Gartner predicts that by 2025, 80% of data and analytics innovations will use graph technologies! It goes to show how relevant this piece of technology has become in solving critical problems for individuals and large organizations alike. Consequently, it also highlights the growing need for tools that can effectively handle complex, interconnected data. 

Traditional databases often struggle to efficiently manage and query interconnected datasets. Graph databases, on the other hand, excel in this domain by design. In this blog post, we'll explore the top five graph database tools leading the pack in 2024. We’ll briefly go through the basics of graph databases. Then you’ll learn how these tools can help you leverage the power of graph-based data models.

What is a Graph Database?

In layman’s terms, a graph database represents data as a couple of data entities and the relationships among them.  Consider the map of a country if you were to represent it through a graph model. You have different cities and they all connect to one another through roads.

Graph databases leverage graph theory principles to store and query data. In graph theory, we label the individual data entities as “nodes” and the relationships as “edges”. So for the city map example, nodes can represent the individual cities and edges can represent the roads that connect those cities. As you can tell, this data model feels very intuitive. Compared to traditional databases, it provides a more efficient way to understand and manage interconnected data. 

Let’s look a bit more into these concepts so you better understand how the theory applies to practice:

• Nodes: Nodes represent individual entities within your data—for example, people, products, locations, or any other object you want to model. Each node holds information about that specific entity in the form of properties. For example, a "customer" node might have properties like "name," "email," and "join date."
  
  
• Edges: Edges define the connections or relationships between nodes. They illustrate how different entities relate to each other. For example, an edge labeled "purchased" can connect a "customer" node to a "product" node, indicating that the customer has bought that product. Edges can also have properties, such as a "date" property on a "purchased" edge to record the transaction date.

Since you store data natively as nodes and edges, you essentially define the interconnectivity right from the start. Therefore, you eliminate the need for expensive JOIN operations required in relational databases to connect related information. This inherent difference gives you faster and more efficient querying, especially when traversing relationships across large datasets. Graph databases are ideal for use cases where you want to understand the connections between data points, for example:

• Social networks
• Recommendation engines
• Fraud detection systems
• Knowledge graphs
• Network and IT operations

How Does Graph Database Work?

In the preceding section, we discussed how graph databases model and store data. As you can imagine, this structure directly reflects real-world networks. Using a graph database, you can intuitively model relationships without complex table structures.

When querying large volumes of complex and highly interconnected data, graph databases don’t require traditional join operations. Nodes link directly through edges. It eliminates the need for foreign keys and join tables. This direct connection model enables efficient traversal of relationships and pattern matching in subgraphs. 

For example, consider an enterprise network monitoring system. Tracking potential cybersecurity threats  there means following the trail of interactions among devices and users across the network. With a graph database, this complex traversal across nodes—for example, “device accessed by user X, communicating with server Y, and transferring data to external site Z”—becomes intrinsic and seamless. You get real-time detection of potentially malicious patterns.

To further optimize performance for graph queries, graph databases use specialized indexing and storage strategies. Native storage and processing engines allow you to directly access and manipulate nodes and edges. On the other hand, some leverage existing relational or NoSQL databases for storage due to the mature ecosystem and active development surrounding them. Then on top of the relational and NoSQL systems, graph databases apply optimizations for graph-specific queries.

Then comes the query languages themselves like Cypher and Gremlin. Cypher allows you to query declaratively so you can specify complex patterns—for example, “identify all devices with unusual communication patterns over the past 24 hours,”. You don’t need to detail procedural steps. You focus on the structure of relationships rather than retrieval processes, simplifying complex queries like this by a large margin. It becomes easier to uncover patterns and insights in vast, highly connected datasets.

Graph databases shine in relationship-critical applications, like network security, recommendation systems, social network and fraud detection. For example, in supply chain management, you can use graph databases to map intricate relationships across multiple suppliers, manufacturers, and distribution centers. You can efficiently traverse connections and flows of various business operation components. With such a model at hand, you can track dependencies, predict delays, and optimize logistics by identifying potential bottlenecks. You cannot achieve this level of efficacy using relational databases and make data-driven decisions in real time.

Key Factors to Consider When Choosing Graph Database Tools

The right graph database tool can drive your project's success by ensuring optimal performance, adaptability, and developer efficiency. Here are the key aspects we recommend you evaluate when making your choice:

Performance and Scalability

Performance becomes the most dominating concern for applications that involve large-scale datasets and deep graph traversals.

