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Graph Database Market Size 2025-2029

The graph database market size is valued to increase by USD 11.24 billion, at a CAGR of 29% from 2024 to 2029. Open knowledge network gaining popularity will drive the graph database market.

Market Insights

North America dominated the market and accounted for a 46% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 1.51 billion in 2023
By Type - RDF segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 670.01 million 
Market Future Opportunities 2024: USD 11235.10 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth due to the increasing demand for low-latency query capabilities and the ability to handle complex, interconnected data. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. One real-world business scenario where graph databases excel is in supply chain optimization. In this context, graph databases can help identify the shortest path between suppliers and consumers, taking into account various factors such as inventory levels, transportation routes, and demand patterns. This can lead to increased operational efficiency and reduced costs.
However, the market faces challenges such as the lack of standardization and programming flexibility. Graph databases, while powerful, require specialized skills to implement and manage effectively. Additionally, the market is still evolving, with new players and technologies emerging regularly. Despite these challenges, the potential benefits of graph databases make them an attractive option for businesses seeking to gain a competitive edge through improved data management and analysis.

What will be the size of the Graph Database Market during the forecast period?

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The market is an evolving landscape, with businesses increasingly recognizing the value of graph technology for managing complex and interconnected data. According to recent research, the adoption of graph databases is projected to grow by over 20% annually, surpassing traditional relational databases in certain use cases. This trend is particularly significant for industries requiring advanced data analysis, such as finance, healthcare, and telecommunications. Compliance is a key decision area where graph databases offer a competitive edge. By modeling data as nodes and relationships, organizations can easily trace and analyze interconnected data, ensuring regulatory requirements are met. Moreover, graph databases enable real-time insights, which is crucial for budgeting and product strategy in today's fast-paced business environment.
Graph databases also provide superior performance compared to traditional databases, especially in handling complex queries involving relationships and connections. This translates to significant time and cost savings, making it an attractive option for businesses seeking to optimize their data management infrastructure. In conclusion, the market is experiencing robust growth, driven by its ability to handle complex data relationships and offer real-time insights. This trend is particularly relevant for industries dealing with regulatory compliance and seeking to optimize their data management infrastructure.

Unpacking the Graph Database Market Landscape

In today's data-driven business landscape, the adoption of graph databases has surged due to their unique capabilities in handling complex network data modeling. Compared to traditional relational databases, graph databases offer a significant improvement in query performance for intricate relationship queries, with some reports suggesting up to a 500% increase in query response time. Furthermore, graph databases enable efficient data lineage tracking, ensuring regulatory compliance and enhancing data version control. Graph databases, such as property graph models and RDF databases, facilitate node relationship management and real-time graph processing, making them indispensable for industries like finance, healthcare, and social media. With the rise of distributed and knowledge graph databases, organizations can achieve scalability and performance improvements, handling massive datasets with ease. Security, indexing, and deployment are essential aspects of graph databases, ensuring data integrity and availability. Query performance tuning and graph analytics libraries further enhance the value of graph databases in data integration and business intelligence applications. Ultimately, graph databases offer a powerful alternative to NoSQL databases, providing a more flexible and efficient approach to managing complex data relationships.

Key Market Drivers Fueling Growth

The growing popularity o

Context

The awesome datasets graph is a Neo4j graph database which catalogs and classifies datasets and data sources as scraped from the Awesome Public Datasets GitHub list.

Content

We started with a simple list of links on the Awesome Public Datasets page. We now have a semantic graph database with 10 labels, five relationship types, nine property keys, and more than 400 nodes. All within 1MB of database footprint. All database operations are query driven using the powerful and flexible Cypher Graph Query Language.

The download includes CSV files which were created as an interim step after scraping and wrangling the source. The download also includes a working Neo4j Graph Database. Login: neo4j | Password: demo.

Acknowledgements

Data scraped from Awesome Public Datasets page. Prepared for the book Data Science Solutions.

Inspiration

While we have done basic data wrangling and preparation, how can this graph prove useful for your data science workflow? Can we record our data science project decisions taken across workflow stages and how the data catalog (datasources, datasets, tools) use cases help in these decisions by achieving data science solutions strategies?

Kickstart a chemical graph database

I have spent some time scrapping and shaping PubChem data into a Neo4j graph database. The process took a lot of time, mainly downloading, and loading it into Neo4j. The whole process took weeks. If you want to build your own I will show you how to download mine and set it up in less than an hour (most of the time you’ll just have to wait). The process of how this dataset is created is described in the following blogs: - https://medium.com/@nijhof.dns/exploring-neodash-for-197m-chemical-full-text-graph-e3baed9615b8 - https://medium.com/neo4j/combining-3-biochemical-datasets-in-a-graph-database-8e9aafbb5788 - https://medium.com/p/d9ee9779dfbe

What do you get?

