entity relationship - Relation between ER Modelling and Database normalization - Stack Overflow
Using a tool such as MySQL Workbench or Toad Data Modeler, depending on your target database vendor, can even generate SQL. Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data. If these relations are stored physically as separate disk files. Consider instead using a star schema (one of the "denormalized" Please refer to this entity relationship diagram (ERD) of a sales star.
However, ERD continues to be popular for conceptual data modeling. Generally a preliminary data model is constructed which is then refined many times.
There are many guidelines rules for refining an ERD. Some of these rules are as follows Mannino, Transform attributes into entity types.
This transformation involves the addition of an entity type and a 1-M one-to-many relationship. Split compound attributes into smaller attributes. A compound attribute contains multiple kinds of data. Expand entity types into two entity types and a relationship.
RDBMS & Graphs: Relational vs. Graph Data Modeling
This transformation can be useful to record a finer level of detail about an entity. Transform a weak entity type into a strong entity type. This transformation is most useful for associative entity types. Add historical details to a data model. Historical details may be necessary for legal as well as strategic reporting requirements. This transformation can be applied to attributes and relationships. Add generalization hierarchies by transforming entity types into generalization hierarchy.
Application of normalization principles toward ERD development enhances these guidelines. To understand this application i representation of dependency concepts in an ERD is outlined, followed by ii representation of normal forms toward the development of entity type structure.
Guidelines for identification of various dependencies is avoided in the paper so as to focus more on their application. Only the first four normal forms and the Boyce-Codd normal forms are considered. Each entity instance represents a set of values taken by the non entity identifier attributes for each primary key entity identifier value.
So, in a way an entity instance structure also reflects an application of the functional dependency concept. Name, Street, City, Zip. Figure 2 Each entity instance will now represent the functional dependency among the entity attributes as shown in Figure 3. Figure 3 During requirement analysis, some entity types may be identified through functional dependencies, while others may be determined through database relationships.
Another important consideration is to distinguish when one attribute alone is the entity identifier versus a composite entity identifier. A composite entity identifier is an entity identifier with more than one attribute. A functional dependency in which the determinant contains more than one attribute usually represents a many-to-many relationship, which is more addressed through higher normal forms.
The notion of having a composite entity identifier is not very common, and often times is a matter of expediency, rather than good entity structure or design. Transitive dependency in an entity type occurs if non entity identifier attributes have dependency among themselves.
For example, consider the modified Student entity type as shown in Figure 4. Figure 4 In this entity type, suppose there is a functional dependency BuildingName?
Fee dependency implies that the value assigned to the Fee attribute is fixed for distinct BuildingName attribute values. In other words, the Fee attribute values are not specific to the SID value of a student, but rather the BuildingName value. The entity instance of transitive dependency is shown in Figure 5. Figure 5 Multi-valued dependency equivalency in ERD occurs when attributes within an entity instance have more than one value. This is a situation when some attributes within an entity instance have maximum cardinality of N more than 1.
When an attribute has multiple values in an entity instance, it can be setup either as a composite key identifier of the entity type, or split into a weak entity type.
For example, consider the following entity type Student Details as shown in Figure 6. The composition of entity identifier is due to the fact that a student has multiple MajorMinor values along with being involved in multiple activities. The multi-valued dependency affects the key structure.
This means that a SID value is associated with multiple values of MajorMinor and Activity attributes, and together they determine other attributes. The more common approach is to denormalize the logical data design[ according to whom? With care this can achieve a similar improvement in query response, but at a cost—it is now the database designer's responsibility to ensure that the denormalized database does not become inconsistent.
This is done by creating rules in the database called constraintsthat specify how the redundant copies of information must be kept synchronised, which may easily make the de-normalization procedure pointless.
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It is the increase in logical complexity of the database design and the added complexity of the additional constraints that make this approach hazardous. This means a denormalized database under heavy write load may actually offer worse performance than its functionally equivalent normalized counterpart. For example, all the relations are in third normal form and any relations with join and multi-valued dependencies are handled appropriately. Examples of denormalization techniques include: For example, in the figure below we see that a great deal of accidental complexity has crept into the model in the form of foreign key constraints everything annotated [FK]which support one-to-many relationships, and JOIN tables e.
A full-fledged relational data model for our data center domain. These constraints and complexities are model-level metadata that exist simply so that we specify the relations between tables at query time.
Yet the presence of this structural data is keenly felt, because it clutters and obscures the domain data with data that serves the database, not the user. The Problem of Relational Data Model Denormalization So far, we now have a normalized relational data model that is relatively faithful to the domain, but our design work is not yet complete. In theory, a normalized schema is fit for answering any kind of ad hoc query we pose to the domain, but in practice, the model must be further adapted for specific access patterns.
This approach is called denormalization. Denormalization involves duplicating data substantially in some cases in order to gain query performance. For example, consider a batch of users and their contact details. A typical user often has several email addresses, which we would then usually store in a separate EMAIL table. Assuming every developer on the project understands the denormalized data model and how it maps to their domain-centric code which is a big assumptiondenormalization is not a trivial task.
Often, development teams turn to an RDBMS expert to munge our normalized model into a denormalized one that aligns with the characteristics of the underlying RDBMS and physical storage tier.
Doing all of this involves a substantial amount of data redundancy. After the cost of this upfront work pays off across the lifetime of the system, right?Normalization - 1NF, 2NF, 3NF and 4NF
Systems change frequently — not only during development, but also during their production lifetimes. Although the majority of systems spend most of their time in production environments, these environments are rarely stable.
Relationships and Data Normalization - DATAVERSITY
Business requirements change and regulatory requirements evolve, so our data models must too. Adapting our relational database model then requires a structural change known as a migration. Migrations provide a structured, step-wise approach to database refactorings so it can evolve to meet changing requirements. Unlike code refactorings — which typically take a matter of minutes or seconds — database refactorings can take weeks or months to complete, with downtime for schema changes.
This conceptual-relational dissonance prevents business and other non-technical stakeholders from further collaborating on the evolution of the system. As a result, the evolution of the application lags significantly behind the evolution of the business. Relational databases — with their rigid schemas and complex modeling characteristics — are not an especially good tool for supporting rapid change.
That model is the graph model. How, then, does the data modeling process differ? In the early stages of graph modeling, the work is similar to the relational approach: Using lo-fi methods like whiteboard sketches, we describe and agree upon the initial domain. After that, our data modeling methodologies diverge.
Once again, here is our example data center domain modeled on the whiteboard: