Graph Database vs Relational Database: Which Is Best for Your Needs?

Should you choose a graph database or a relational database for your business (or do you even need to choose)?

This overview will help you understand their differences and decide which is right for your needs.

Graph databases are a unique type of database that leverage graph structures for semantic queries. Unlike traditional databases that use tables, graph databases store data as a network graph, comprising nodes (entities) and edges (relationships).

This focus on relationships makes graph databases particularly powerful for applications that require intricate data connections.

Think of a graph database like a map of your friends on a social media platform. Each person (including you) is a "node" - imagine them as dots on a piece of paper. The connections between people - their friendships - are "edges," like lines drawn between the dots.

For example, you're connected to your friend Sarah, who's connected to her colleague John, who's connected to his roommate Mike. In a traditional database, finding out "who are the friends of my friends' friends?" would be like looking through multiple separate lists and matching names back and forth. It's like trying to find someone by checking three different phone books in sequence.

But in a graph database, it's more like tracing your finger along the lines on the paper - you can easily follow the connections from you → Sarah → John → Mike. The database is built specifically to understand and navigate these relationships, just like how you can naturally understand your social connections without having to consult multiple lists.

In graph databases, nodes and edges represent entities and their relationships, respectively. This structure allows for efficient navigation and querying of interconnected data.

Nodes can represent anything from people to products, while edges illustrate how these nodes are related. This interconnected graph model simplifies the representation of complex data structure.

One of the standout features of graph databases is their flexible schema. Unlike relational databases that require predefined tables and columns, graph databases can adapt to changing business needs without disrupting existing data. This flexibility, combined with the ability to efficiently query relationships, makes graph databases a versatile choice for many applications.

Graph databases excel in scenarios that require the management of complex relationships, such as social networking and recommendation engines. For instance, for streaming platforms or eCommerce sites, recommendation engines leverage interconnected data to suggest products or content based on user behavior and preferences.

Another significant application of graph databases is in artificial intelligence (AI) and machine learning. The ability to model complex relationships and detect patterns makes them ideal for AI development. For example, graph databases can be used to build more accurate models by understanding the intricate relationships between different data points.

Fraud detection is another area where graph databases shine. Analyzing the relationships between transactions, accounts, and users allows graph databases to identify members of fraud rings and fake profiles.

Relational databases are one of the most widely used types of databases, known for their structured approach to data storage. They organize data into tables, using rows and columns to represent records and attributes, respectively. This structured schema is key to their effectiveness in managing data integrity and consistency.

Relational databases are the backbone of many business applications, serving as powerful tools to organize interconnected data. They excel in managing structured data where relationships between elements are clearly defined and consistent.

The way relational databases store data makes them perfect for applications like:

• Sales systems tracking orders, inventory, and customer purchases
• Customer relationship management (CRM) platforms storing customer profiles and interactions
• Web analytics systems recording user behavior and engagement metrics
• Enterprise resource planning (ERP) systems managing company-wide operations

Relationships between tables are established using foreign keys, which link to primary keys in other tables. This setup ensures data integrity, as each row can be uniquely identified by a primary key. The use of SQL (Structured Query Language) is central to relational databases, providing a standard way to create, modify, and query tables.

An essential feature of relational databases is their support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. These properties ensure that operations are reliably processed, maintaining data integrity even in the event of failures. This makes relational databases particularly suitable for applications requiring high data reliability and consistency.

Applications with a large number of concurrent transactions also benefit from relational databases. Their structured nature allows for precise tracking of transactions, making them preferred over graph databases in many scenarios. Their strength lies in maintaining data integrity while handling millions of records across multiple related tables.

While both types of databases have their strengths, they cater to different data modeling and querying requirements.

The way a graph database stores data is fundamentally different from traditional approaches. Graph databases use nodes and edges, focusing on the relationships between entities. In contrast, the relational model organizes information into tables and predefined schemas, with rows representing records and columns as attributes.

In graph databases, edges can represent various types of relationships, such as parent-child or ownership connections. This flexibility makes them ideal for evolving data structures. On the other hand, the relational model often requires resource-intensive join operations to handle complex relationships, but excels at maintaining data integrity and consistency.

The flexibility and adaptability of graph databases make them suitable for applications with evolving data models. In contrast, relational databases provide a structured approach that ensures data integrity but may struggle with highly interconnected data. This fundamental difference in data modeling impacts how each type of database performs in various scenarios.

Graph query languages like Cypher are designed to express relationships visually, making it easier to work with interconnected data. These languages emphasize the relationships between nodes, allowing for more intuitive and efficient querying of graph databases. In contrast, SQL is the standard query language for relational databases, focusing on columns and rows.