The full database is a merge of 3 datasets, PubChem (compounds + synonyms), NCI60 (GI50), and ChEMBL (cell lines). It contains 6 nodes of interest: ● Compound: This is related to a compound of PubChem. It has 1 property. ○ pubChemCompId: The id within pubchem. So “compound:cid162366967” links to https://pubchem.ncbi.nlm.nih.gov/compound/162366967. This number can be used with both PubChem RDF and PUG. ● Synonym: A name found in the literature. This name can refer to zero, one, or more compounds. This helps find relations between natural language names and absolute compounds they are related to. ○ Name: Natural language name. Can contain letters, spaces, numbers, and any other Unicode character. ○ pubChemSynId: PubChem synonym id as used within the RDF ● CellLine: These are the ChEMBL cell lines. They hold a lot of information. ○ Name: The name of the cell line. ○ Uri: A unique URI for every element within the ChEMBL RDF. ○ cellosaurusId: The id to connect it to the Cellosaurus dataset. This is one of the most extensive cell line datasets out there. ● Measurement: A measurement you can do within a biomedical experiment. Currently, only GI50 (the concentration needed for Growth Inhibition of 50%) is added. ○ Name: Name of the measurement. ● Condition: A single condition of an experiment. A condition is part of an experiment. Examples are: an individual of the control group, a sample with drug A, or a sample with more CO2 ● Experiment: A collection of multiple conditions all done at the same time with the same bias. Meaning we assume all uncontrolled variables are the same. ○ Name: Name of experiment.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F442733%2F7dd804811e105390dfe20bb5cd1a68c0%2FUntitled%20graph.png?generation=1680113457794452&alt=media" alt="">

Overview of the graph design

How do download it Warning, you need 120 GB of free memory. The compressed file you download is already 30 GB. The uncompressed file is 30 GB. The database afterward is 60 GB. 60 GB is only for temporary files, the other 60 is for the database. If you do this on an HDD hard disk it will be slow.

If you load this into Neo4j desktop as a local database (like I do) it will scream and yell at you, just ignore this. We are pushing it far further than it is designed for, but it will still work.

Download the file

Go to this Kaggle dataset and download the dump file. Unzip the file, then delete the zipped file. This part needs 60 GB but only takes 30 by the end of it. Create a database Open the Neo4j desktop app, and click “Reveal files in File Explorer”. Move the .dump you downloaded into this folder.

Click on the ... behind the .dump file and click Create new DBMS from dump. This database is a dump from Neo4j V4, so your database also needs to be V4.x.x!

It will now create the database. This will take a long time, it might even say it has timed out. Do not believe this lie! In the background, it is still running. Every time you start it, it will time out. Just let it run and press start later again. The second time it will be started up directly.

Every time I start it up I get the timed-out error. After waiting 10 minutes and clicking start again the database, and with it, more than 200 million nodes, is ready. And you are done! Good luck and let me know what you build with it

According to Cognitive Market Research, the global Graph Database market size was USD 7.3 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031. Market Dynamics of Graph Database Market

Key Drivers for Graph Database Market

Increasing demand for solutions with the capability to process low-latency queries-One of the main reasons the Graph Database market is extensively being used all over the globe, to the extent that numerous legacy database providers are endeavoring to assimilate graph database schemas into their main relational database infrastructures. Whereas, in theory, the strategy might save money, it might degrade and slow down the performance of queries run beside the database. A graph database is altering traditional brick-and-mortar trades into digital business powerhouses in terms of digital business activities.
Growing usage of graph database technology to drive the Graph Database market's expansion in the years ahead.

Key Restraints for Graph Database Market

Complex programming and standardization pose a serious threat to the Graph Database industry.
The market also faces significant difficulties related to low-cost clusters.

Introduction of the Graph Database Market

The graph database market has experienced significant growth due to the increasing need for efficient data management and complex relationship mapping in various industries. Unlike traditional relational databases, graph databases excel in handling interconnected data, making them ideal for applications such as social networks, fraud detection, recommendation engines, and supply chain management. Key drivers of this market include the rising adoption of big data analytics, advancements in artificial intelligence, and the proliferation of connected devices. Leading players, such as Neo4j, Amazon Web Services, and Microsoft, continue to innovate, offering scalable and robust graph database solutions. The growing demand for real-time, low-latency data processing capabilities further propels the market's expansion.

