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.

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.

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.

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.

Software Model simulations were conducted using WRF version 3.8.1 (available at https://github.com/NCAR/WRFV3) and CMAQ version 5.2.1 (available at https://github.com/USEPA/CMAQ). The meteorological and concentration fields created using these models are too large to archive on ScienceHub, approximately 1 TB, and are archived on EPA’s high performance computing archival system (ASM) at /asm/MOD3APP/pcc/02.NOAH.v.CLM.v.PX/. Figures Figures 1 – 6 and Figure 8: Created using the NCAR Command Language (NCL) scripts (https://www.ncl.ucar.edu/get_started.shtml). NCLD code can be downloaded from the NCAR website (https://www.ncl.ucar.edu/Download/) at no cost. The data used for these figures are archived on EPA’s ASM system and are available upon request. Figures 7, 8b-c, 8e-f, 8h-i, and 9 were created using the AMET utility developed by U.S. EPA/ORD. AMET can be freely downloaded and used at https://github.com/USEPA/AMET. The modeled data paired in space and time provided in this archive can be used to recreate these figures. The data contained in the compressed zip files are organized in comma delimited files with descriptive headers or space delimited files that match tabular data in the manuscript. The data dictionary provides additional information about the files and their contents. This dataset is associated with the following publication: Campbell, P., J. Bash, and T. Spero. Updates to the Noah Land Surface Model in WRF‐CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition. Journal of Advances in Modeling Earth Systems. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(1): 231-256, (2019).

Pre-trained models described in Charting Brain Growth and Aging at High Spatial Precision. Rutherford et al 2021. Models (and code + tutorials for applying them) are also available in this project's GitHub repository.

Abstract: Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

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.

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.

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.

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.

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.

Big Data Protocolの価格履歴の追跡により、暗号資産投資家は投資のパフォーマンスを簡単に監視することができます。Big Data Protocolの始値、高値、終値、取引量を時系列で簡単に追跡できます。さらに、日々の変化をパーセンテージで即座に表示できるため、大きな変動のあった日を簡単に特定することができます。 Big Data Protocolの価格履歴データによると、その価値は2021-03-07で前例のないピークにまで急騰し、$15.01ドルを超えました。 一方、一般に「Big Data Protocol史上最安値」と呼ばれる、Big Data Protocolの価格軌跡の最低点は2022-12-19に発生しました。 その期間中にBig Data Protocolを購入した場合、現在394%という驚くべき利益を享受していることになります。 設計上、64,923,252.85 Big Data Protocolが作成されます。現時点で、Big Data Protocolの循環供給量は約52,278,856です。 このページに掲載されている価格はすべて、信頼できる情報源であるBitgetから入手したものです。売り手によって価値が異なる可能性があるため、投資のチェックは単一の情報源に頼ることが極めて重要です。 当社のBig Data Protocol価格のヒストリカルデータセットには、1分、1日、1週間、1ヶ月の間隔（始値/高値/安値/終値/出来高）のデータが含まれています。これらのデータセットは、一貫性、完全性、正確性を保証するために厳格なテストを受けています。これらは、取引シミュレーションとバックテスト用に特別に設計されており、無料でダウンロードでき、リアルタイムで更新されます。

Maven Central Dependency Graph

This is an updated version of the artifact at https://zenodo.org/record/1489120

The Maven dependency graph is an open dataset of Maven Central artifacts, their dependencies, as well as other relationships. Its main intent is to domesticate the wild within and around the Maven central ecosystem, in particular, and JVM-based libraries at large, making it more harnessable to both academics and industry. It is intended to answer high-level research questions concerning artifacts releases, evolution, and usage trends over time. It can also be used to assist researchers in selecting relevant datasets, among the mass of existing software artifact, for assessing particular empirical software engineering challenges. The complexity of these questions can range from simple pattern matching to advanced big data analysis and machine learning techniques.

The accompanying paper to this dataset is has been accepted for publication in the proceedings of the International Conference on Mining Software Repositories 2019 and has received the MSR 2019 Data Showcase Award. This paper is available for download on arXiv.

What is new?

The previous version included artifacts until September 6, 2018.
This version includes artifacts until September 10, 2019.

This version includes license information as well as information about associated code repository.

This version contains 4 201 392 artifacts (version) of 308116 distinct libraries from 47481 distinct group IDs.

Note 33 638 artifacts represents version ranges and note actual versions. They can be filtered out by excluding version containing ','.

Usage

Usage:

• Download the archive from zenodo
• Decompress the archive

# Pull the image and start the container
docker run -d --name mm-neo4j -p 7474:7474 -p 7687:7687 -v /path/to/neo4j-data:/data --env=NEO4J_dbms_memory_heap_max_size=8g lyadis/mm-neo4j:latest

• Open http://127.0.0.1:7474/browser/ in your browser

Big Shopping reported ILS81.51M in Selling and Administration Expenses for its fiscal quarter ending in September of 2024. Data for Big Shopping | BIG - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last March in 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.

This dataset groups all the tables supplementing the contents of the article "Data Journals: A Survey", which is going to be published by the Journal of the Association for Information Science and Technology (JASIST). Tables are published with no header. Any details can be found in the article.

Abstract Data occupy a key role in our information society. However, although the amount of published data continues to grow and terms like “data deluge” and “big data” today characterize numerous (research) initiatives, a lot of work is still needed in the direction of publishing data in order to make them effectively discoverable, available, and reusable by others. Several barriers hinder data publishing, from lack of attribution and rewards, vague citation practices, quality issues, to a rather general lack of data sharing culture. Lately, data journals came forward as a solution to overcome some of these barriers. In this study of more than 100 currently existing data journals, we describe the approaches they promote for description, availability, citation, quality and open access or datasets. We close by identifying ways to expand and strengthen the data journals approach as a means to actually promote datasets access and exploitation.

Explore the progression of average salaries for graduates in Big Data Analytics from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Big Data Analytics relative to other fields. This data is essential for students assessing the return on investment of their education in Big Data Analytics, providing a clear picture of financial prospects post-graduation.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.