Introduction

Graph analytics tools have begun to command a significant place in today's data-driven world. They offer a powerful tool that allows us to identify patterns in social networks, detect money laundering schemes, and much more. By providing the necessary firepower to handle complex relationships within interconnected data, graph analytics tools are becoming increasingly indispensable to many organizations.

In this blog post, we will explore the world of graph analytics platforms, exploring their importance, how they work, and the various types available. We will then move on to discuss some key factors to consider when choosing a graph analytics tool to help you make an informed decision that best suits your needs as you explore your options.

As we move further into 2024, a number of graph analytics tools based on cutting-edge technology have risen to prominence in the field. With this in mind, we will take a closer look at the top 7 graph analytics tools of 2024, examining their strengths, features, and what sets them apart from the rest. These include PuppyGraph, AWS Neptune, Cambridge Semantics, DataStax, Neo4j, OrientDB, and MarkLogic.

Whether you are a data scientist, a data engineer, or someone with a keen interest in the field, this blog post aims to provide you with a comprehensive understanding of graph analytics and the tools you'll need to make use of this tech. So, get ready to dive deep into the fast-paced world of graph analytics and explore their potential.

What is graph analytics?

Graph analytics and the platforms that support it are powerful tools used to analyze complex relationships between connected data in a network. Graph analytics rely on a graph, a mathematical structure of nodes and edges where nodes represent entities and edges depict their relationships. This graph analysis focuses on pairwise data relationships and the structural characteristics of the graph as a whole.

Graph analytics, a branch of data science, stands out from traditional analytics. Traditional analytics focuses only on numerical data, whereas graph analytics focuses on unstructured, dynamic, and fluctuating datasets. This type of analytics provides an intuitive data model that can accommodate new entities and relationships as they arise, including weighted graph representations. With the help of a graph analytics platform, this approach offers a powerful way to analyze complex data.

One of the main benefits of graph analytics is the visual representation they provide. The ability to visualize data is an integral part of graph analytics, providing an easy way to understand and navigate through graph data. This effective communication of data facilitates faster pattern detection and understanding of the information within the graph.

How do graph analytics work?

Graph analytics uses graph-specific algorithms to assess the relationships between entities across different applications. The result is an array of insightful predictions and analyses. The use of these specific graph technology and algorithms is what sets graph analytics apart from other forms of data analysis.

Cluster, partition, PageRank, and shortest path are popular examples of graph algorithms. These algorithms are essential for comprehending the relationships and network structures among entities within a graph. Utilizing these algorithms, graph analytics can proficiently analyze and forecast patterns in data and analyze relationships, offering a potent instrument for data scientists and analysts.

What are the types of graph analytics?

Graph analytics, being an umbrella term, encompasses several distinct types. Each type has its own specialized focus and application. That being said, a company's use case may lead them to leverage one or many types of graph analysis. Let's take a look at a few particulars.

Path analysis

Path analysis is one of these types, pinpointing the shortest or most efficient routes between two nodes, in a graph. This type of analysis is commonly used in social network analysis and other applications.

Community analysis

Community analysis is another type, which aims to identify groups or clusters of nodes that share common features or have more dense connections to each other than to nodes outside their group. This method is typically achieved using algorithms such as the Louvain algorithm, Label Propagation, and Weakly Connected Components.

Link prediction

Link prediction and link analysis, another subset of graph analytics, deals with analyzing and predicting potential future links between nodes in a graph. This method helps understand the relationships between various data points in the graph.

Node embeddings

Finally, node embeddings convert nodes into vector representations that succinctly capture the structural essence of the nodes and their relationships within the graph. These embeddings are especially beneficial for inputting graph-based data into machine learning algorithms.

Key factors to consider for graph analytics tools

Choosing the appropriate graph analytics tool is a significant decision that can greatly influence the success of your data analysis. Making sure you select the right tool for your specific use case means you must understand what type of analysis you plan to perform using graph, and if the platform supports it. Once you've established this, several key factors must be considered when assessing different tools.

Deployment & maintenance

One vital factor to consider is how easy a tool is to deploy and maintain over time. A tool that can be seamlessly integrated into existing systems (e.g., existing databases, data warehouses or data lakes) without extensive setup time or specialized knowledge not only accelerates the adoption process but also reduces the total cost of ownership (TCO). Look for solutions that offer straightforward installation processes, clear documentation, and active community or professional support. This ensures that your team can focus on deriving insights from data, rather than getting bogged down by complex deployment hurdles and ongoing maintenance challenges.