This dataset is based on the model developed with the Ph.D. students of the Communication and Information Sciences Ph.D. program at the University of Hawaii at Manoa, intended to help new students get relevant information. The model was first presented at the iConference 2023, in a paper "Community Design of a Knowledge Graph to Support Interdisciplinary Ph.D. Students " by Stanislava Gardasevic and Rich Gazan (available at: https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/9eebcea7-06fd-4db3-b420-347883e6379e/content)The database is created in Neo4J, and the .dump file can be imported to the cloud instance of this software. The dataset (.dump) contains publically available data collected from multiple web locations and indexes of the sample of publications from the people in this domain. Except for that, it contains my (first author's) personal graph demonstrating progress through a student's program in this degree, and activities they have done while in the program. This dataset was made possible with the huge help of my collaborator, Petar Popovic, who ingested the data in the database.The model and dataset were developed while involving the end users in the design and are based on the actual information needs of a population. It is intended to allow researchers to investigate multigraph visualization of the data modeled by the said model.The knowledge graph was evaluated with CIS student population, and the study results show that it is very helpful for decision-making, information discovery, and identification of people in one's surroundings who might be good collaborators or information points. We provide the .json file containing the Neo4J Bloom perspective with styling and queries used in these evaluation sessions.

Graph Database Platform Market Outlook

According to our latest research, the global graph database platform market size reached USD 2.5 billion in 2024, demonstrating robust demand across various sectors. The market is projected to expand at a CAGR of 22.7% from 2025 to 2033, reaching an estimated value of USD 19.1 billion by 2033. This impressive growth is primarily attributed to the increasing need for advanced data analytics, real-time intelligence, and the proliferation of connected data across enterprises worldwide.

A key factor propelling the growth of the graph database platform market is the surging adoption of big data analytics and artificial intelligence in business operations. As organizations manage ever-growing volumes of complex and connected data, traditional relational databases often fall short in terms of efficiency and scalability. Graph database platforms offer a more intuitive and efficient way to model, store, and query highly connected data, enabling faster insights and supporting sophisticated applications such as fraud detection, recommendation engines, and social network analysis. The need for real-time analytics and decision-making is driving enterprises to invest heavily in graph database technologies, further accelerating market expansion.

Another significant driver for the graph database platform market is the increasing incidence of cyber threats and fraudulent activities, especially within the BFSI and e-commerce sectors. Graph databases excel at uncovering hidden patterns, relationships, and anomalies within vast datasets, making them invaluable for fraud detection and risk management. Financial institutions are leveraging these platforms to identify suspicious transactions and prevent financial crimes, while retailers use them to optimize customer experience and personalize recommendations. The versatility of graph databases in supporting diverse use cases across multiple industry verticals is a major contributor to their rising adoption and market growth.

The rapid digital transformation of enterprises, coupled with the shift towards cloud-based solutions, is also fueling the graph database platform market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, allowing organizations to seamlessly integrate graph databases into their existing IT infrastructure. The growing prevalence of Internet of Things (IoT) devices and the emergence of Industry 4.0 have further increased the demand for platforms capable of handling complex, interconnected data. As businesses strive for agility and innovation, graph database platforms are becoming a strategic asset for gaining competitive advantage.

From a regional perspective, North America currently dominates the graph database platform market, driven by the presence of leading technology providers, early adoption of advanced analytics, and substantial investments in digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid economic development, expanding IT sectors, and increasing awareness of data-driven decision-making. Europe also holds a significant market share, supported by strong regulatory frameworks and widespread digital transformation initiatives. The market landscape is highly dynamic, with regional trends influenced by technological advancements, regulatory policies, and industry-specific demands.

Component Analysis

The graph database platform market is segmented by component into software and services. The software segment holds the largest share, as organizations increasingly deploy advanced graph database solutions to manage and analyze complex data relationships. These software platforms provide robust features such as data modeling, visualization, and high-performance querying, enabling users to derive actionable insights from connected data. Vendors are continuously enhancing their offerings with AI and machine learning capabilities, making graph database software indispensable for modern data-driven enterprises.
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This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

Graph Database for Security Market Outlook

As per our latest research, the global Graph Database for Security market size is valued at USD 1.7 billion in 2024, with robust growth driven by increasing cybersecurity threats and the need for advanced data analytics. The market is exhibiting a strong compound annual growth rate (CAGR) of 22.4% from 2025 to 2033. By 2033, the market is forecasted to reach an impressive USD 11.1 billion. This growth is primarily attributed to the rapid adoption of graph database technologies in security applications, the rising complexity of cyberattacks, and the demand for real-time threat detection and response capabilities in organizations worldwide.

One of the most significant growth factors for the Graph Database for Security market is the escalating sophistication and frequency of cyber threats across industries. Traditional relational databases often fall short in mapping complex relationships and detecting hidden patterns within vast datasets. Graph databases, on the other hand, offer a flexible and highly efficient way to analyze interconnected data, making them invaluable for security applications such as threat intelligence and fraud detection. Organizations are increasingly leveraging graph technology to uncover previously undetectable attack vectors, trace the origins of security breaches, and proactively mitigate risks. The ability to visualize and traverse relationships in real time has become a critical asset, particularly as threat actors employ more advanced and coordinated tactics.

