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.

Data modeling

Data modeling defines how databases organize and represent data. Graph databases and relational databases model data very differently.

Graph databases

Graph databases use nodes to represent entities and edges for relationships. Such a network-oriented approach to data modeling treats relationships among data components just as importantly as the data itself.  This makes representing and querying complex, interconnected data structures a lot easier and intuitive. It works best for non-linear data, such as social networks or recommendation systems. 

Graph databases adapt well to changes due to its schema-less design. You can add new nodes and relationships without modifying the entire structure. Dynamic data environments with complex and frequently changing relationships will find graph databases the ideal choice.

Relational databases

Relational databases organize data into tables, with rows representing records and columns representing attributes or data types. Each table stores a specific entity type (for example, customers, orders). This tabular structure works well for structured data with clear, predefined relationships—for example, inventory systems or financial records. 

Primary and foreign keys define relationships among data across different tables. However, complex relationships may require multiple JOIN operations that can complicate queries. As data grows, these queries also become harder to optimize.

Query language

The query language determines how users retrieve and manipulate data. Graph databases and relational databases have vast differences in the query languages they use.

Graph databases

Graph databases by design excels in traversing paths with multiple relationships. They use query languages like Cypher and Gremlin. These languages are optimized for traversing relationships and simplify queries that involve multiple connections. As a result, you can efficiently implement graph algorithms like the following:

• PageRank: Used to measure the importance of each node in a graph, like ranking web pages in a search result.
• Shortest path: Used to find the shortest path between two nodes. It has numerous applications such as social networks, logistics, and network routing.
• Community detection: A family of algorithms used to identify clusters of nodes that closely relate to one another. These algorithms have applications in fraud detection, social network analysis, recommendation systems, and many more.

The graph query languages focus on following relationships between nodes. Developers can express complex queries in a natural, readable form. However, learning these specialized languages often requires understanding graph theory. This introduces a learning curve for developers who have become used to SQL query languages.

Relational databases

Relational databases use SQL as the standard for query languages. It provides a powerful way to query structured data. You can use SQL to filter, join, and aggregate data across multiple tables. 

While SQL can robustly handle structured data, it becomes complex and slows down when querying deep relationships, especially with many JOIN operations. Despite the limitation, SQL is widely known and has extensive support from different tools. For this reason, developers find SQL easy to adopt and use for most traditional applications.

Performance

Performance dictates how efficiently each database handles various types of queries and data loads. Graph databases and relational databases have different performance merits depending on the use case at hand.

Graph databases

Graph databases exhibit superior performance for queries involving large volumes of complex and highly interconnected data, particularly for graph traversal and subgraph pattern matching. While it's technically possible to represent graph data in relational databases and use SQL queries with multiple JOINs to simulate graph traversal, the performance is significantly lower compared to dedicated graph databases.

Graph databases are specifically optimized for graph queries in several ways. Some utilize native storage and processing engines, enabling direct access and manipulation of nodes and edges. Others leverage existing relational or NoSQL databases for storage, benefiting from the maturity and ongoing development of these systems while applying optimizations for graph-specific queries. The efficiency of graph databases in handling complex queries on large datasets is evident in their consistently impressive performance in benchmarks, which holds true regardless of the underlying architecture.

Relational databases

Relational databases handle structured data more efficiently, particularly when queries involve simple relationships or transactions. They perfectly fit in environments that prioritize data consistency and accuracy, such as banking or e-commerce. 

However, performance can degrade when managing complex many-to-many relationships through JOIN operations. These JOINs require combining data from multiple tables. It increases query complexity and can slow down execution as datasets grow. 

Optimization techniques like indexing can mitigate some of these issues. But a relational database still has to scan through tables to identify relationships. As a result, relational databases face limits when handling highly connected data.

Scalability

Scalability affects how well a database can grow as data volume and usage increase. Graph databases and relational databases don’t have the same scaling strategies.

Graph databases

Graph databases scale horizontally by distributing data across multiple machines. In this method, as datasets grow, you add new servers to handle the increased workload. Horizontal scaling powers applications like real-time recommendation engines and IoT systems that deal with massive volumes of interconnected data. 

Essentially, graph databases distribute both the data and query load across multiple servers. Consequently, you get low latency and high throughput, even as the number of relationships grows exponentially.