Scalability

Scalability is an important factor, allowing a graph analytics tool to handle large datasets efficiently, maintain high read and write loads, and adapt to increasing data sizes.

Performance

Performance is another key factor, with superior performance leading to quicker and more precise analysis, improving decision-making, and enhancing scalability to manage larger and more complex graphs.

Supported integrations

When looking at the available tools, you'll also want to consider your current data stack. You'll want to ensure that the analytics platform supports your data sources so that the data you are generating can easily be pulled into the platform.

Other considerations

Finally, when choosing a tool for software engineering, consider the following:

• The variety of tools provided
• The tool’s capacity to support the complete software engineering lifecycle
• Ensuring it meets the needs of a wide user base, including those primarily engaged in data analysis.

Top 7 Best Graph Analytics Tools of 2024

As we move into 2024, a number of graph analytics tools have risen to prominence in the field. These tools offer a wide range of features to cater to various use cases. Let’s delve into these leading contenders and discover what sets them apart from the rest.

PuppyGraph

PuppyGraph stands out due to being the first and only graph analytics engine in the market that can transforms existing relational data stores into a unified graph model in under 10 minutes. It natively integrate with popular lakehouses like Apache Iceberg, Apache Hudi, Delta Lake, BigQuery, Amazon Redshift, Snowflake, and databases like MySQL, PostgreSQL, DuckDB, etc.,  allowing users to execute queries directly on tables as graphs within their current data sources. This streamlines analytics and eliminates the need for complex ETL processes.

PuppyGraph's analytics engine utilizes the Lakehouse architecture, allowing users to perform native graph queries across one or multiple of their existing relational data stores, free from the usual costs, latency, and maintenance burdens associated with graph databases. Additionally, it streamlines data management by making use of your existing data store permissions, as there isn't another duplication of the data.

PuppyGraph sets itself apart by decoupling storage from computation, capitalizing on the advantages of columnar data lakes to deliver significant scalability and performance gains. When conducting intricate graph queries like multi-hop neighbor searches, the need arises to join and manipulate numerous records. The columnar approach to data storage enhances read efficiency, allowing for the quick fetching of only the relevant columns needed for a query, thus avoiding the exhaustive scanning of entire rows.

Moreover, PuppyGraph boosts efficiency by using min/max statistics and predicate pushdown, dramatically decreasing the volume of data that needs to be scanned. Its integration with vectorized data processing, which executes operations on batches of values simultaneously, further elevates PuppyGraph’s scalability and ensure second-level responses to complex queries. This approach not only simplifies data analysis but also enhances query performance overall. Additionally, its auto-partitioned, distributed computing architecture effectively handles petabyte-scale datasets, guaranteeing strong scalability in terms of both storage and computational power.

PuppyGraph supports two most popular graph query languages Gremlin and OpenCypher. Not to mention, PuppyGraph has a forever free community edition that's available via Docker install and can be easily deployed in your VPC or data center, providing you with full control of your data while guaranteeing adherence to any required data governance policies.

Here is a quick YouTube video that shows what is PuppyGraph in 100 seconds.

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AWS Neptune

AWS Neptune is a fully managed graph database service known for its:

• High availability
• Performance
• Support for open graph APIs
• Integrations with other AWS services

These features make it an excellent choice for representing relationships in graph form.

A significant feature of AWS Neptune is its ability to maintain high throughput and minimize latency. The service is engineered to store a large portion of the graph in the cache, effectively reducing graph query latency and delivering timely responses even when dealing with intricate and extensive datasets.

However, AWS Neptune can be expensive for large-scale deployments, which might be a limiting factor for some businesses. Despite this, its robust performance and features make it a strong contender in the graph analytics tools arena.

In addition, AWS Neptune offers the following benefits to graph analytics users:

• Supports open graph APIs
• High availability and performance
• Excellent for representing relationships in graph form

Cambridge Semantics

Cambridge Semantics' AnzoGraph DB is a recognized top knowledge graph platform. This platform makes Cambridge Semantics a contender in the graph analytics space. One of AnzoGraph DB’s notable features is its comprehensive set of over 40 functions designed for regular business analytics, views, and windowed aggregates. It facilitates in-graph feature engineering and transformations, empowering application developers to create custom functions and aggregates for parallel processing across graph databases.

