Choosing the right data model for optimal data organization and retrieval is a foundational element of effective database design. The relational data model—which uses tables with rows and columns to structure data so that it can be easily queried and retrieved—has been the trusted go-to for agile and efficient business operations for most data professionals, but in some cases the hierarchical data model—which organizes data like a family tree, with parent-child relationships—may be more appropriate.
This comprehensive guide explores these two models in detail, examining their structures, purposes, operational mechanisms, and when to use them effectively in different scenarios.
Hierarchical and relational data modeling paradigms have distinct characteristics, differing significantly in structure, how they represent data, and their operational mechanisms. Here’s a high-level look at how they compare.
| Hierarchical Data Model | Relational Data Model |
|---|---|
| Stores data hierarchically in tree structure; Uses parent-child relationships | Organizes data in table form; Uses common fields to establish relationships between tables |
| Does not support queries | Supports declarative queries |
| Complex and difficult to design | Comparatively easy for users |
| Less flexible | More flexible |
Read Data Modeling vs. Data Architecture: Key Differences to learn more about how data models work and what goes into them.
A hierarchical data model organizes data in a tree-like structure. Data elements are represented as nodes with parent-child relationships. Due to this approach, hierarchical databases are especially adept at representing structured data with well-defined relationships. Each parent can have multiple children, but each child has only one parent.
Hierarchical data models were the first to be adopted in enterprise environments, making their way into various early business computing applications and solutions including file systems, corporate datastores, and certain specialized databases.
In a hierarchical data model, data is organized in a top-down manner, with a single root node at the top. Each subsequent node in the hierarchy has a parent node, and a child node can have multiple parent nodes. Retrieving data in a hierarchical model involves traversing the tree-like structure, starting from the root node and moving down to the desired node—in this capacity, the model is particularly suited for representing data with clear hierarchical relationships.
This hierarchical arrangement and parent node/child node linkage system makes it ideal for representing large volumes of data in a structured manner with well-defined and nested relationships. Hierarchical data models are ideally suited for applications with data that naturally exhibits a hierarchical structure.
A computer file explorer is one of the most well-known examples of hierarchical data models. It uses a tree structure for file systems, where directories act as parent nodes, and files serve as child nodes. A primary use case is organizing and managing data in file systems, where directories and subdirectories are arranged hierarchically.
Hierarchical data models are well-suited for representing organizational structures, such as company hierarchies and organizational charts. They’re also widely used in representing product categories in e-commerce platforms, with parent categories branching into subcategories, allowing for efficient navigation and catalog management.
In recent years, hierarchical data models have been instrumental to the design of information retrieval systems, formats, and standards like XML and HTML. In XML, elements are structured hierarchically to allow for the representation of complex data, such as web content, configuration files, and data exchange between systems. Hierarchical data models are still a viable option for representing and managing data that exhibits clear parent-child relationships, enabling efficient storage and retrieval in various domains.
A relational data model represents data as tables consisting of rows and columns. Each row in a table represents a specific record, while each column represents an attribute or field.
The relational database management system (DBMS) continues to serve as a reliable workhorse for the modern enterprise. Most database administrators and developers are accustomed to the relational data model and its representation of data in tabular form, where data elements are organized into tables with rows and columns.
Read our Guide to Database Management to see how data models fit into the larger picture of enterprise data.
In the relational data model, data is organized into tables. Each table represents an entity and each row represents a specific instance of that entity. Relationships between entities are established through foreign keys, which link one table to another.
This model allows for complex querying and data retrieval using structured query language (SQL). Relational data models emphasize data normalization to reduce redundancy and maintain data integrity, and provide powerful mechanisms for filtering, joining, and aggregating data.
The relational data model is renowned for its simplicity, flexibility, and ability to handle complex data structures efficiently. Subsequently, they are widely used across a diverse range of applications and industries, though perhaps most prominently in business and enterprise use cases and environments.
Most business datasets—for example, customer information, sales transactions, and inventory records—are best represented and managed as relational data types. Organizations that employ relational databases to store, organize, and retrieve this data efficiently have a wealth of tools, knowledge, and resources at their disposal.