Complex queries involving multiple hops or relationships are often simpler to write and execute in graph databases using graph query languages. SQL, while powerful, can become cumbersome when dealing with complex joins and nested subqueries. This difference in querying capabilities can significantly impact performance and ease of use.

Think of finding connections in a graph database like following a trail of breadcrumbs - you simply move from one point to the next. In contrast, searching in a relational database is more like looking up multiple different maps and trying to piece together a route.

Imagine you're at a party and trying to find out how you're connected to someone across the room. In a graph database, it's like asking your friend, who asks their friend, who asks their friend - you follow the connections directly. This makes graph databases lightning-fast at tasks like:

• Finding all friends-of-friends within 3 connections
• Generating "people you may know" suggestions
• Mapping the fastest route between two points

Now imagine trying to find the same connection at the party by:

1. Checking a guest list for your friends
2. Cross-referencing their friends from another list
3. Checking a third list for those friends' friends
4. Combining all these lists together
   

This is similar to how relational databases perform "joins" - they must combine multiple tables to find connections, which becomes slower as the amount of data grows.

Scalability and storage are vital factors to consider when choosing a database. Both graph and relational databases have different approaches to handling growing data volumes and ensuring efficient data storage.

Graph databases typically utilize horizontal scaling, allowing them to grow by adding more servers rather than upgrading existing hardware. This approach enables parallel processing of graph queries across multiple servers, improving scalability and performance. Horizontal scaling is particularly advantageous for managing large amounts of interconnected data.

Relational databases primarily rely on vertical scaling, which involves upgrading hardware components like CPU, storage, or memory to enhance performance. Although some relational databases, such as InterSystems IRIS^®, can also scale horizontally through sharding and other means, this process increases complexity.

Understanding these scaling approaches helps in choosing the right database based on your scalability needs.

Graph databases utilize index-free adjacency, which allows for faster traversal of relationships without the need for indexes. This non-normalized data model simplifies the storage of related data, enhancing storage efficiency. This approach is particularly beneficial for applications with highly interconnected data, where quick access to relationships is crucial.

Relational databases, with their normalized data models, ensure data consistency and integrity. Transactional databases (OLTP) may require more storage space due to the need for indexes and constraints, while analytic databases (OLAP) require much less due to the amazing efficiency of columnar storage.

Trade-offs between storage efficiency and data integrity are key considerations when choosing between graph and relational databases. Understanding these differences, and how they apply to different workloads, can guide you in selecting the most suitable database for your storage needs.

Determining when to use a graph database versus a relational database involves understanding the specific requirements of your application. Both types of databases have their strengths and are suited for different use cases.

Characteristics	Graph Databases	Relational Databases
Data Structure	Nodes & Edges (flexible schema)	Tables, Rows & Columns (predefined schema)
Ideal Use Cases	Social Networks Recommendation Engines Fraud Detection	Financial Systems Inventory Management Healthcare Records
Query Performance	Fast for relationships (constant-time traversal)	Slows with complex joins (join-heavy operations)
Scaling Approach	Horizontal Scaling (add more servers)	Vertical Scaling (upgrade hardware)
Data Integrity	Varies by implementation	ACID Compliant
Learning Curve	Steeper (new query languages)	Standard (SQL is widespread)

The graph data model shines when your data tells a story through its connections. Imagine trying to understand how all the characters in a film or book are related - that's the kind of complex relationship mapping where graph databases excel. They're ideal when your data is heavily interconnected and those relationships are as valuable as the data itself.

Key Applications:

• Social Media Platforms: LinkedIn uses graph databases to instantly show professional networks and degrees of connection - turning "who knows who" into actionable insights.
• Fraud Detection Systems: Banks employ graph databases to spot suspicious patterns, like money cycling through accounts in ways that traditional queries might miss.
• Recommendation Engines: Netflix leverages graph relationships to understand not just what you watch, but how your viewing patterns connect to others, enabling more nuanced suggestions.

Think of a relational database as your company's digital vault - it's where data integrity and consistency cannot be compromised.

Banking or healthcare systems rely on this structured approach because every transaction or patient record must be perfect, every time. When your data structure is stable and you need guaranteed accuracy, relational databases provide that foundation.