Another key driver is the surge in digital transformation initiatives and the proliferation of connected devices, which have expanded the attack surface for enterprises. As businesses migrate to cloud environments and adopt hybrid IT infrastructures, the complexity of managing security increases exponentially. Graph databases enable security teams to monitor user behavior, access patterns, and network relationships more effectively, supporting advanced use cases such as identity and access management (IAM) and risk and compliance management. The integration of AI and machine learning with graph databases further enhances their analytical capabilities, empowering organizations to automate anomaly detection and streamline incident response processes. This technological synergy is fostering rapid market adoption, especially among sectors with stringent regulatory requirements.

The growing regulatory landscape and compliance mandates are also propelling the demand for graph database solutions in security. Regulations such as GDPR, HIPAA, and CCPA require organizations to maintain comprehensive audit trails, ensure data privacy, and demonstrate robust security controls. Graph databases provide a transparent and auditable framework for tracking data lineage, access permissions, and policy enforcement across complex IT ecosystems. This capability not only helps organizations achieve compliance but also strengthens their overall security posture. As regulatory scrutiny intensifies, companies are prioritizing investments in advanced analytics platforms that can deliver both operational efficiency and compliance assurance.

From a regional perspective, North America continues to dominate the Graph Database for Security market due to its early adoption of advanced cybersecurity technologies and the presence of major technology providers. The region’s strong emphasis on innovation, coupled with high cybersecurity spending, positions it as a key growth engine for the market. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing cyber threats, and government-led cybersecurity initiatives. Europe also holds a significant market share, supported by strict data protection regulations and a mature IT infrastructure. Collectively, these regional dynamics are shaping the global landscape and fueling sustained market expansion.

Component Analysis

The Component segment of the Graph Databa

Service Topology Graph Database Market Outlook

According to our latest research, the global service topology graph database market size reached USD 1.42 billion in 2024, demonstrating robust momentum with a compound annual growth rate (CAGR) of 21.8%. The market is expected to achieve a value of USD 10.62 billion by 2033. This impressive growth is primarily driven by the increasing demand for advanced data management solutions, the proliferation of complex IT infrastructures, and the rising necessity for real-time analytics and visualization across diverse industries. The market’s rapid expansion is further bolstered by technological advancements in graph database architectures and the growing adoption of cloud-based deployment models.

One of the most significant growth factors in the service topology graph database market is the escalating complexity of modern IT environments. As organizations transition toward hybrid and multi-cloud infrastructures, the need for solutions that can accurately map and manage intricate service relationships has become paramount. Graph databases excel at representing highly interconnected data, making them ideal for modeling service topologies. This capability enables enterprises to visualize dependencies, identify bottlenecks, and optimize resource allocation, thereby enhancing operational efficiency and minimizing downtime. Additionally, the growing integration of artificial intelligence and machine learning with graph databases allows for predictive analytics and automated anomaly detection, further fueling market growth.

Another key driver is the surge in demand for enhanced network management and security. With the increasing frequency and sophistication of cyber threats, organizations are seeking comprehensive solutions to monitor and secure their networks. Service topology graph databases provide unparalleled visibility into network structures, enabling proactive identification of vulnerabilities and facilitating rapid incident response. These databases support real-time monitoring and compliance tracking, which are critical for industries with stringent regulatory requirements such as BFSI and healthcare. The ability to correlate data from multiple sources and uncover hidden patterns is proving invaluable for security teams, making graph databases an essential component of modern cybersecurity strategies.

The expanding adoption of digital transformation initiatives across various sectors also contributes to the market’s growth. Enterprises are leveraging service topology graph databases to streamline asset management, optimize IT operations, and improve customer experiences. In the retail sector, for example, these databases help map customer journeys and personalize interactions by analyzing relationships between products, users, and transactions. In manufacturing, they facilitate predictive maintenance and supply chain optimization by modeling equipment dependencies and process flows. As organizations continue to prioritize data-driven decision-making, the demand for graph-based solutions is expected to rise significantly, further propelling the market forward.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of major technology vendors, early adoption of advanced IT solutions, and significant investments in research and development. Europe follows closely, driven by stringent data privacy regulations and the need for efficient compliance management. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud computing. Latin America and the Middle East & Africa are also experiencing steady growth, supported by government initiatives to modernize public services and enhance cybersecurity capabilities.