While the pricing details for AnzoGraph DB will require you to chat with their sales team, its robust features and performance make it a compelling option for businesses seeking advanced graph analytics capabilities.

DataStax

DataStax specializes in real-time data for AI. One of its main offerings is Astra DB, a cloud-based database-as-a-service built on the Apache Cassandra platform. Astra DB is designed to provide developers and administrators with a scalable and cloud-native database solution.

For those looking for graph analytics, DataStax Enterprise Graph is equipped with features that address diverse requirements, including:

• Automatic geospatial data handling
• Automated geospatial software installation
• Support for Apache Spark 2.0
• Improved throughput for DSE Graph

These collectively offer a scalable and adaptable solution for the management of both structured data and unstructured data, ensuring that the data structure for data stored is handled efficiently.

Despite some user reviews suggesting scaling challenges, DataStax has proven to be a valuable tool for a wide range of applications in numerous industries. Its flexibility in deployment options and the integration of structured and unstructured data from multiple sources into a single authoritative data store make it an attractive option.

Neo4j

One of the most well-known names in the graph database market, Neo4j is an open-source graph database known for its:

• Efficiency in storing and retrieving data with tree relationships
• Excellence in persisting knowledge graphs and discovering relationships between entities
• An intuitive query language called Cypher
• Flexible integration options with various programming languages.

One of Neo4j’s significant features is its exceptional performance, especially in handling analytical queries. Its Parallel Runtime capability enables it to deliver up to 100X faster performance by leveraging concurrent threads across multiple CPU cores for efficient data processing.

However, some users have reported scaling challenges and performance issue when running queries beyond 3-hop, limited integration with other APIs, and a lack of a built-in visualization tool. Despite these challenges, Neo4j’s robust features and performance make it a strong contender in the graph analytics tools market.

OrientDB

OrientDB is an open-source NoSQL database management system known for its multi-model capabilities, including support for:

• graph
• document
• key-value
• object models

It is designed to be scalable, high-performance, and reliable, making it suitable for a wide range of applications.

OrientDB supports ACID transactions and offers indexing and querying capabilities. It also supports distributed and sharded architectures, which are beneficial for businesses dealing with large volumes of data.

Regarding challenges, OrientDB has a steep learning curve, particularly for beginners. Its community support is also limited compared to other databases. Despite these challenges, OrientDB’s robust features and free pricing model make it an appealing option for businesses seeking affordable and efficient graph analytics tools.

MarkLogic

MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities. It is the most secure multi-model database and can be deployed in any environment, making it an ideal choice for applications that deal with complex data containing multiple relationships.

MarkLogic’s universal index offers the following features:

• Comprehensive data search
• APIs for application development and deployment
• Support for ACID transactions
• Various security features

While MarkLogic is a standout in graph analytics tools, it comes with its own set of challenges. It is considerably more expensive than some other options, which could be a deterrent for smaller businesses or those with tight budgets. Additionally, despite its strong performance, it requires a significant investment in time and resources to fully leverage its sophisticated graph analytics features. It's worth noting that while MarkLogic is capable of powering a data hub and offers multi-model database capabilities, these functionalities can be complex to implement and may require a steep learning curve. The platform may also require dedicated technical support and maintenance, which could add to the overall cost. Despite these challenges, MarkLogic could remain a feasible choice for businesses that need advanced data management solutions and are willing to invest.

Conclusion

Graph analytics tools are vital for analyzing complex relationships within interconnected data. The leading seven graph analytics tools of 2024 provide a variety of features and capabilities to meet diverse business needs, including seamless integration with existing data sources, scalable analytics, and a proprietary graph query engine optimized for speed and efficiency.

Whether you are a data scientist aiming to reveal hidden patterns in large data sets, a data engineer striving to add graph models on your existing data, or an organization looking to improve your decision-making process, this list of the best graph analytics tools offers valuable solutions. The selection of the perfect graph analytics tool ultimately rests on your particular use case and needs. Consider the features, scalability, ease of use, and compatibility with your existing systems when choosing the right tool for your needs.

When it comes to the easiest and most performant way to allow your company to leverage graph analytics, look no further than PuppyGraph. By leveraging your existing data stores, PuppyGraph can easily integrate within your existing data infrastructure and power graph analytics in a matter of minutes. Deploy, connect, and query your data sources with ease. Ready to start? Download the forever free PuppyGraph Developer Edition or begin your free 30-day trial of the Enterprise Edition today, and lay the foundation for knowledge-driven decisions in your organization.