According to Cognitive Market Research, the global Graph Analytics market size will be USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Market Dynamics of Graph Analytics Market

Key Drivers for Graph Analytics Market

Increasing Recognition of the Advantages of Graph Databases- One of the main reasons for the Graph Analytics market is the increasing recognition of the advantages of graph databases. Unlike traditional relational databases, graph databases excel at handling complex relationships and interconnected data, making them ideal for use cases such as fraud detection, recommendation engines, and social network analysis. Businesses are leveraging these capabilities to uncover insights and patterns that were previously difficult to detect. The rise of big data and the need for real-time analytics are further driving the adoption of graph databases, as they offer enhanced performance and scalability for large-scale data sets. Additionally, advancements in artificial intelligence and machine learning are amplifying the value of graph databases, enabling more sophisticated data modeling and predictive analytics.
Growing Uptake of Big Data Tools to Drive the Graph Analytics Market's Expansion in the Years Ahead.

Key Restraints for Graph Analytics Market

Limited Awareness and Understanding pose a serious threat to the Graph Analytics industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

Graph Analytics Market size was valued at USD 77.1 Million in 2023 and is projected to reach USD 637.1 Million by 2030, growing at a CAGR of 35.1% during the forecast period 2024-2030.

Global Graph Analytics Market Drivers
The market drivers for the Graph Analytics Market can be influenced by various factors. These may include:

Growing Need for Data Analysis: In order to extract insightful information from the massive amounts of data generated by social media, IoT devices, and corporate transactions, there is a growing need for sophisticated analytics tools like graph analytics.

Growing Uptake of Big Data Tools: Graph analytics solutions are becoming more and more popular due to the spread of big data platforms and technology. Businesses are using these technologies to improve the efficiency of their analysis of intricately linked datasets.

Developments in AI and ML: The capabilities of graph analytics solutions are being improved by advances in machine learning and artificial intelligence. These technologies make it possible for recommendation systems, anomaly detection, and forecasts based on graph data to be more accurate.

Increasing Recognition of the Advantages of Graph Databases: Businesses are realizing the advantages of graph databases for handling and evaluating highly related data. Consequently, there’s been a sharp increase in the use of graph analytics tools to leverage the potential of graph databases for diverse applications.

The use of advanced analytics solutions, such as graph analytics, for fraud detection, cybersecurity, and risk management is becoming more and more important as a result of the increase in cyberthreats and fraudulent activity.

Demand for Personalized suggestions: Companies in a variety of sectors are using graph analytics to provide their clients with suggestions that are tailored specifically to them. Personalized recommendations increase consumer engagement and loyalty on social networking, e-commerce, and entertainment platforms.

Analysis of Networks and Social Media is Necessary: In order to comprehend relationships, influence patterns, and community structures, networks and social media data must be analyzed using graph analytics. The capacity to do this is very helpful for security agencies, sociologists, and marketers.

Government programs and Regulations: The need for graph analytics solutions is being driven by regulations pertaining to data security and privacy as well as government programs aimed at encouraging the adoption of data analytics. These tools are being purchased by organizations in order to guarantee compliance and reduce risks.

Emergence of Industry-specific Use Cases: Graph analytics is finding applications in a number of areas, such as healthcare, finance, retail, and transportation. These use cases include supply chain management, customer attrition prediction, and financial fraud detection in addition to patient care optimization.

Technological Developments in Graph Analytics Tools: As graph analytics tools, algorithms, and platforms continue to evolve, their capabilities and performance are being enhanced. Adoption is being fueled by this technological advancement across a variety of industries and use cases.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

This statistic shows the size of the global big data market related to healthcare in 2016 and a forecast for 2025. It is estimated that over this period the market will increase from around 11.5 billion to nearly 70 billion U.S. dollars.

This statistic displays the adoption rates of Big Data analytics in the United Kingdom (UK) in 2015 and 2020. In 2015, the adoption rate amounted to 56 percent across all examined industry. 67 percent of the industries will adopt Big Data analytics in 2020.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

This dataset is about books and is filtered where the book is Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

According to a 2023 global survey, an increasing share of businesses believe they are making effective use of data. Over three-quarters of respondents said that they were driving innovation with data, while half considered their businesses to be competing on data and analytics.

