If you're new to database architecture, data modeling is a crucial tool that a lot of organizations use to expedite their application development and unlock the value of their data. It's a technique used to define and organize the various processes of your business and enables you to create a more visual description of your organization.
Although data models are pretty technical in nature, they are also designed to be simple and visual, striking a delicate balance between everyday terminology and difficult to digest tech-speak. With the help of data models, everyone on your team can better understand and collaborate on your data more effectively.
What is a Data Model?
A data model is an abstract model that comprises entities defined by properties or attributes and standardizes the relationship between entities, and data modeling is the process of creating a data model for the data to be stored in a database.
To better illustrate what a data model is, let’s use a basic banking application as an example. For this application type, "bank", "branch", "account", and "customer" are all potential entities of its data model. And "account number", "account name", and "account type" can represent common attributes of an account entity. The communication connections between these entities are known as relationships which can be one-to-one, one-to-many, or many-to-many relationships.
A data model specified in data modeling notation clearly defines the structure of the data, and a data model can be a conceptual, logical, or physical.
- Conceptual Data Model - a high-level diagram containing only the entities and relationships, which are primarily designed for the business stakeholders.
- Logical Data Model - describes all the entities, attributes, relationships, unique identifiers, and other key features regardless of how they will be implemented physically.
- Physical Data Model - describes how data is physically stored.
What are the common types of data models?
In this section, we will provide an overview of some of the most common types of data models used today.
Relational Database Model (RDBMS)
Put simply, relational databases store and provide access to data points that are related to one another. Relational databases are based on the relational model, which is a simple and intuitive method of representing data in tables. In this kind of database, each table is composed of rows representing a record with a unique ID called a key. Each table is also composed of columns which hold attributes of the data, and a record usually contains a value for each attribute. This makes it relatively easy to establish the relationships among data points.
Graph Database Models
As the name implies, a graph database models a database in the form of graph. In this graph, nodes form the entities, the edges between nodes represent the relationships between them, and properties represent information associated with nodes. This model is especially useful in modeling more complex relationships.
Key-Value Store Models
Key-value store, or key-value database, is a simple database that uses an associative array as the fundamental data model. With this database model, each key is associated with one and only one value in a collection, and values can range from simple objects to more complex data structures like JSON, HTML, XML, and images. This relationship is referred to as a key-value pair.
Document-Oriented Database Models
Document-oriented databases are a type of key-value store where key-documents pairs are stored, and values represent unstructured documents stored in JSON or XML formats. In this model, relationships are represented as nested data.
Now that we have identified common data models, let’s discuss the advantages and disadvantages of each!
Advantages and Disadvantages of Database Models
Relational Database Model (RDBMS)
- RDBMS provides easy data access. You can query any table directly or join tables to get required data without having to go through a tree or a hierarchy.
- RDBMS has an easy to alter database structure that doesn't necessarily affect the database itself.
- Data types, data validations, and referential integrity ensure the integrity and consistency of data.
- RDBMS consists of authentication, authorization, and access control mechanisms to ensure security.
- RDBMS can become complex when the amount of data increases.
- Lengths of data fields are limited.
- There are high setup and maintenance costs.
Graph Database Model
1. When implemented, graph data models perform well for queries with an increasing amount of data and complexity of relationships among nodes.
2. If you need to make an update like adding a new entity, it can be easily done without damaging the existing model.
1. Most graph database models require that you store all of your data on a single server.
2. Graph databases lack operational features like transactions, rollback mechanisms, and data recovery options.
Key-Value Store Model
1. Its simple data structure makes data read and write operations faster and easier.
2. Key-value store models are flexible when it comes to introducing changes like adding new fields while there are actions in other entries of the database.
3. Key-value store models can be implemented with commodity hardware, and storage and setup costs are less substantial in comparison to other models.
4. When the amount of data increases, key-value stores perform auto sharding to spread data between servers.
5. Secure data through encryption.
- There are no other access mechanisms besides the key.
- There's a lack of consistency when multiple, simultaneous transactions are performed.
- Key-value store models have more limited query capabilities when compared with RDBMS.
Document-oriented Database Model
1. Schemaless and every document can be housed in a different structure.
2. Ability to store large, dynamic, structured, and unstructured data sets.
3. Creation and maintenance is easy.
- Lacks proper security features like strong authentication and encryption methods.
- Data can be inconsistent.
- Not suitable for running complex search queries.
What tools or software are used to create a data model?
While there are many free and open-source tools or software available today that can be used to create data models, here are five of our favorites:
Erwin Data Modeler - This tool can be used to visualize, design, and automatically generate data models like relational and NoSQL databases. It has an easy-to-use interface and allows centralized model development and management and increased data quality.
Lucidchart - This tool provides database diagrams and shapes to define data models. This online and collaborative cloud-based solution eliminates the need to download and install software and enables working offline.
DbSchema - With this modeling tool, you can share your model using Git, deploy the model to multiple databases, open and edit the model offline, and create database reports. It enables modeling for all relational and NoSQL databases (like MySQL, PostgreSQL, MongoDB, MariaDB, and Snowflake), and it works by allowing you to visually interact with the database. Because of its simplicity, you don’t have to be a database expert in order to use it.
