoberman as he covers five important settings the data modeler must be aware of to produce a great data model. In addition, he will summarize the three levels of modeling using an analogy we can all relate to. If you’ve ever taken a photograph, you will appreciate the connection between a stunning photograph and a robust meaningful data model.

About Steve:

Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style, and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. 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 and Conference Chair of the Data Modeling Zone conference.

Topics : Data Modeling,

Products : ER/Studio Data Architect,

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