Determining the tectonic setting of unknown volcanic rocks continues to be one of the key challenges in geoscience. While discrimination diagrams have been successfully employed due to their ease of use, recently, validation with big data has raised questions about their performance. In this study, the discrimination boundaries of Th/Yb versus (vs.) Nb/Yb and TiO2/Yb vs. Nb/Yb diagrams, which are the most used types of discrimination diagrams, were redefined based on a large amount of compiled data and support vector machine, a machine learning method. The effectiveness of discrimination diagrams was verified, and the limitations and conditions when using them were clarified. The results show that when using the Th/Yb vs. Nb/Yb diagram, only basalts with Th/Yb ratios higher than the discrimination boundary can be identified as volcanic arcs in origin. In contrast, a significant overlap occurs across boundaries in other cases when using these diagrams, particularly for enriched samples with Nb/Yb ratios higher than five. Therefore, when using these diagrams to determine the tectonic setting of unknown samples, their limitations must be considered when interpreting their results.

The graph database market is experiencing robust growth, projected to reach $5.97 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 24.4% from 2025 to 2033. This expansion is fueled by the increasing need for managing complex, interconnected data across diverse industries. The rising adoption of big data analytics, the demand for improved data visualization and real-time insights, and the need for flexible data modeling are key drivers. Growth is particularly strong in sectors like financial services, where fraud detection and risk management rely on analyzing intricate relationships within data, and in telecommunications, where network optimization and customer relationship management benefit from graph databases' capabilities. Furthermore, the emergence of cloud-based graph database solutions is simplifying deployment and reducing infrastructure costs, thereby accelerating market adoption among both large enterprises and SMEs. The market segmentation reveals significant regional variations, with North America currently dominating due to early adoption and technological advancements, followed by Europe and APAC. However, APAC is expected to witness significant growth in the coming years, driven by increasing digitalization and government initiatives in countries like China and India. The competitive landscape is characterized by a mix of established players like Amazon, Microsoft, and Oracle, and emerging specialized graph database vendors such as Neo4j and TigerGraph. These companies are focusing on enhancing their offerings through continuous innovation in areas such as query performance, scalability, and integration with other data management technologies. The market is also witnessing increasing competition from NoSQL and NewSQL databases offering graph capabilities, leading to a focus on differentiation through specialized features and robust customer support. Industry challenges include the complexities associated with implementing and managing graph databases, the need for specialized skills, and the potential for data security concerns. Despite these challenges, the continued expansion of data volumes and the increasing demand for advanced analytics solutions will drive sustained growth in the graph database market throughout the forecast period.

A systematic literature review (SLR) was carried out to identify the known approaches for data modeling of connected data.

The main contribution of this SLR is an analysis of sixteen works, from 2013 to 2020, in terms of three dimensions: type of contribution, bibliometrics, and data modeling characteristics.

Some parameters of the review and results from data collection were converted into files.

The entire set of works retrieved from DL is also available to allow reproductibility.

This dataset contains bitcoin transfer transactions extracted from the Bitcoin Mainnet blockchain. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: btc-tx- where For example file btc-tx-100000-149999-aa.bz2 and the rest of the parts if any contain transactions from block 100000 to block 149999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: BLOCK TIME FORMAT: The block time file has the following format: IMPORTANT NOTE: Public Bitcoin Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as https://btcscan.org . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/chapter/10.1007/978-3-030-94590-9_14 @incollection{kilicc2022analyzing, title={Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and {\c{S}}en, Alper}, booktitle={Big Data and Artificial Intelligence in Digital Finance}, pages={253--267}, year={2022}, publisher={Springer, Cham} }

Centrifugal compressors are widely used in the oil and natural gas industry for gas compression, reinjection, and transportation. Fault diagnosis and identification of centrifugal compressors are crucial. To promptly monitor abnormal changes in compressor data and trace the causes leading to these data anomalies, this paper proposes a security monitoring and root cause tracing method for compressor data anomalies. Additionally, it presents an intelligent system design method for fault tracing in compressors and localization of faults from different sources. This method starts from petrochemical big data and consists of three parts: fault dynamic knowledge graph construction, instrument data sliding fault-tolerant filtering, and the fusion and reasoning of fault dynamic knowledge graph and instrument data variation monitoring. The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)—SPE (Square Prediction Error)—CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.