Relational databases

Relational databases typically scale vertically. Contrary to horizontal scaling, they add more resources, like processing units, memory, storage, to a single machine. This works well for smaller datasets. However, you’ll start to face limitations as data grows beyond the capacity of a single server. 

Some relational databases use horizontal scaling techniques like sharding, but they introduce complexity. It becomes harder to maintain consistency and manage JOIN operations across distributed tables. Vertical scaling can also become expensive, requiring more powerful hardware to handle large datasets and complex queries.

Ease of use

The next important aspect of comparison is how accessible developers and teams find the database to be.

Graph databases

Graph databases require learning new concepts and query languages. You have to change your mental model of data and how you conceptualize their interactions. This can introduce a learning curve, especially for teams accustomed to SQL. To effectively work with graph databases, you may also have to understand basic graph theory. 

However, graph query languages like Cypher make it much easier to query interconnected data. A relationship-centric database that models interconnected data feels very natural and intuitive. If you compare this process to a relational one, SQL feels awkward and non intuitive for the same kind of data. Graph databases, with their graph query languages, provide clear, readable queries for complex relationships.

Relational databases

Relational databases are more familiar to most developers because SQL is a widely adopted, well-documented language. Developers can easily find resources, tools, and community support to help them work with SQL. Relational databases have a decades-long history of development, optimization, and tooling. So it’s easier to integrate them into existing projects. For applications that don’t involve complex relationships, relational databases are often simpler and quicker to set up and use.

Data integrity

Data integrity ensures data accuracy and consistency across all database operations. Graph databases and relational databases have fundamentally different ways how they ensure and maintain data integrity.

Graph databases

Remember that graph databases don't follow a strict schema. The flexibility allows you to add new nodes and relationships dynamically. But for this benefit of evolving data models, you may need to set up additional mechanisms for data integrity. 

While graph databases support transactional operations, not all of them strictly maintain ACID (Atomicity, Consistency, Isolation, Durability) properties, especially in distributed environments. In cases where a graph database spans multiple machines, ensuring strict consistency can become a challenge. 

Graph databases that prioritize high availability and performance often provide eventual consistency models rather than immediate consistency. This means data might temporarily become inconsistent across nodes but eventually synchronize. Use cases like social networks or recommendation systems have less-critical real-time consistency requirements. For those applications, you may find this trade-off acceptable.

You can also enforce integrity constraints related to the connections between nodes in a graph database. However, they may require additional measures to validate node properties or ensure consistency across disconnected subgraphs.

Relational databases

Relational databases by design build upon strong ACID properties. Strong ACID support ensures that data changes are either fully committed or rolled back in case of errors, thus preserving data integrity even in concurrent environments.  

Contrary to graph databases, relational databases rely on predefined schemas that rigidly define table structures, data types, and relationships. You can insert or update only valid data that conforms to the schema. Foreign keys create explicit relationships between tables so that related data remains consistent. Referential integrity checks prevent actions like deleting a parent record that has dependent child records in another table.

Transaction management

Transaction management makes sure that multiple operations occur reliably and consistently.

Graph databases

Graph databases take a more flexible approach in transaction management. They offer trade-offs between consistency and performance based on the specific use cases. While some graph databases provide ACID compliance for local transactions, maintaining consistency across distributed clusters requires careful design and additional mechanisms. For applications involving real-time, complex relationships, graph databases can manage transactions efficiently within a single graph. However, global consistency can introduce additional complexity. Some graph databases may also adopt eventual consistency models or provide configurable consistency levels.

When it comes to concurrency control, consider the fact that pessimistic locking locks resources preemptively to prevent conflicts. Because of graph data’s highly connected nature, pessimistic locking can therefore lead to performance bottlenecks. As a result, graph databases often favor optimistic concurrency control. It allows transactions to proceed without locking resources upfront. They detect conflicts at commit time, and may not retry the conflicting transactions.

Relational databases

Relational databases traditionally prioritize strong consistency and data integrity using ACID transactions. This guarantees that all operations within a transaction execute as a single unit. The database remains consistent even in the face of errors or concurrent operations.

Unlike graph databases, relational databases often employ pessimistic concurrency control mechanisms like two-phase locking (2PL). It involves locking resources before something gains access to them, ensuring that no other transaction can modify them until the lock is released. This maintains isolation and prevents conflicts between concurrent transactions.

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.