One of the ways to ensure that the database remains flexible in the types of questions that you can ask, is by making the fields as descriptive and as specific as possible.
- [Instructor] Part of the benefit of having…your data stored in a relational database…is that it becomes very easy to ask…a wide variety of questions about your data.…That is, as long as the data structure is well considered.…One of the ways to ensure that the database remains…flexible in the types of questions that you can ask,…is by making the fields as descriptive,…and specific, as possible.…A side effect of this effort is that the fields also…become quite short and to the point.…Here's a typical example of how our customer data…might be stored in a spreadsheet.…In the customers table, we're currently storing…the name of the customer and their billing address.…
And once again, we have a storage structure…that works perfectly fine for our immediate business needs.…Given this information, we would be able to process…a customer's credit card since we have their billing address…stored and easily accessible.…What's being lost though, is any future or alternate…use of this information, outside of billing a credit card.…
Adam Wilbert covers the basics of relational database design, regardless of whether you use Access, FileMaker, Open Office, or SQL Server. Learn how to prevent data anomalies, gather requirements to plan your design, and develop a conceptual data model—translating your ideas into components like tables, relationships, queries, and views. Plus, learn about logical design considerations that can help you construct a database that is easy to maintain.
- What is a database management system (DBMS)?
- Moving through the database development cycle
- Preventing duplicate, inconsistent, and conflicting data entries
- Gathering requirements
- Developing relationships
- Identifying key fields
- Following a naming convention
- Developing the actual database
Skill Level Beginner
1. Relational Database Basics
Relational structures3m 46s
2. Preventing Data Anomalies
3. Gathering Requirements
4. Developing the Conceptual Data Model
5. Normalizing Your Data
6. Logical Design Considerations
7. Developing the Physical Database
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