This statistic presents the estimated adoption rates of Big Data in the United Kingdom (UK) in 2020, by industry. The report estimated the future adoption rate in the leading retail banking sector at 81 percent. Energy and utility companies were expected to rank second with an adoption rate of 80 percent in 2020. The UK in total was estimated to have an adoption rate of 67 percent.

The accumulated rainfall chart with wide intervals will be updated for download starting from September 15, 2023. Please update before December 31, 2023 as the old links will expire after that. If you need to download a large amount of data, please apply for membership at the Meteorological Data Open Platform https://opendata.cwa.gov.tw/index.

Big Shopping reported 2.45 in Dividend Yield for its fiscal quarter ending in May of 2024. Data for Big Shopping | BIG - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Big Shopping reported ILS40.13B in Assets for its fiscal quarter ending in September of 2024. Data for Big Shopping | BIG - Assets including historical, tables and charts were last updated by Trading Economics this last March in 2025.

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Graph Database Market Size 2024-2028

The graph database market size is forecast to increase by USD 11.81 billion at a CAGR of 24.4% between 2023 and 2028.

The graph database market is experiencing rapid growth, fueled by the increasing importance of understanding complex data relationships. Drivers include the rise of open knowledge networks, enabling sophisticated data analytics and the growing demand for real-time insights through low-latency query processing. Businesses across diverse sectors are leveraging graph databases to enhance customer relationship management, detect fraud, personalize recommendations, and gain competitive advantage. However, the lack of industry standards and the need for specialized expertise present challenges to broader adoption. Despite these hurdles, the market's future remains bright. As organizations increasingly rely on data-driven decision-making and seek to unlock the potential hidden within interconnected data, the graph database market is poised for continued expansion. This report explores key trends, challenges, and opportunities within this dynamic landscape.

What will be the Size of the Graph Database Market During the Forecast Period?

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The market has experienced significant growth in recent years, driven by the increasing demand for advanced data management solutions in various industries. Graph databases, which utilize the property graph model to represent data as interconnected entities or vertices and relationships or edges, offer unique advantages for handling complex, interconnected data. This model is particularly well-suited for applications in social networks, recommendation engines, and business processes that require real-time analytics and visualization. Despite their benefits, graph databases face challenges such as a lack of standardization and the need for specialized skills for programming and managing these databases. However, their ease of use and ability to handle long tasks, stored procedures, and indexes make them an attractive option for industries such as finance, logistics, medical information, and disease surveillance.
Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. Applications of graph databases include route optimization, warehouse management, and logistics management, making them essential tools for logistics professionals.

How is this Graph Database Industry segmented and which is the largest segment?

The graph database industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Large enterprises
  SMEs


Type

  RDF
  LPG


Solution

  Graph Extension
  Graph Processing Engines
  Native Graph Database
  Knowledge Graph Engines


Geography

  North America

    US


  Europe

    Germany
    UK
    France


  APAC

    China


  South America



  Middle East and Africa

By End-user Insights

The large enterprises segment is estimated to witness significant growth during the forecast period.

Graph databases have gained significant traction among large enterprises due to their ability to effectively model and analyze complex, interconnected data. Unlike traditional relational databases, graph databases naturally represent relationships between data entities, facilitating more efficient querying and analysis. This is particularly beneficial for businesses seeking real-time insights, such as customer behavior analysis, personalized marketing, and fraud detection. Graph databases enable fast data processing and offer advanced analytics tools, making them an ideal choice for industries like finance, logistics, and healthcare.