Real-World Performance

When selecting a graph database, real-world performance under typical workload conditions is essential. Consider the nature of your data workload: Is it more transactional (OLTP) or analytical (OLAP)? For applications that require numerous quick transactions, such as real-time recommendations or dynamic user interactions, the database tool should excel at low-latency operations. Alternatively, if your needs focus on large-scale analysis, such as complex network analytics or behavioral patterns, you’ll want a tool optimized for high-throughput batch processing.For example, if you have a customer insights platform analyzing buyer preferences and purchase histories, you may need to quickly identify top connections within social or behavioral networks. The graph database tool should efficiently handle fast lookups for real-time personalization while also performing deeper analyses on patterns and clusters that support targeted marketing strategies.

Future-Proof Scalability

Choose a graph database tool that allows you to scale compute resources as you need. Scalability in a graph database refers to the system’s ability to handle growing amounts of data and increasing user requests by efficiently adding compute or storage resources. This ensures that the database can expand seamlessly to meet the demands of applications.  For example, consider IoT (Internet of Things) applications that track and analyze data from millions of sensors, like the ones smart cities employ to monitor traffic patterns and environmental conditions. For such use cases, you must guarantee seamless scalability. 

High-Throughput Write Operations

Applications that deal with frequent data modifications require a high write-efficient tool. Cybersecurity systems, for example, must log and track hundreds of new threat events per second. They have to analyze relationships between threat actors, affected devices, and possible vulnerabilities. Therefore, you need a tool with robust write performance and real-time analytics capabilities. The tool must possess the ability to near-instantly process and cross-reference new data inputs without interrupting ongoing analyses.

Data Modeling Flexibility

Flexible data modeling allows you to represent complex, dynamic relationships across varied data domains and accommodate diverse data structures.

Adaptable to Specific Domains

Select a graph database tool that fits naturally into your specific domain. A supply chain management application, for example, tracks goods, suppliers, logistics routes, and customs requirements across numerous entities. With flexible data modeling, you can map connections between items, suppliers, routes, and regulations in real time. You get detailed visibility and tracking through all supply chain phases. With a customizable data model, your database will effectively reflect industry-specific complexities and dependencies.

Support for Advanced Graph Models

Depending on your use case, you may need support for specialized graph models. For example, you can use property graphs to have rich metadata on both nodes and edges. In a retail analytics platform, property graphs can help track not only customer interactions but also attributes like purchase date, product type, and channel (in-store, online, and so on) directly within each relationship. This flexibility in modeling allows you to capture and analyze data at a granular level. You get deeper insights into customer behavior patterns and interactions.

Besides  you have RDF (Resource Description Framework) graphs that provide a powerful way to represent and query highly structured, semantically rich data. Unlike traditional property graphs, RDF uses triples—subject-predicate-object statements—to define relationships. It supports standardized vocabularies and ontologies. This enhances data interoperability across different systems and domains. With RDF, users benefit from semantic clarity, making it an ideal choice for applications requiring strict data interpretation and adherence to data standards.

ETL Requirements

Carefully consider whether or not the graph database tool requires ETL (Extract, Transform, Load) and if so, whether it offers tools to facilitate the process.ETL processes typically involve extracting data from sources, transforming it to fit database requirements, and then loading it—a multi-step workflow that can slow down data availability. In traditional ETL workflows, data only becomes available after each processing cycle completes. So you incur potential lags at every stage. Moreover, in ETL workflows, changing data sources often requires you to reconfigure extraction and transformation steps. This adds development cycles and further delay until you can get value out of your data. For domains like finance, logistics, and IoT that require fast and timely access, ETL may do more harm than good.

An ETL-free system ingests data directly in its native format—an especially valuable feature when data sources vary in structure and content. ETL-free tools can adapt to new data sources or formats since they don’t depend on rigid preprocessing. You get much less setup complexity. Teams can skip lengthy preprocessing steps and focus on immediate querying and analysis. Being ETL-free also means the tool supports real-time data integration for faster insights and responses to real-time changes in data. Overall, this flexibility empowers you to accommodate dynamic data needs without costly delays.

Query Language Capabilities

A robust query language can simplify the process of extracting value from your graph data.

