The Entity Relationship Model
Advantages, Disadvantages. Conceptual simplicity; Visual representation; Effective communication; Integration with the relational database. 6 days ago Entity Relationship (E-R) Model; UML (Unified Modelling Language) Data Model; Physical Data Model; Advantages and Disadvantages of. An entity–relationship model (ER model) describes inter-related things of interest in a specific domain of knowledge. - An ER model is composed Advantages Easy conversion to any data model:easily converted into another data model. 3.
Keys are commonly used to join or combine data from two or more tables. For example, an Employee table may contain a column named Location which contains a value that matches the key of a Location table. Keys are also critical in the creation of indexes, which facilitate fast retrieval of data from large tables. Any column can be a key, or multiple columns can be grouped together into a compound key.
What is Data Modelling? Conceptual, Logical, & Physical Data Models
It is not necessary to define all the keys in advance; a column can be used as a key even if it was not originally intended to be one.
A key that has an external, real-world meaning such as a person's name, a book's ISBNor a car's serial number is sometimes called a "natural" key. If no natural key is suitable think of the many people named Brownan arbitrary or surrogate key can be assigned such as by giving employees ID numbers. In practice, most databases have both generated and natural keys, because generated keys can be used internally to create links between rows that cannot break, while natural keys can be used, less reliably, for searches and for integration with other databases.
For example, records in two independently developed databases could be matched up by social security numberexcept when the social security numbers are incorrect, missing, or have changed. Dimensional model[ edit ] The dimensional model is a specialized adaptation of the relational model used to represent data in data warehouses in a way that data can be easily summarized using online analytical processing, or OLAP queries.
In the dimensional model, a database schema consists of a single large table of facts that are described using dimensions and measures. A dimension provides the context of a fact such as who participated, when and where it happened, and its type and is used in queries to group related facts together. Dimensions tend to be discrete and are often hierarchical; for example, the location might include the building, state, and country. A measure is a quantity describing the fact, such as revenue.
It is important that measures can be meaningfully aggregated—for example, the revenue from different locations can be added together.
In an OLAP query, dimensions are chosen and the facts are grouped and aggregated together to create a summary. The dimensional model is often implemented on top of the relational model using a star schemaconsisting of one highly normalized table containing the facts, and surrounding denormalized tables containing each dimension. An alternative physical implementation, called a snowflake schemanormalizes multi-level hierarchies within a dimension into multiple tables.
A data warehouse can contain multiple dimensional schemas that share dimension tables, allowing them to be used together. Coming up with a standard set of dimensions is an important part of dimensional modeling.
Its high performance has made the dimensional model the most popular database structure for OLAP. Post-relational database models[ edit ] Products offering a more general data model than the relational model are sometimes classified as post-relational. The data model in such products incorporates relations but is not constrained by E.
Codd 's Information Principle, which requires that all information in the database must be cast explicitly in terms of values in relations and in no other way —  Some of these extensions to the relational model integrate concepts from technologies that pre-date the relational model.
For example, they allow representation of a directed graph with trees on the nodes. The German company sones implements this concept in its GraphDB. Some post-relational products extend relational systems with non-relational features. Others arrived in much the same place by adding relational features to pre-relational systems.
The resource space model RSM is a non-relational data model based on multi-dimensional classification. Graph database Graph databases allow even more general structure than a network database; any node may be connected to any other node.
MultiValue Multivalue databases are "lumpy" data, in that they can store exactly the same way as relational databases, but they also permit a level of depth which the relational model can only approximate using sub-tables.
Multivalue can be thought of as a compressed form of XML. An example is an invoice, which in either multivalue or relational data could be seen as A Invoice Header Table - one entry per invoice, and B Invoice Detail Table - one entry per line item.
In the multivalue model, we have the option of storing the data as on table, with an embedded table to represent the detail: A Invoice Table - one entry per invoice, no other tables needed. The advantage is that the atomicity of the Invoice conceptual and the Invoice data representation are one-to-one.
The advantage of the Logical data model is to provide a foundation to form the base for the Physical model. However, the modeling structure remains generic. At this Data Modeling level, no primary or secondary key is defined. At this Data modeling level, you need to verify and adjust the connector details that were set earlier for relationships. Characteristics of a Logical data model Describes data needs for a single project but could integrate with other logical data models based on the scope of the project.
Designed and developed independently from the DBMS. Data attributes will have datatypes with exact precisions and length.
Normalization processes to the model is applied typically till 3NF. It offers an abstraction of the database and helps generate schema. This is because of the richness of meta-data offered by a Physical Data Model. This type of Data model also helps to visualize database structure. Characteristics of a physical data model: The physical data model describes data need for a single project or application though it maybe integrated with other physical data models based on project scope.
Data Model contains relationships between tables that which addresses cardinality and nullability of the relationships. Developed for a specific version of a DBMS, location, data storage or technology to be used in the project.
Columns should have exact datatypes, lengths assigned and default values. Primary and Foreign keys, views, indexes, access profiles, and authorizations, etc. Advantages and Disadvantages of Data Model: Advantages of Data model: The main goal of a designing data model is to make certain that data objects offered by the functional team are represented accurately. The data model should be detailed enough to be used for building the physical database.
The information in the data model can be used for defining the relationship between tables, primary and foreign keys, and stored procedures.
Explain different data models with its advantages and disadvantages
Data Model helps business to communicate the within and across organizations. Data model helps to documents data mappings in ETL process Help to recognize correct sources of data to populate the model Disadvantages of Data model: To developer Data model one should know physical data stored characteristics.
This is a navigational system produces complex application development, management. Thus, it requires a knowledge of the biographical truth. Even smaller change made in structure require modification in the entire application.What is database and DBMS .?what is advantage and disadvantages of DBMS
There is no set data manipulation language in DBMS. Conclusion Data modeling is the process of developing data model for the data to be stored in a Database. Data Models ensure consistency in naming conventions, default values, semantics, security while ensuring quality of the data.
There are three types of conceptual, logical, and physical.