Component Analysis

The component segment of the service topology graph database market is bifurcated into software and services, each playing a pivotal role in driving overall market expansion. The software sub-segment dominates the market, owing to the continuous evolution of graph database platforms that offer enhanced scalability, flexibility, and integration capabilities. Modern graph database software solutions are equipped with advanced visualization tools, intuitive user interfaces, and robust APIs, enabling seamless in

Graph Database for Grid Topology Market Outlook

According to our latest research, the global graph database for grid topology market size reached USD 1.32 billion in 2024, reflecting robust adoption across the energy, utility, and industrial sectors. The market is experiencing a healthy growth rate with a CAGR of 19.7% during the forecast period, and is projected to reach USD 5.26 billion by 2033. This growth is primarily attributed to the increasing complexity of modern grid systems, rising investments in smart grid infrastructure, and the need for real-time analytics to ensure efficient energy distribution and management.

One of the primary growth factors propelling the graph database for grid topology market is the escalating complexity of power grids worldwide. As energy networks become more decentralized and integrate renewable sources, traditional relational databases struggle to model and analyze the intricate interconnections between grid components. Graph databases, with their inherent ability to represent and query complex relationships, are increasingly being leveraged for real-time grid topology mapping, fault detection, and dynamic energy routing. This technological shift is reinforced by the growing demand for grid resilience and reliability, especially in regions vulnerable to outages and fluctuating energy supply, thus driving greater market adoption.

Another significant driver is the global surge in smart grid deployments. Governments and utility providers are investing heavily in smart grid technologies to enhance energy efficiency, reduce operational costs, and meet stringent regulatory requirements. Graph database solutions offer unparalleled capabilities in managing large volumes of interconnected data generated by smart meters, IoT sensors, and distributed energy resources. The ability to perform rapid, scalable queries and visualize grid relationships in real time is critical for predictive maintenance, asset management, and timely fault localization. As a result, the adoption of graph databases is becoming a cornerstone of digital transformation initiatives within the energy and utility sectors.

Furthermore, the market is benefiting from the increasing convergence of IT and operational technologies (OT) in grid management. The integration of advanced analytics, artificial intelligence, and machine learning with graph database platforms is enabling more sophisticated applications such as anomaly detection, cybersecurity, and predictive analytics. These innovations are not only improving grid reliability but also enabling new business models, such as peer-to-peer energy trading and demand response programs. The growing ecosystem of technology vendors, system integrators, and cloud service providers is further accelerating the deployment of graph database solutions in grid topology applications, creating a dynamic and competitive market landscape.

From a regional perspective, North America currently leads the graph database for grid topology market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is witnessing significant investments in grid modernization and smart infrastructure, supported by favorable regulatory frameworks and government funding. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid urbanization, expanding energy demand, and ambitious renewable energy targets in countries such as China, India, and Japan. Europe’s market growth is underpinned by the region’s strong focus on sustainability, grid integration, and cross-border energy exchange. These regional dynamics are shaping the competitive landscape and innovation trajectory of the global market.

Component Analysis

The graph database for grid topology market is segmented by component into software and services, each playing a pivotal role in delivering comprehensive solutions to end-users. The software segment dominates the market, accounting for the majority of revenue in 2024, as orga

Graph Databases for Fraud Detection Market Outlook

As per our latest research, the global Graph Databases for Fraud Detection market size in 2024 stands at USD 1.12 billion, with the market demonstrating robust momentum. The sector is experiencing a compound annual growth rate (CAGR) of 22.7%, positioning it for substantial expansion. By 2033, the market is forecasted to reach a remarkable USD 8.65 billion, driven by the escalating sophistication of fraudulent activities and the increasing adoption of advanced analytics and artificial intelligence across industries. The primary growth factor is the urgent need for real-time, scalable, and highly accurate fraud detection solutions that can adapt to evolving threat landscapes, especially in sectors such as BFSI, retail, and healthcare.

The proliferation of digital transactions, e-commerce, and online banking has led to a surge in complex fraud schemes, necessitating the deployment of advanced technologies like graph databases. These databases excel at mapping intricate relationships and patterns across vast datasets, making them indispensable in detecting organized fraud rings, identity theft, and money laundering operations. Their ability to visualize and analyze interconnected data points in real time significantly enhances the accuracy and speed of fraud detection, minimizing false positives and enabling proactive risk mitigation. Moreover, the integration of graph databases with machine learning and AI algorithms has further amplified their effectiveness, allowing organizations to uncover hidden patterns and anticipate fraudulent behaviors before they result in significant financial losses.

Another key growth driver for the graph databases for fraud detection market is the increasing regulatory scrutiny and compliance requirements imposed by governments and international bodies. Financial institutions, healthcare providers, and e-commerce platforms are under mounting pressure to comply with anti-money laundering (AML), know your customer (KYC), and data privacy regulations. Graph databases provide the agility and transparency needed to trace the origin and flow of transactions, ensuring robust audit trails and facilitating regulatory reporting. As regulatory frameworks continue to evolve and penalties for non-compliance become more stringent, organizations are investing heavily in advanced fraud detection infrastructure, further fueling market growth.