Moon Modeler - This data modeling software is predominately for Relational, NoSQL, and GraphQL database models, and it provides quick start guides for each data model type. It's beneficial because:
- It provides you with the ability to design nested structures
- Offers quick and comfortable data modeling
- Supports schema documentation
- Generates validation and creation scripts
- Allows you to connect to existing databases and reverse engineer databases with documents or JSON
Toad Data Modeler - This tool enables modeling database structures and allows you to automatically make changes to existing models. It has several benefits including the ability to reverse and forward engineer databases, the ability to connect to multiple databases simultaneously, in-depth reporting, and model customizations.
The Process of Creating a Data Model
The process of creating a data model will vary according to your organization's standards and requirements, but in this section, we will identify the most common steps performed when creating any data model.
1. Identify Your Entities and Attributes
All data models are made up of entities, the objects or concepts we want to track data for, and they make up the tables in a database. Each of these entities has different attributes, which are details we want to track about entities—you can think of attributes as the columns in a table. If you're in retail, "products", "vendors", and "customers" could all be considered examples of entities in a data model for your business. Underneath the "products" entity, "product name" and "SKU" could be considered attributes.
2. Identify Relationships
It's important to understand that entities do not exist in isolation from one another, in fact, they’re connected to each other. The connections between entities in a data model are called relationships, and relationships generally reflect business rules. Relationships between entities can be one-to-one, one-to-many, or many-to-many. Here's a breakdown for each of these relationships:
- One-to-One Relationship - In a one-to-one relationship, one record in a table is associated with one and only one record in another table. A marriage is a great example of a 1:1 relationship because, by law in the U.S., a husband can only have one wife, and a wife can only have one husband. For example, in a school database, each student has only one student ID, and each student ID is assigned to only one person.
- One-to-Many Relationship - In a one-to-many relationship, one record in a table can be associated with one or more records in another table. For example, a doctor can have many different patients, but a patient usually only has one doctor. This is a commonly existing relationship between entities, and it's indicated by 1:M or 1 : * notation.
- Many-to-Many Relationship - A many-to-many relationship occurs when multiple records in one table are associated with multiple records in another table. Many-to-many relationships can exist between customers and products in the way that customers can purchase various products and products can be purchased by many customers. This relationship can be used for students and courses as well since students can be in multiple courses and courses can have multiple students. M: M or *:* notation is used to depict this relationship.
3. Assign Keys
The purpose of keys in a database is to enforce unique values in an entity and provide a means for sorting the tables to access entity data faster.
- Candidate Keys - This is the minimal set of attributes that uniquely identify an entity, and there can be more than one candidate key for an entity.
- Primary Key - This is a unique attribute that is used to identify one and only instance of an entity. For instance, in an employee table, the employee id becomes the primary key as it uniquely identifies each employee.
- Super Key - You can define a super key as a superset of candidate keys.
- Foreign Key - In a database, a foreign key points to a primary key in another table, and a foreign key must reference a valid primary key in the other entity.
Must-Have Skills for Data Modeling
Although there are many tools to make data modeling easier, it does help to have a few skills up your sleeve:
- Sufficient technical experience using database systems, modeling tools, and technical documentation and a sound knowledge and understanding of relational and NoSQL databases is incredibly useful when it comes to data modeling.
- Ability to quickly learn and adapt to emerging modeling concepts. For successful data modeling, one must be on top of emerging technologies and keep experimenting with them to better understand how to incorporate them into existing models.
- Good communication skills are required as data modelers often need to discuss and present conceptual and logical models to business stakeholders.
- Decision-making skills are also important because the decisions that you make will likely have an impact on the overall model.
Common Mistakes When Creating a Data Model
- Poor Planning - This is a common mistake that people make when creating data models. Data modeling requires well-established goals and a proper understanding of your requirements prior to getting started. If you do not know these things beforehand, you will ultimately waste time and resources figuring them out while you're creating your data model. Changes in requirements often come with directional changes, but incorporating these changes will require minimal effort if you have a proper plan from the beginning.
- Poor or Missing Documentation - Maintaining documentation during model creation is vital to keeping everyone informed and track progress. Some data modelers make the mistake of documenting their work at the end of the project, or in some cases, do not create one at all. When this happens, important information can be missed, so documentation should start as early as possible.
- Poor Naming Standard - Having a consistent naming standard is important because it provides clarity. If there is not a standard naming protocol in place, it may be difficult to interpret the model that you've created. This should be established well in advance of creating the model in order to help standardize your documentation as well.
In conclusion, data models are like blueprints that define the entities, attributes, relationships, and constraints of a database. From conceptual to more physical data models, they all have benefits and drawbacks you should take into consideration when choosing one for your data modeling requirements.
Data modeling can be challenging if you don't know what you're doing, but Crowdbotics has an expert team of professionals that can help make this process easier. If you'd like some additional support, we also offer a managed app development option that takes this process off of your hands completely. To learn more about how we can help, get in touch with us today!