Expressive and Intuitive Querying

Opt for a tool that supports a query language capable of handling everything from simple lookups to advanced analytics. These can include recursive pattern matching, subgraph pattern matching, and advanced graph algorithms. 

For example, telecommunications networks require the ability to trace call paths across multiple nodes to detect patterns like fraudulent call routing. So you need a language capable of recursive pattern matching, advanced pathfinding, and aggregation, where queries can retrieve relevant insights without excessive complexity. The ability to perform such nuanced searches reduces processing time and improves accuracy.

Developer-Friendly Syntax

Query languages like Cypher have an intuitive and declarative style. As a result, new users will find the learning curve much less steep. Such a syntax simplifies the process of retrieving data based on patterns rather than step-by-step instructions. A developer-friendly language accelerates development time by making the query composition process more accessible. It always balances readability with functionality to enhance productivity.

Deployment and Operational Excellence

You naturally want your database to remain efficient, secure, and manageable over time. And for that, you need reliable and versatile deployment options.

Flexible Deployment Options

Deployment flexibility supports different infrastructure needs, from cloud, on-premises, to hybrid setups. Consider your infrastructure requirements and ensure the graph database tool aligns with your security and compliance policies. 

For example, financial institutions often need on-premises deployments for strict security requirements to retain complete control over sensitive data. On the other hand, a cloud-first solution offers cost-effective scalability for a tech startup with data-intensive applications, such as a social media platform analyzing engagement metrics. These deployment choices allow organizations to balance scalability with data control.

High Availability and Reliability

Mission-critical applications demand high availability. You need mechanisms for data replication, failover, and automated backups to prevent service interruptions. If your graph database tool vendor offers flexible deployment options, you can likely control these critical areas as well. 

In sectors like healthcare, access to patient data must remain uninterrupted for emergency cases. Therefore, the infrastructures powering such sectors, without automatic failover and quick disaster recovery capabilities, cannot ensure that continuity. Such high reliability features minimize risk and secure consistent access to critical data even in the event of failures.

Management and Monitoring Tools

Evaluate the database tool’s managerial capabilities, along with data security and governance features. These include user access control, automated indexing, and real-time performance tracking. 

In customer-facing applications, usage can spike unpredictably, for example, in retail during holiday seasons. With automated indexing, data retrieval remains efficient despite increased demand. On the other hand, real-time monitoring can alert administrators to potential issues. It enhances and promotes proactive management and increases operational efficiency.

Ecosystem and Tooling

A strong community and ecosystem provide resources, support, and additional functionality that drive long-term success with the graph database tool you end up picking.

Active and Engaged Community

A thriving community and a rich ecosystem are vital for the long-term success of any technology. They fuel innovation, provide support, and ensure the continuous improvement of a graph database. When evaluating options, consider these aspects:

• Consider active open-source projects around the graph database tool. These often cultivate highly engaged communities where users contribute to development, share knowledge, and support each other.
• Forums, mailing lists, and online resources can provide help to users and promote shared experiences. For example, an enterprise deploying a complex graph database setup might rely on community-driven tutorials and best practices for guidance.

• A strong community generates documentation, tutorials, and technical articles that accelerate the learning process.

Expanding Integrations and Partnerships

Consider how the tool approaches integration with existing technologies:

• Connectors simplify data integration with other data sources, visualization tools, and analytics platforms, expanding a graph database’s capabilities. For example, a financial institution tracking fraud patterns may need specialized integrations with data visualization tools to display suspicious transaction flows.
• Strategic partnerships with key technology providers can enhance functionality. It can broaden the product’s reach and lead to new integrations. As a result, tooling gets better and so do user experiences.

5 Best Graph Database Tools of 2025

You can find a diverse range of graph database tools in the market today. To help you decide the perfect solution, let’s take a look at the best tools that have been shaping the industry.

PuppyGraph

PuppyGraph offers a unique advantage by enabling graph queries directly on existing relational databases and data lakes. Unlike traditional graph databases, it eliminates the need for ETL processes. Organizations can start querying their existing data from multiple sources as a unified graph in no more than 10 minutes, without any data migration whatsoever.

PuppyGraph not only reduces setup time and complexity but also preserves the integrity of your existing relational data infrastructure. You can query the same data using both SQL and graph query languages like Cypher and Gremlin at the same time.