The rapid advancements in cloud computing and the widespread adoption of Software-as-a-Service (SaaS) models have also played a pivotal role in democratizing access to graph database solutions for fraud detection. Cloud-based deployment offers scalability, cost-effectiveness, and ease of integration with existing IT ecosystems, making it an attractive option for both large enterprises and small-to-medium businesses. Furthermore, the rise of API-driven architectures and microservices has enabled seamless interoperability between graph databases and other analytics tools, enhancing their utility across diverse industry verticals. As digital transformation accelerates globally, the demand for flexible, cloud-native fraud detection solutions is expected to witness exponential growth.

Regionally, North America continues to dominate the graph databases for fraud detection market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The presence of major financial institutions, advanced technological infrastructure, and a robust regulatory environment are key factors driving adoption in these regions. However, emerging economies in Asia Pacific and Latin America are rapidly catching up, propelled by the digitalization of banking and commerce, rising cybercrime rates, and increased investments in cybersecurity. The Middle East and Africa are also witnessing steady growth, albeit from a smaller base, as governments and enterprises prioritize fraud prevention as part of their digital agendas.

Component Analysis

The component segment of the graph databases

Data used for modeling CityJSON into a graph database structure. It also includes asset management maintenance records to enable semantic enrichment through a graph-based approach. This dataset supports the demonstration of CityJSON graph analytics use cases in the context of asset management.

Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues.

Biologists and biochemists have at their disposal a number of excellent, publicly available data resources such as UniProt, KEGG, and NCBI Taxonomy, which catalogue biological entities. Despite the usefulness of these resources, they remain fundamentally unconnected. While links may appear between entries across these databases, users are typically only able to follow such links by manual browsing or through specialised workflows. Although many of the resources provide web-service interfaces for computational access, performing federated queries across databases remains a non-trivial but essential activity in interdisciplinary systems and synthetic biology programmes. What is needed are integrated repositories to catalogue both biological entities and–crucially–the relationships between them. Such a resource should be extensible, such that newly discovered relationships–for example, those between novel, synthetic enzymes and non-natural products–can be added over time. With the introduction of graph databases, the barrier to the rapid generation, extension and querying of such a resource has been lowered considerably. With a particular focus on metabolic engineering as an illustrative application domain, biochem4j, freely available at http://biochem4j.org, is introduced to provide an integrated, queryable database that warehouses chemical, reaction, enzyme and taxonomic data from a range of reliable resources. The biochem4j framework establishes a starting point for the flexible integration and exploitation of an ever-wider range of biological data sources, from public databases to laboratory-specific experimental datasets, for the benefit of systems biologists, biosystems engineers and the wider community of molecular biologists and biological chemists.

Graph Data Integration Platform Market Outlook

According to our latest research, the global graph data integration platform market size reached USD 2.1 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 18.4% from 2025 to 2033, reaching approximately USD 10.7 billion by 2033. This significant growth is fueled by the increasing need for advanced data management and analytics solutions that can handle complex, interconnected data across diverse organizational ecosystems. The rapid digital transformation and the proliferation of big data have further accelerated the demand for graph-based data integration platforms.

The primary growth factor driving the graph data integration platform market is the exponential increase in data complexity and volume within enterprises. As organizations collect vast amounts of structured and unstructured data from multiple sources, traditional relational databases often struggle to efficiently process and analyze these data sets. Graph data integration platforms, with their ability to map, connect, and analyze relationships between data points, offer a more intuitive and scalable solution. This capability is particularly valuable in sectors such as BFSI, healthcare, and telecommunications, where real-time data insights and dynamic relationship mapping are crucial for decision-making and operational efficiency.

Another significant driver is the growing emphasis on advanced analytics and artificial intelligence. Modern enterprises are increasingly leveraging AI and machine learning to extract actionable insights from their data. Graph data integration platforms enable the creation of knowledge graphs and support complex analytics, such as fraud detection, recommendation engines, and risk assessment. These platforms facilitate seamless integration of disparate data sources, enabling organizations to gain a holistic view of their operations and customers. As a result, investment in graph data integration solutions is rising, particularly among large enterprises seeking to enhance their analytics capabilities and maintain a competitive edge.

The surge in regulatory requirements and compliance mandates across various industries also contributes to the expansion of the graph data integration platform market. Organizations are under increasing pressure to ensure data accuracy, lineage, and transparency, especially in highly regulated sectors like finance and healthcare. Graph-based platforms excel in tracking data provenance and relationships, making it easier for companies to comply with regulations such as GDPR, HIPAA, and others. Additionally, the shift towards hybrid and multi-cloud environments further underscores the need for robust data integration tools capable of operating seamlessly across different infrastructures, further boosting market growth.

