The database you choose may make or break your system, depending on whether you’ve made the right choice. Databases have become a critical component of business operations across organizations small and large. Research suggests 75% of businesses have come to the exact realization. 

The same research also shows that 80% of the world’s data live in relational databases. The adoption of graph databases has also jumped by 605% from 2012 to 2019. So for the use case at hand, the question of selecting a database often comes down to the type of database you should settle for.

In this article, we will discuss these two database types in detail. We cover everything you need to know to help you determine which might fit your requirements better.

What is a graph database?

A graph database organizes data by focusing on relationships rather than structured tables. It uses a simple, flexible data model consisting of nodes, edges, and properties:

• Nodes represent individual entities, like people or products. 
• Edges define the relationships between these entities, such as friendships or purchases.
• Properties describe details about both the nodes and edges. 

This structure allows data to naturally express how entities connect to one another. It almost feels like a more intuitive, adaptable, and natural way to manage highly-connected data.

Traditional databases rely on predefined schemas and rigid relationships. Graph databases allow for dynamic, real-time changes to data connections. The schema-less design means you don’t have to plan for every possible relationship in advance. This makes graph databases more adaptable, especially when handling complex, evolving data structures like social networks, recommendation systems, or supply chains. Relationships being first-class citizens, graph databases can give you insights that conventional relational approaches may fail to achieve.

What is a relational database?

A relational database organizes data into structured tables. Each table consists of rows and columns. These tables follow a predefined schema that strictly defines how the database stores the data. The schema-first approach helps maintain consistency across the database. Apart from data types, relational databases also enforce constraints so the data remains accurate. 

Every row in a table represents a specific record, and each column holds a specific attribute or data type. Each record possesses a unique identifier, known as a primary key. Primary keys ensure that you can easily retrieve data from the database and link data across multiple tables.

Relational databases use relationships between tables to organize data efficiently. To establish these relationships, you use foreign keys. Foreign keys reference primary keys in other tables. This way, you can query related data together, even if it exists in separate tables. 

Structured Query Language (SQL) serves as the standard for querying and managing data within relational databases. SQL allows users to define, manipulate, and control access to data. SQL makes it easy to perform complex queries, join tables, and other operations. 

Relational databases offer reliable data management for systems that prioritize accuracy, data consistency, and integrity. For example, in transactional systems like banking, data corruption can lead to consequences like financial discrepancies or stock miscounts.

How is a graph database different from a relational database?

Graph databases and relational databases differ significantly in how they handle querying, performance, scalability, and what they prioritize. 

Graph databases focus on the relationships between data points, using nodes, edges, and properties. This structure, combined with query languages like Cypher or Gremlin, allows you to efficiently navigate complex relationships. On the contrary, relational databases organize data into tables. They each represent an entity, with primary and foreign keys defining the relationships. The SQL standard provides excellent support for handling structured data but can become cumbersome when managing deeply connected relationships.

The difference extends to performance as well. Graph databases outperform relational databases when handling highly connected data, since they directly traverse relationships without complex JOIN operations. This makes them ideal for applications like social networks or recommendation engines. Relational databases, while reliable for structured data, struggle with many-to-many relationships due to the overhead of multiple JOINs. Use cases that require rapid analysis of large, interconnected datasets, graph databases offer better flexibility and speed.

Then consider scalability. Today many enterprise relational databases can scale horizontally—for example, YugabyteDB, SingleStore, and BigQuery. Vertical scaling faces limits as data grows. Graph databases, on the other hand, scale horizontally across multiple machines. They can maintain performance even as the dataset expands. This makes them better choices for applications requiring real-time insights and large volumes of interconnected data. 

Despite the flexibility in handling dynamic data models, graph databases require learning new query languages. Teams familiar with SQL-like mental models of data have to develop an entire different viewpoint. 

Graph database vs relational database: detail comparison

Let’s take a more in-depth look into the differences between these two database types in some key areas. When deciding between the two, we highly recommend that you consider these factors.

A brief history: graph database and relational database

If we look at the history of relational databases and graph databases, we can observe different needs in data storage and management over time. 

In 1970, E.F. Codd at IBM introduced the relational database model. It revolutionized data organization and how people used to retrieve them. The model focused on organizing data into tables and laid the foundation for Structured Query Language (SQL). By the 1980s, relational databases had become the go-to solution for businesses worldwide.

Graph databases emerged later from the need to manage more complex, interconnected data that relational databases struggled to handle efficiently. While graph theory dates back to the 18th century, it wasn't until the late 1990s and early 2000s that practical implementations of graph databases began to emerge. Neo4j, Inc. developed the first property graph model in 2000 during the creation of a media management system. It marked the birth of modern graph databases.

The rise of the internet and social networks fueled the demand for graph databases. These systems could efficiently manage and query vast amounts of highly connected data. Meanwhile, relational databases continued to evolve, with notable milestones like the standardization of SQL in 1986 and the introduction of advanced optimization techniques. 