Relational data models are prevalent in enterprise resource planning (ERP) and customer relationship management (CRM) systems, as well as web development and e-commerce platforms. Content management systems (CMS), online shopping platforms, and social media websites use relational databases to store user profiles, posts, comments, and other dynamic content. The WordPress CMS, which powers nearly half of all websites on the internet, uses the MySQL relational database as one of its underlying technologies.
Hierarchical and relational data modeling paradigms have distinct characteristics, strengths, and weaknesses. While both models serve the purpose of organizing data, they differ significantly in their structures, data representation, and operational mechanisms.
In terms of data structure, hierarchical data models organize data in a tree-like structure with parent-child relationships. In contrast, relational data models organize data in tables with rows and columns.
Data model relationships represent the connections or associations between different entities or data tables within a database. These relationships define how data from one entity relates to data in another, providing a crucial means to establish connections, dependencies, and constraints in the data structure.
Common types of relationships include one-to-one, one-to-many, and many-to-many, each reflecting the cardinality of how data elements in one entity correspond to those in another. In general, hierarchical data models represent one-to-many relationships effectively, but struggle with many-to-many relationships. Relational data models can handle both one-to-many and many-to-many relationships efficiently through foreign keys and join operations.
Hierarchical data models are better-suited for data applications with clear hierarchical structures; complex data relationships can be difficult to model using a hierarchical approach. Relational data models tend to be more flexible in this regard and adaptable to a wide range of data structures and relationships.
If you look at a typical org chart, it’s not hard to quickly identify the key leaders and decision-makers in the organization. Hierarchical data models are simpler to design and understand for hierarchically structured data, whereas relational data models can handle complex data scenarios but may require more complex queries and joins.
Despite their differences, hierarchical and relational data models share some characteristics. Here are the most common:
Normalization involves organizing data in such a way that it minimizes redundancy and ensures data integrity while maintaining relational connections between tables. One of the primary reasons for data normalization is to reduce data duplication.
Breaking down data into separate tables and eliminating repetitive information saves storage space and enhances data consistency. This not only reduces the risk of data inconsistencies but also makes it easier to update and maintain the database, as changes only need to be made in one place rather than multiple locations.
The choice between hierarchical and relational data models depends on the nature of the data and the specific requirements of your application. The following guidelines and example use cases can help you decide when to use a hierarchical data model.
When representing organizational hierarchies, such as company departments or reporting structures, the hierarchical data model can be effective for clearly conveying levels of rank and priority.
File systems on computers and servers often follow a hierarchical structure, making the hierarchical data model a natural fit for data modeling in this context.
When dealing with data that can be categorized into hierarchical taxonomies, such as product categories or species classifications, hierarchical models work better than relational models for visually representing descent and cascading effects.
Lastly, hierarchical data models are ideal for representing data with clear, relatively simple hierarchical relationships.
The following guidelines and example use cases are better-suited to the relational data model than the hierarchical.
The relational data model is better equipped to handle scenarios with many-to-many relationships, where one entity can be related to multiple others (and vice versa). For example, when dealing with data that involves complex relationships between entities, such as customer orders, products, and suppliers, the relational data model is usually a more flexible option.
Relational data modeling and their RDMBs allow for ad hoc querying and reporting capabilities using SQL-based querying—a significant advantage for data professionals looking to customize their organizations’ business intelligence instruments. And in scenarios where data needs to be integrated from various sources and consolidated into a single database, the relational data model is a more streamlined option for facilitating this process.
Both hierarchical and relational data models have strengths and weaknesses, making them uniquely suitable for specific use cases. While the hierarchical data model excels in representing simple hierarchical structures, the relational data model provides more flexibility and can handle complex relationships effectively.
Ultimately, your decision should be guided by the nature of your data and the requirements of your application/use case—and in many cases, a hybrid approach may also be possible through integrating hierarchical data into a relational database for a best-of-breeds approach.
Read Best Data Modeling Tools and Software to learn about the top products to help enterprises with data modeling needs.
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