There are many diverse skills that database administrators and developers can bring to the table. They may be expert SQL coders and end up in high demand throughout the department when optimization is required. Maybe that new DBA is very well-versed in cloud migrations and can help the team successfully relocate their database environment.
It could be that they are a data storage wizard and can find ways to maximize how disk resources are used that never occurred to the team before.
A skill that may not immediately spring to mind that can benefit anyone in the field of database support is data modeling. There are several goals of developing a data model when designing or attempting to understand a database system. They include:
Cloud computing has raised the importance of data modeling as the pace of migration to and from the cloud has increased. Another aspect of modern database solutions that make data modeling skills more valuable is the rise of multi-platform environments. When called upon to move a database from an on-premises data center to the cloud or from a specific platform to a different one, effective data modeling is critical to the project’s success.
Database models describe the system’s logical design and structure as well as how the data will be stored, modified, and accessed by users. There are four general categories of data models used by database professionals.
Entity types represent collections of similar physical or abstract objects. An entity type simply represents the data with no regard to the object’s behavior.
Attributes are associated with entities, and each entity must have at least one attribute. Developers usually represent attributes as columns in tables, with multiple columns often used to provide greater detail and control over the data resources.
Naming conventions are essential for understanding and modifying code. Logical and physical data models often have different naming conventions, aligned with the purpose of the respective model.
The relationships between entities are an essential component of data models, and understanding them is a critical skill for modelers.
Normalization organizes the data in a model to develop more cohesion between entity types. Reducing data redundancy is an important step in the normalization process.
IDERA’s ER/Studio Data Architect provides your team with a tool for developing and managing enterprise data models. The application supports a wide range of database platforms and can be instrumental in performing tasks such as standardizing data dictionaries and developing naming standards.
An IDERA whitepaper is available that provides more information about improving your data modeling skills. DBAs who wish to add to their knowledge of data modeling should give it a read. They’re likely to learn something that will contribute to more efficient database design and help them cope with the challenges of working in multi-platform environments.