PuppyGraph seamlessly integrates with major relational databases like PostgreSQL and MySQL. It also supports data lakes like Iceberg and Delta Lake. PuppyGraph’s native graph analytic engine gives you sub second query execution. Its high-performance capabilities remain consistent that helps users achieve petabyte-level scalability in their data systems without costly infrastructure changes. 

PuppyGraph’s flexible data model accommodates various data relationships. It gives you a range of both automated and manual graph modeling tools that can efficiently translate SQL data into a graph representation. Additionally, PuppyGraph automatically proposes optimal mapping strategies for data points. You get the best user experience with guided support and automation in model development.

PuppyGraph also gives you a centralized hub for graph data visualization. With its wide array of data sources support, it doesn’t matter where and how your data resides—plain text, databases, or data warehouses. You don’t have to go through the hassle of hopping between tools to visualize and analyze data for different data sources. All data converges into a single graph that becomes the single source of truth for your visualization and analytics processes through PuppyGraph’s user-friendly platform.

Neo4j

Neo4j has one of the widest adoptions among graph databases. It possesses a large community and a mature ecosystem. Neo4j’s Cypher query language has been designed for expressive and versatile graph queries. With its native graph storage, Neo4j can efficiently handle connected data. 

However, native storage also means that data from external sources requires ETL for migration into Neo4j. ETL imposes significant time and complexity on the setup process. For those looking to unify data from multiple sources without extensive ETL, Neo4j’s requirements may introduce additional steps and infrastructure dependencies. Organizations with large, constantly evolving datasets might find these aspects a set of severe challenges to overcome before they can start getting value out of their data. 

Additionally, scaling Neo4j across extremely large deployments can present challenges in high-throughput or high-velocity environments.

Amazon Neptune

Amazon Neptune offers a fully managed cloud-native graph database service on AWS. As a native graph database, it supports both property graphs and RDF graphs, providing options for different data models. However, like other native graph databases, Neptune requires ETL to bring in data from existing relational databases or on-premises systems. So you incur additional processing layers.

Neptune’s cloud integration provides high availability and built-in scaling within the AWS ecosystem. This brings huge benefits to users already deeply embedded in AWS infrastructure. However, this dependency on AWS may be restrictive for organizations with multi-cloud or on-premises requirements. If you seek cross-platform flexibility and streamlined data integration, carefully consider the ETL requirements and potential vendor lock-in risks.

Dgraph

Dgraph has been designed as a distributed, native graph database that enables high query volumes and performance for large datasets. Its native GraphQL support gives you API-driven data management, making it ideal for applications centered around GraphQL-based data access. However, this specific focus on GraphQL may not fully meet the needs of teams looking for support with other specialized graph query languages, like Cypher or Gremlin.

As a native storage database, Dgraph requires ETL for data ingestion from relational databases and other external data sources. It slows down the transition for organizations moving to graph-based analysis. For teams looking for immediate data integration from multiple sources, this ETL requirement may introduce unnecessary complexity.

ArangoDB

ArangoDB is a multi-model database, meaning it supports key-value, document, and graph storage within a single system. This versatility allows users to work with different data models as they need. It supports graph queries as well. However, its multi-model architecture may not offer the same level of specialization or optimization as dedicated graph database systems, especially for highly complex and large volume graph workloads.

Like other native graph storage databases, ArangoDB requires ETL to integrate data from relational databases, adding time and setup complexity. Companies requiring an ETL-free, faster-time-to-market transition from relational data to a unified graph environment might find this ETL requirement less than ideal. The multi-model structure may also mean that organizations looking for a focused, specialized graph tool will fail to find the depth of graph-specific optimizations they need.

Conclusion

Graph database tools have seen impressive innovation and research in recent years. The technologies and techniques that have been coming out continue to pave the way for everyone to leverage graph analysis and solve the problems that stand in the way of its adoption. The tool you choose can either set you up for success and growth, or burden you with new problems that impair or sometimes nullify the benefits you expect from graph analytics.

PuppyGraph exists to maximize the power of graphs for everyone. It solves the daunting challenges of transitioning to graph database systems and while perpetuating the performance, scalability, and security of your high-volume data systems. To try PuppyGraph out for yourself, download the forever free PuppyGraph Developer Edition, or book a free demo today.