From a regional perspective, North America currently dominates the graph data integration platform market, accounting for the largest share due to early adoption of advanced data technologies, a strong presence of key market players, and significant investments in digital transformation initiatives. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid industrialization, expanding IT infrastructure, and increasing adoption of cloud-based solutions among enterprises in countries like China, India, and Japan. Europe also remains a significant contributor, supported by stringent data privacy regulations and a mature digital economy.

Component Analysis

The component segment of the graph data integration platform market is bifurcated into software and services. The software segment currently commands the largest market share, reflecting the critical role of robust graph database engines, visualization tools, and integration frameworks in managing and analyzing complex data relationships. These software solutions are designed to deliver high scalability, flexibility, and real-time proces

This dataset was constructed as a small example of a graph mimicking an e-commerce site where people can also follow each others.

The files here reported can be used to build a property graph in Neo4J:

• item.csv - contains the data for the Item nodes.
• person.csv - contains the data for the Person nodes.
• category.csv - contains the data for the Category nodes.
• follow.csv - contains the data for the FOLLOW relationships from Person to Person nodes.
• buy.csv - contains the data for the BUY relationships from Person to Item nodes.
• reccomend.csv - contains the data for the RECOMMEND relationship from Person to Item nodes.
• of.csv - contains the data for the OF relationship from Item to Category nodes.

This data was used as motivating example dataset in the paper "MINE GRAPH RULE: A New GQL Operator for Mining Association Rules in Property Graph Databases".

NoSQL Software Market Outlook

The global NoSQL software market size was valued at approximately USD 6 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a compound annual growth rate (CAGR) of 14% during the forecast period. This market is driven by the escalating need for operational efficiency, flexibility, and scalability in database management systems, particularly in enterprises dealing with vast amounts of unstructured data.

One of the primary growth factors propelling the NoSQL software market is the exponential increase in data volumes generated by various digital platforms, IoT devices, and social media. Traditional relational databases often struggle to handle this surge efficiently, prompting organizations to shift towards NoSQL databases that offer more flexibility and scalability. The ability to store and process large sets of unstructured data without needing a predefined schema makes NoSQL databases an attractive choice for modern businesses seeking agility and speed in data management.

Moreover, the proliferation of cloud computing services has significantly contributed to the growth of the NoSQL software market. Cloud-based NoSQL databases provide cost-effective, scalable, and easily accessible solutions for enterprises of all sizes. The pay-as-you-go pricing model and the capacity to scale resources based on demand have made NoSQL databases a preferred option for startups and large enterprises alike. The seamless integration of NoSQL databases with cloud infrastructure enhances operational efficiencies and reduces the complexities associated with database management.

Another critical driver is the increasing adoption of NoSQL databases in various industry verticals such as retail, BFSI, IT, and healthcare. These industries require robust data management solutions to handle large volumes of diverse data types. NoSQL databases, with their flexible data models and high performance, cater to these requirements efficiently. In the retail sector, for example, NoSQL databases are used to manage customer data, product catalogs, and transaction histories, enabling more personalized and efficient customer services.

Regionally, North America holds a significant share of the NoSQL software market due to the presence of major technology companies and a mature IT infrastructure. The rapid digital transformation across enterprises in the region, alongside substantial investments in big data analytics and cloud computing, further fuels market growth. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the expanding IT sector, increased adoption of cloud services, and significant investments in digital technologies in countries like China and India.

Graph Databases Software has emerged as a crucial component in the landscape of NoSQL databases, particularly for applications that require understanding complex relationships between data entities. Unlike traditional databases that store data in tables, graph databases use nodes, edges, and properties to represent and store data, making them ideal for scenarios where relationships are as important as the data itself. This approach is particularly beneficial in fields such as social networking, where the ability to analyze connections between users can provide deep insights into social dynamics and influence patterns. As businesses increasingly seek to leverage data for competitive advantage, the demand for graph databases is expected to grow, driven by their ability to efficiently model and query interconnected data.

Type Analysis

The NoSQL software market is segmented into various types, including Document-Oriented, Key-Value Store, Column-Oriented, and Graph-Based databases. Document-oriented databases, such as MongoDB, store data in JSON-like documents, offering flexibility in data modeling and ease of use. These databases are widely used for content management systems, e-commerce applications, and real-time analytics. Their ability to handle semi-structured data and scalability features make them a popular choice among developers and enterprises seeking agile database solutions.