Both database types have adapted to the growing demand for scalability, cloud integration, and data analytics. Graph databases have become central to NoSQL systems. On the other hand, relational databases are seeing advancements in NewSQL, which combines scalability with transactional reliability.

As businesses face increasingly complex data needs, both relational and graph databases continue to play vital roles. They each excel in different areas of data management. Their development mirrors the ongoing search for efficient ways to store, query, and analyze datasets that continue to expand with the digitalization of our lives.

Which is better: graph database or relational database?

No definitive answer exists that can decide the better database for you. The right choice depends on the specific use case and the nature of the data. 

Graph databases are proficient in handling complex, interconnected data where relationships play a central role. For example, systems like fraud detection, social networks, recommendation engines, and supply chain optimization need to quickly traverse multiple layers of relationships. On the other hand, relational databases perform better when managing structured, tabular data. They will serve you better for applications like financial systems, e-commerce platforms, and transactional workloads that prioritize consistency and efficiency.

Graph databases outperform relational databases when managing data with deep, complex relationships. They avoid costly JOIN operations and handle multi-hop queries with greater speed and efficiency. However, relational databases can better manage large volumes of structured data. They are optimized for handling transactions and simple queries. They are a better choice when you have minimal relationships between data points.

Then you have to consider scalability. We’ve already discussed that graph databases scale horizontally. It works well for applications like real-time recommendations, where relationships continue to expand as the amount of data increases. However, graph databases may face performance challenges when dealing with large, unconnected datasets. 

In contrast, relational databases typically scale vertically by adding resources to a single machine. Some use horizontal scaling techniques like sharding. For large datasets with minimal relationships, relational databases or data warehouses like Snowflake and Databricks often perform better than graph databases.

Each database type comes with its own set of advantages and disadvantages. Graph databases simplify querying complex relationships but require learning new query languages and adapting existing systems. They have less relevance for applications that prioritize data volume over relationships, such as traditional data warehousing. Relational databases are easier to use, benefit from well-established standards, and handle structured data efficiently. 

Ultimately, the choice between a graph database and a relational database depends on your specific requirements. 

• If your application relies heavily on relationships between data, a graph database provides the efficiency and flexibility you need. 
• If you focus on structured data with minimal relationships, a relational database offers better performance, simplicity, and stability. 

Carefully assess your data needs, query complexity, and scalability requirements before making a decision.

Why PuppyGraph?

PuppyGraph offers a truly unique solution with the best of both relational databases and graph databases. It’s the first and only real time, zero-ETL graph query engine in the market. PuppyGraph can transform existing relational data stores into a unified graph model in less than 10 minutes, bypassing traditional graph databases' cost, latency, and maintenance hurdles.

Let’s look at how PuppyGraph can help your realize your use case:

No ETL

PuppyGraph enables you to query your SQL data as a graph by directly connecting to your data warehouses and lakes. This eliminates the need to build and maintain time-consuming ETL pipelines for a traditional graph database setup. You don’t have to wait for data or encounter ETL process failures anymore.

Petabyte-level scalability

PuppyGraph eradicates graph scalability issues by separating computation and storage. Using min-max statistics and predicate pushdown, PuppyGraph significantly reduces the amount of data it scans. 

PuppyGraph perfectly aligns with vectorized data processing. It contributes to PuppyGraph’s ability to scale effectively and ensure rapid responses to intricate queries. You get streamlined data analysis and overall improved query performance. PuppyGraph’s auto-sharded, distributed computation effortlessly manages vast datasets, ensuring robust scalability on both storage and computation.

Complex queries in seconds

PuppyGraph delivers lightning-fast results, handling complex multi-hop queries like 10-hop neighbors in seconds. Our patent-pending technology efficiently leverages all computing resources for exceptional performance. PuppyGraph’s distributed query engine design allows users to increase the performance by simply adding more machines. 

Deploy to query in 10 mins

PuppyGraph's revolutionary query engine eliminates onboarding hassles that comes with a graph database. You deploy and start querying within just 10 minutes. 

Replacing an existing Neo4j database? Effortlessly drop in PuppyGraph as the replacement and seamlessly connect to third-party tools without any data or code migration. 

Conclusion

In this article, we’ve discussed the core concepts behind relational databases and graph databases, how they work, and their unique strengths. Hopefully, you now have a clear idea how these two database types differ and why those differences matter for your use case.

It suffices to say that both relational and graph databases have strong footprints in all data-driven use cases across different industries. You need both to operate efficiently and succeed. That’s why PuppyGraph exists—offering the best of both worlds in a unified platform where you can intuitively navigate data and solve big data problems effectively.

You don’t have to take our word for it. If you’re ready to start with PuppyGraph, download the forever free PuppyGraph Developer Edition or begin your free 30-day trial of the Enterprise Edition today.