Understanding the Stages & Types of Data Models: A Comprehensive Guide 101
Data Modeling is the process of cleansing and organizing data into a visual representation or plan that aids in the mapping out of database relationships and operations. Various types of Data models serve as a blueprint for creating an optimal database, regardless of its specific contents.
Table of Contents
A Data Modeler completes the process by directly working with data entities and characteristics to determine their relationships and construct a suitable model. Data architects also work on various types of data models, focusing on the creation of physical blueprints.
This article talks about the different types of Data Models and the process of Data Modeling along with a few Data Modeling Tools.
Table Of Contents
- What is a Data Model?
- Why Create a Data Model?
- Types of Data Models
- Data Modeling Process
- Benefits of Data Modeling
- Types of Data Modeling
- Data Modeling Tools
What is a Data Model?
A Data Model is a graphical technique to construct your database that allows you to start with the big ideas. It provides a uniform framework for representing real-world entities and their attributes. These entities have been reduced to their most basic elements and are frequently linked in some way. Everything is expressed in a fairly straightforward manner, including entities, their properties, and relationships between things. Data models also assist you in describing how entity data is organized, stored, and queried.
The Data Model gives us an idea of how the final system will look after it has been fully implemented. It specifies the data items as well as the relationships between them. In a database management system, data models are used to show how data is stored, connected, accessed, and changed. We portray the information using a set of symbols and language so that members of the organization may communicate and understand it.
Why Create a Data Model?
Creating and maintaining a Data model for your database has numerous advantages. A couple of these types of data models will be outlined and explained further down. The following are the advantages of having a Data model from a business standpoint:
- A Data model creates a common communication layer that makes it easier for the Data Architect and the business folks to communicate. It ensures that all the intricacies can be discussed in detail because it is a visual model with a shared terminology.
- High-quality documentation ensures that the code that is implemented is also of high quality. The code builds on the preceding documentation phase’s decisions, reducing the number of errors.
- Developers can spend more time on feature development because there is less code that needs to be changed. As a result, the amount of time spent coding is reduced, and some costs are eliminated.
- Creating a Data Model allows you to determine the scope of the project. Complexity is decreased and tough topics are comprehended because of the Data model’s visual depiction.
The following are the technical advantages of having a Data Model:
- Technical Layer: A technical layer is attached to a Data model which contains all the technical information (specified by the Data Architect), allowing developers to concentrate on implementation rather than interpretation.
- Lesser Mistakes: There are fewer mistakes. Fewer mistakes are made on the data and application side as a result of the Data model’s clarity and precision. Developers can concentrate on feature development rather than database design.
- Database Structure Optimization: The Database structure can be optimized right from the start before any data is entered. This decreases the amount of data that needs to be moved (i.e. to improve performance after the database is in production).
- Data Risk Reduction: Risks to data are reduced. Data Architects and Database Administrators can develop backup and restore procedures if they have a better understanding of the data’s size. In a disaster recovery scenario, having strategies and safeguards in place decreases risks.
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Types of Data Models
When dealing with the different types of Data models, a variety of stakeholders are engaged. As a result, there are three types of Data models to meet the needs of each stakeholder. The following are the:
- Types of Data Models: Conceptual Data Model
- Types of Data Models: Logical Data Model
- Types of Data Models: Physical Data Model
To develop the database structure, each Data model builds on the previous one.
Database and information system design, like any other design process, starts with a high level of abstraction and gradually gets more concrete and specific. Based on the level of abstraction they give, there are three types of Data models. The approach will begin with a conceptual model and progress to a logical model before arriving at a physical model. The parts that follow go through each of the types of Data models in greater detail.
Types of Data Models: Conceptual Data Model
Conceptual Data Model is the first in types of Data Model. They’re also known as Domain Models, and they provide a big-picture view of the system’s contents, how it’ll be organized, and which business rules will be involved. Typically, Conceptual Models are generated as part of the early project requirements gathering process. Entity classes (which define the types of items that are significant for the company to represent in the Data model), their characteristics and limitations, their relationships, and applicable security and Data Integrity requirements are typically included. Any notation is usually straightforward.
Types of Data Models: Logical Data Model
The Logical Data Model is the second one in the types of Data models. They are less abstract and provide more information on the concepts and relationships in the domain. It follows one of the formal Data Modelling notation systems. Logical data models show the relationships between entities as well as Data properties such as Data types and their respective lengths. Technical system needs are not specified in Logical Data Models. In agile or DevOps techniques, the logical data Modeling stage is frequently skipped. In highly procedural implementation contexts or for projects that are data-oriented by nature, such as Data warehouse design or reporting system development, Logical Data Models might be valuable.
Types of Data Models: Physical Data Model
Physical Data Model is the last one in the types of Data models. They define the format in which data will be physically stored in a database. As a result, they are the least abstract of the bunch. Physical Data Models provide a finished design that can be implemented as a Relational Database, complete with associative tables that show the associations between entities. The primary keys and foreign keys will be utilized to keep those relationships up to date. Physical Data Models might incorporate attributes related to a Database Management System (DBMS), such as performance tweaking.