Key Applications:

• Banking systems requiring real-time transaction processing
• Healthcare records demanding absolute accuracy
• Inventory management systems needing precise stock tracking

Ask yourself the following:

1. Is your primary concern:
   
   1. Finding connections between data? → Graph Database
   2. Maintaining data integrity? → Relational Database
2. Do you need to:
   
   1. Frequently change data structure? → Graph Database
   2. Ensure transaction consistency? → Relational Database
3. Are you working with:
   
   1. Complex networks of relationships? → Graph Database
   2. Structured business processes? → Relational Database

Many modern applications use both types of databases. An e-commerce platform might use a relational database for orders and inventory while employing a graph database for product recommendations.

The key is choosing the right tool for each specific need within your system.

Many organizations, excited by the potential of graph databases, fall into the trap of using them where simpler solutions would suffice. Here's when to reconsider your graph database choice:

• Inappropriate Use: Using a graph database for an employee-department relationship
• Better Solution: A simple relational table with a foreign key
• Why: The overhead of maintaining a graph structure outweighs any benefits for straightforward relationships

• Inappropriate Use: Building a basic inventory system with graph relationships
• Better Solution: Traditional relational tables with standard SQL queries
• Cost Impact: Graph databases can be 2-3x more expensive for simple operations

• Warning Sign: If you're spending more time explaining the data model than using it
• Reality Check: Can the relationships be handled with 2-3 SQL joins?
• Performance Impact: Simple operations taking milliseconds in SQL might take seconds in graph queries

Understanding where relational databases start to struggle is crucial for planning scalable applications:

• Breaking Point: More than 5 levels of relationship depth
• Performance Impact: Each additional join can multiply query time by 1.5-2x
• Example: Customer → Orders → Products → Categories → Suppliers

• Common Scenario: Organizational hierarchies or network relationships
• Limitation: SQL recursion is often limited by database settings
• Performance: Depth > 4 levels can increase query time exponentially

• Problem: Adding new relationship types requires schema changes
• Impact: Can cause hours of downtime in large systems
• Cost: Schema changes in production can cost $10,000+ in large enterprises

• Limitation: Joins across shards can be more expensive
• Impact: Can reduce query performance 
• Solution Cost:: Often requires application-level changes if the database does not support sharding

Key Takeaway: The decision between graph and relational databases should be based on:

• Relationship complexity (depth and breadth)
• Query patterns (recursive vs simple joins)
• Scale requirements (data volume and relationship density)
• Team expertise and resources
• Budget constraints

The landscape of database technology is continuously evolving, shaped by advancements in software applications and computing hardware. Understanding future trends can help businesses stay ahead of the curve and leverage new technologies for better data management and analysis.

Graph analytics, an emerging field that helps organizations uncover hidden patterns and relationships in complex data networks, is expected to experience significant growth, reaching an estimated value of $637 million by 2030.

The ability to analyze complex data relationships is becoming increasingly crucial in big data and AI applications. Graph databases, with their robust graph data models and efficient querying capabilities, are well-positioned to capitalize on this trend.

The rise of graph analytics highlights the importance of understanding and leveraging complex data relationships to gain deeper insights and drive innovation. As businesses continue to adopt graph technology, the demand for skilled professionals in this field is likely to grow, further driving the development and adoption of graph databases.

Relational databases are also evolving to meet modern data challenges, with innovations like NewSQL databases merging traditional relational database strengths, such as ACID, with NoSQL flexibility.

These advancements aim to address scalability and flexibility issues, allowing businesses to manage diverse data types while maintaining the consistency and structure of traditional relational databases. The future outlook for relational databases is promising, with ongoing advancements likely to enhance their relevance even further in a data-driven world.

As businesses continue to navigate complex data environments, the ability to leverage both relational and graph database technologies will be crucial in achieving efficient and effective data management.

Choosing between graph and relational databases depends on the specific needs of your application and the nature of the data. Graph databases excel in scenarios with complex relationships, offering better performance for interconnected data, while relational databases are preferred for structured data requiring high data integrity and ACID compliance.

InterSystems provides robust support for both relational and NoSQL functionality such as Documents and Objects through our InterSystems IRIS data platform.

For relational databases, InterSystems IRIS offers strong support for SQL and ACID-compliant relational database capabilities, making it ideal for applications that require structured data management and transactional integrity. This means you can rely on InterSystems IRIS for high-performance, reliable data operations in traditional relational database environments.

In addition to its relational database capabilities, InterSystems IRIS also supports graph database functionality, allowing businesses to store and query data in a graph model. This is particularly beneficial for applications that require understanding and visualizing complex relationships between data points, such as social networks and supply chain analysis.

By supporting both types of databases, InterSystems IRIS provides a versatile and powerful platform for modern data management needs.

Ready to take the next step in managing your data? Find out more about InterSystems IRIS today.