However, the lack of standardization and technical expertise required for graph database implementation can pose challenges. Popular graph database technologies include Cypher and Gremlin, based on the Property Graph model, which utilizes vertices, edges, labels, and indexes for data representation. Integration with recommendation engines, social networks, and data management solutions is essential for maximizing the value of graph databases. Cloud deployments in various data centers and regions provide flexibility and scalability. Despite these advantages, data silos and hoarding remain prevalent issues, necessitating data integration solutions for enterprise data unification.

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The Large enterprises segment was valued at USD 2.2 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 34% to the growth of the global mar

People 25 Years and Over Who Have Completed an Associate's Degree or Higher (5-year estimate) in Big Stone County, MN was 38.00% in January of 2023, according to the United States Federal Reserve. Historically, People 25 Years and Over Who Have Completed an Associate's Degree or Higher (5-year estimate) in Big Stone County, MN reached a record high of 38.00 in January of 2023 and a record low of 23.40 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for People 25 Years and Over Who Have Completed an Associate's Degree or Higher (5-year estimate) in Big Stone County, MN - last updated from the United States Federal Reserve on March of 2025.

Dataset Construction This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (https://github.com/blockchain-etl/bitcoin-etl) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by https://www.walletexplorer.com/ [1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain}, Dataset Description Bitcoin Activity Temporal Coverage: From 03 January 2009 to 25 January 2021 Overview: This dataset provides a comprehensive representation of Bitcoin exchanges between entities over a significant temporal span, spanning from the inception of Bitcoin to recent years. It encompasses various temporal resolutions and representations to facilitate Bitcoin transaction network analysis in the context of temporal graphs. Every dates have been retrieved from bloc UNIX timestamp and GMT timezone. Contents: The dataset is distributed across three compressed archives: All data are stored in the Apache Parquet file format, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package. orbitaal-stream_graph.tar.gz: The root directory is STREAM_GRAPH/ Contains a stream graph representation of Bitcoin exchanges at the finest temporal scale, corresponding to the validation time of each block (averaging approximately 10 minutes). The stream graph is divided into 13 files, one for each year Files format is parquet Name format is orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory STREAM_GRAPH/EDGES/ orbitaal-snapshot-all.tar.gz: The root directory is SNAPSHOT/ Contains the snapshot network representing all transactions aggregated over the whole dataset period (from Jan. 2009 to Jan. 2021). Files format is parquet Name format is orbitaal-snapshot-all.snappy.parquet. These files are in the subdirectory SNAPSHOT/EDGES/ALL/ orbitaal-snapshot-year.tar.gz: The root directory is SNAPSHOT/ Contains the yearly resolution of snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory SNAPSHOT/EDGES/year/ orbitaal-snapshot-month.tar.gz: The root directory is SNAPSHOT/ Contains the monthly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet, where [YYYY] and [MM] stands for the corresponding year and month, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering These files are in the subdirectory SNAPSHOT/EDGES/month/ orbitaal-snapshot-day.tar.gz: The root directory is SNAPSHOT/ Contains the daily resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet, where [YYYY], [MM], and [DD] stand for the corresponding year, month, and day, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering These files are in the subdirectory SNAPSHOT/EDGES/day/ orbitaal-snapshot-hour.tar.gz: The root directory is SNAPSHOT/ Contains the hourly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet, where [YYYY], [MM], [DD], and [hh] stand for the corresponding year, month, day, and hour, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering These files are in the subdirectory SNAPSHOT/EDGES/hour/ orbitaal-nodetable.tar.gz: The root directory is NODE_TABLE/ Contains two files in parquet format, the first one gives information related to nodes present in stream graphs and snapshots such as period of activity and associated global Bitcoin balance, and the other one contains the list of all associated Bitcoin addresses. Small samples in CSV format orbitaal-stream_graph-2016_07_08.csv and orbitaal-stream_graph-2016_07_09.csv These two CSV files are related to stream graph representations of an halvening happening in 2016.