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.
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.
In this section, we will provide an overview of some of the most common types of data models used today.
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.
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, 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 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!
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.
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.
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.
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:
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 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.
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.
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:
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.
Although there are many tools to make data modeling easier, it does help to have a few skills up your sleeve:
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!
June 22, 2021