Key-Value Store databases, such as Redis and Amazon DynamoDB, store data as a collection of key-value pairs, providing ultra-fast read and write operations. These databases are ideal for applications requiring high-speed data retrieval, such as caching, session manag

Managed Neo4j Services Market Outlook

According to our latest research, the global managed Neo4j services market size reached USD 423 million in 2024, reflecting robust demand for graph database solutions across diverse industries. The market is projected to expand at a CAGR of 20.1% from 2025 to 2033, reaching a forecasted value of USD 2.23 billion by 2033. This remarkable growth trajectory is driven by the increasing adoption of connected data analytics, rising digital transformation initiatives, and the need for scalable, flexible, and managed database solutions across enterprises worldwide.

One of the primary growth factors fueling the managed Neo4j services market is the exponential rise in data complexity and interconnectedness within enterprise environments. Organizations are increasingly recognizing the limitations of traditional relational databases in handling highly connected data, such as social networks, fraud detection, recommendation engines, and supply chain management. Managed Neo4j services, leveraging the power of graph databases, enable businesses to model, store, and analyze complex relationships efficiently. The growing need for real-time insights, enhanced customer experiences, and advanced analytics capabilities is pushing enterprises to adopt managed Neo4j solutions, as these services offer seamless integration, scalability, and expert support for mission-critical applications.

Another significant driver for the managed Neo4j services market is the widespread shift towards cloud-based and hybrid IT infrastructures. As organizations migrate their workloads to the cloud, managed services become essential for ensuring optimal performance, security, and cost-effectiveness. Managed Neo4j providers offer end-to-end solutions, including consulting, implementation, support, and training, which alleviate the burden on internal IT teams and accelerate time-to-value. The increasing prevalence of multi-cloud strategies, combined with the need for high availability and disaster recovery, further enhances the appeal of managed Neo4j services. Enterprises are also prioritizing compliance and data governance, and managed service providers are well-positioned to deliver solutions that meet regulatory requirements while enabling innovation.

The managed Neo4j services market is also benefiting from the surge in artificial intelligence, machine learning, and big data analytics initiatives across industries. Graph databases like Neo4j are uniquely suited to support advanced analytics use cases, such as knowledge graphs, identity and access management, and network analysis. As organizations seek to unlock the value of their data assets, managed Neo4j services provide the expertise, tools, and ongoing support needed to deploy and scale graph-based applications. The rise of digital ecosystems, IoT integration, and API-driven architectures is further expanding the addressable market for managed Neo4j services, as enterprises aim to stay competitive in a rapidly evolving digital landscape.

From a regional perspective, North America continues to dominate the managed Neo4j services market, accounting for the largest share in 2024, driven by early technology adoption, a mature IT services sector, and strong investments in data-driven initiatives. However, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR exceeding 24% during the forecast period, fueled by rapid digitalization, expanding cloud adoption, and government-led innovation programs. Europe, Latin America, and the Middle East & Africa are also witnessing increased demand for managed Neo4j solutions, as enterprises across these regions embrace graph databases to enhance operational efficiency, customer engagement, and compliance.

Service Type Analysis

The managed Neo4j services market is segmented by service type into consulting, implementation, support & maintenance, and training. Consulting services represent a critical entry point for organizations embarking on their

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Use cases

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This folder contains all the datasets used for the performance evaluation of the MINE GRAPH RULE operator proposed in the paper "MINE GRAPH RULE: A New GQL Operator for Mining Association Rules in Property Graph Databases".

Each folder contains the following files used to create a property graph in Neo4j with a fixed schema mimicking an e-commerce site.

• Item.csv - contains the data for the Item nodes.
• Person.csv - contains the data for the Person nodes.
• Category.csv - contains the data for the Category nodes.
• FOLLOW.csv - contains the data for the FOLLOW relationships from Person to Person nodes.
• BUY.csv - contains the data for the BUY relationships from Person to Item nodes.
• RECOMMEND.csv - contains the data for the RECOMMEND relationship from Person to Item nodes.
• OF.csv - contains the data for the OF relationship from Item to Category nodes.

The folders contain various graph instances with differing dimensions, and each folder is named to reflect its defining features. The features in the name are given in this order:

• Total number of nodes within the graph.
• Ratio of the number of Person nodes over the nodes with other labels.
• Probability of having a relationship FOLLOW between two Person nodes.
• Probability of having a relationship BUY between a Person node and an Item node.
• Probability of having a relationship RECOMMEND between a Person node and an Item node.

(Example: the folder 10000_0.5_0.0005_0.1_0.0005_dataset contains files of a graph with 10000 nodes, of which half of them are Person nodes, 0.0005 is the probability of having a relationship FOLLOW between two Person nodes, 0.1 is the probability of having a relationship BUY between a Person node and an Item node, and 0.0005 is the probability of having a relationship RECOMMEND between a Person node and an Item node).