Hierarchical Database vs Relational Databases: Differences & Similarities

The choice of a suitable database architecture is essential for optimizing your organization’s data operations. Organizing your data into a structured format can help with effective management and analysis. This will make it easier to produce actionable insights. Among the many available database models, hierarchical and relational databases are prominent ones.

Hierarchical databases are one of the earliest data storage models, and they continue to be relevant in many organizations. In comparison, relational databases are prevalent in modern applications, enabling you to maintain complex relationships between different data elements.

But what are the main aspects that one must consider before selecting one of these databases?

This article outlines the key differences between a hierarchical database vs relational database, highlighting the pros and cons of each type.

What Is a Hierarchical Database?

A hierarchical database is a data storage model where information is organized in a tree-like structure with multiple nodes. In this model, there are two types of nodes, parent and child, which exist on different hierarchical levels. Each parent node can connect to one or more child nodes, establishing a one-to-many relationship to specify data relationships.

Pros & Cons of a Hierarchical Database

Pros:

• The one-to-many relationship between different data elements makes it easier to understand and navigate the structure.
• In this database model, tables are separated from physical storage, which simplifies the process of adding or deleting information without affecting the entire database.
• Most programming languages support reading data from hierarchical databases, enhancing accessibility.‍

Cons:

• The one-to-many relationship restricts the ability to handle complex data structures where a single child node may have multiple parent nodes.
• Searching for an element is time-consuming as this model requires you to perform a top-to-bottom sequential search.

Examples of a Hierarchical Database

• Hierarchical databases are suitable for representing organizational structures and company hierarchies in organizational dashboards.
• A real-world example where hierarchical databases are used is the computer file explorer. Representing directories as parent nodes and files as child nodes makes it easier for you to manage data.
• Information retrieval systems that use XML and HTML are based on hierarchical data models to organize web content hierarchically.

What Is a Relational Database?

Relational databases store data in a tabular format consisting of rows and columns. The rows highlight records—a collection of related values—with each record having a unique identification key. Columns represent attributes of a specific data type. For example, an e-commerce relational database can contain rows of customer transactions, with columns representing customer ID, product ID, and order date.

Pros & Cons of a Relational Database

Pros:

• By supporting many-to-many formats, relational databases allow you to create robust relationships between different elements.
• Relational databases are independent of physical storage, which makes it easier to store data without hardware dependencies.
• Any changes in the database structure don’t affect its accessibility. You can use Structured Query Language (SQL) with a relational Database Management System (DBMS) to query data.‍

Cons:

• Designing relational databases can be more complex and time-consuming compared to simpler data storage models.
• There may be performance issues when querying data from multiple tables, especially with complex operations like joins.

Examples of a Relational Database

• Many modern businesses use relational databases to store and organize transactional, customer, and product data.
• Relational databases are predominantly used in Enterprise Resource Planning (ERP) to manage core business processes, including finance, human resources, and inventory.
• Social networking websites utilize relational databases to store user information, such as profiles, posts, comments, and interactions.

Hierarchical Database vs. Relational Database

Let’s explore the key differences between hierarchical vs relational database.

Are There Any Similarities Between Hierarchical & Relational Databases?

Despite the relational database vs hierarchical database differences, they share some similarities.

• Search Operations: Both database types support search operations to locate specific elements, although the methods and tools used may differ.‍
• Data Storage and Retrieval: With the correct implementation, both models allow you to efficiently store and retrieve information to maintain data consistency and accuracy.

When to Choose a Hierarchical Database?

• The hierarchical model is preferable for working with and modeling file systems on your machine or server.
• When representing organizational hierarchy, such as departmental relationships and reporting hierarchies, this data model can effectively convey priorities and dependencies.
• With this database model, you can efficiently organize hierarchical taxonomy information, like species categorization and object classifications.

When to Choose a Relational Database?

• A relational database is preferable in scenarios involving many-to-many relationships between data elements.
• If your tech stack includes a SQL database, using relational databases would be advantageous.
• With relational databases, you can perform ad hoc queries and detailed reporting operations using SQL.

Can You Use Hierarchical & Relational Databases Together?

Yes, you can use hierarchical and relational databases together, depending on the specific use case. In situations that require you to store data in a tree-like structure, you can use hierarchical databases. An instance is if you’re working on applications that require some categorization, such as organizing books by subcategories and genres in a library.

You can then use relational databases to enrich and manage detailed information about each category from the hierarchical data. Based on your needs, you can migrate data from a hierarchical to a relational database using SQL queries. This enables you to maintain a robust data management system within your organization.

How Well Do Both Databases Integrate with Modern Technologies?

One of the differentiating factors between hierarchical database vs relational models is their integration capabilities.

Integrating hierarchical databases with modern applications can be challenging. The rigid tree-like data structure of this database type makes it less flexible. This is among the main reasons why applications these days rely more on relational databases.

It is easier to integrate relational databases with other applications, as they provide a well-structured data format aligning with the requirements of most modern organizations. The integration capabilities of relational databases have significantly evolved to satisfy the demands of complex workflows.

However, to build custom integrations, you might have to write extensive code, work with APIs, and manually perform data transformations. To overcome this challenge, you can use SaaS-based tools like Airbyte.

Airbyte is a no-code data integration tool that allows you to build robust data pipelines effortlessly. It offers over 400+ pre-built connectors, which can help you move structured, semi-structured, and unstructured data across multiple platforms, including MySQL, PostgreSQL, and SQL Server.

If the connector you seek is unavailable, you can build a custom connector using Airbyte’s Connector Development Kits (CDKs) and Connector Builder.

Here are a few features of Airbyte that can simplify your data replication journey:

• AI-Enabled Connector Builder: The Connector Builder comes with an AI assistant that automatically reads through your preferred platform’s API documentation and auto-fills most configuration fields. This feature makes it almost effortless for you to build custom connectors.‍
• Schema Management: You can mention how Airbyte must handle source schema changes to maintain data accuracy and consistency. This feature allows you to automate schema checks every 15 minutes for cloud users and every 24 hours for self-hosted accounts.‍
• Change Data Capture: Using Airbyte’s CDC functionality, you can automatically identify and replicate source data changes into the destination. This feature allows you to keep track of records and maintain data consistency.‍
• Vector Database Support: Airbyte supports prominent vector databases, including Milvus, Qdrant, and Pinecone. You can store vector embeddings in these databases and use them based on your specific requirements to build robust AI applications.‍
• RAG Transformations: By integrating Airbyte with AI frameworks like LlamaIndex and LangChain, you can perform RAG transformations, such as chunking, embedding, and indexing. These operations help you modify your data, making it compatible with LLMs.

Summary

The choice between hierarchical database vs relational database, requires an assessment of your data structure and the specific application you are working on. You must also determine the appropriate tech stack and database technology between SQL vs NoSQL.

Hierarchical databases are efficient when managing tree-like data structures like taxonomical information and file management systems. On the other hand, relational databases are more prevalent for applications that require you to handle structured, tabular data, such as transactional systems or reporting and analysis.