Data Modeling Process
Data Modeling as a discipline invites stakeholders to examine Data processing and storage in great depth. Different conventions govern which symbols are used to represent data, how models are built up, and how business needs are communicated in Data Modeling methodologies. All approaches provide structured workflows that comprise a list of actions that must be completed in sequential order. An example of a workflow is shown below:
- Determine the entities. The identification of the items, events, or concepts represented in the Data set to be modeled is the first step in the Data modeling process. Each entity should be logically different from the others while maintaining a sense of unity.
- Determine the most important properties of each entity. One or more distinct traits called attributes, separate each entity type from the others. For example, a “customer” object might have a first name, last name, phone number, and salutation, but an “address” entity might have a street name and number, as well as a city, state, nation, and zip code.
- Determine the relationships that exist between entities. In the first draught of a Data model, the nature of each entity’s relationships with the others will be established. In the above example, each client “resides” at a certain address. If the model was expanded to include an object named “orders,” each order would be shipped to and paid to an address. These links are often defined using a single modeling language (UML).
- Complete the attribute-to-entity mapping. This ensures that the model appropriately reflects the company’s data usage intentions. There are a variety of formal Data modeling paradigms in use. Object-oriented developers commonly employ analysis and design patterns, although stakeholders from other business areas may utilize different patterns.
- Assign keys as appropriate, and choose a level of normalization that strikes a balance between the need to decrease redundancy and the necessity for performance. Normalization is a method of structuring the various types of Data models (and the databases they represent) in which numerical identifiers, known as keys, are allocated to groupings of data to indicate relationships between them without having to repeat the data. For example, if each client is given a key, that key can be linked to their address and order history without having to duplicate the information in the customer database. Normalization reduces the amount of storage space required by a database, but at the expense of query performance.
- Complete and test the different types of Data models. As business demands evolve, Data modeling is an iterative process that should be repeated and updated.
Benefits of Data Modeling
Developers, Data Architects, Business Analysts, and other stakeholders can examine and comprehend relationships among data in a Database or Data warehouse using Data Modeling. Furthermore, it has the ability to:
- Reduce software and database development errors.
- Increase enterprise-wide consistency in documentation and system architecture.
- Enhance the performance of your application and database.
- Streamline Data mapping across the organization.
- Improve communication between the development and Business Intelligence teams.
- Make database design easier and faster at the conceptual, logical, and physical levels.
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Types of Data Modeling
As enterprises’ data storage needs have expanded, Data Modelling has evolved alongside Database Management Systems, with model types becoming more complicated. Listed below are a few different types of models:
- Hierarchical Data Models: In a treelike style, Hierarchical Data Models represent one-to-many relationships. Each record in this architecture has a single root or parent table that translates to one or more child tables. This paradigm was adopted in the IBM Information Management System (IMS), which was released in 1966 and quickly became popular, particularly in the banking industry. Although this method is less efficient than more recently established Database models, it is nevertheless utilized in XML systems and Geographic Information Systems (GISs).
- Relational Data Models: E.F. Codd, an IBM researcher, first introduced Relational Data Models in 1970. They’re still employed in today’s relational databases, which are widely used in enterprise computing. Relational Data modeling does not necessitate a thorough knowledge of the physical aspects of the Data storage system. It reduces database complexity by explicitly joining Data segments through the use of tables.
- Entity-Relationship (ER) Data Models: ER Data Models represent the relationships between entities in a database using formal diagrams. Data architects utilize a variety of ER modeling tools to produce visual maps that communicate database architecture goals.
- Object-oriented Data Models: These various types of Data models became popular at the same time as object-oriented programming in the mid-1990s. The “things” in question are fictitious representations of real-world entities. Objects are organized into hierarchies of classes, each with its own set of attributes. Tables can be included in object-oriented databases, but they can also handle more sophisticated data interactions. This method is used in multimedia and hypertext databases, among other applications.
- Dimensional Data Models: Ralph Kimball created Dimensional Data Models, which were created to speed up data retrieval for analytic purposes in a Data Warehouse. While relational and ER models focus on efficient storage, Dimensional models include redundancy to make it easier to find information for reporting and retrieval. This type of modeling is common in OLAP systems.
The Star Schema, in which data is structured into facts (measurable elements) and dimensions (reference information), and each fact is encircled by its corresponding dimensions in a star-like pattern, is a prominent Dimensional Data model. The other is the Snowflake Schema, which is similar to the star schema but incorporates additional layers of linked dimensions, increasing the complexity of the branching pattern.
Data Modeling Tools
Today, a variety of commercial and free source Computer-Aided Software Engineering (CASE) solutions, including Data modeling, diagramming, and visualization tools, are extensively utilized. Following are a few examples:
- Erwin Data Modeler is a Data Modelling tool that supports alternative notation approaches, including a dimensional approach, and is based on the Integration DEFinition for information modeling (IDEF1X) Data modeling language.
- Enterprise Architect is a visual modeling and design tool for enterprise information systems, architectures, software applications, and databases. It is built on the foundation of object-oriented languages and standards.
- ER/Studio is a database design software that works with several common database management systems today. Both relational and dimensional data models are supported.
- Open Source Solutions like Open ModelSphere are examples of free Data modeling tools.
As a software developer or Data Architect, creating Data models will be beneficial. Knowing when to employ the appropriate data model and how to include business stakeholders in the decision-making process can be quite beneficial. In this article, you learned about the different types of Data Models and the Data Modeling process.
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