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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 rising popularity of open knowledge networks reflects a broader trend towards the integration and accessibility of connected data. As organizations and researchers seek more efficient ways to manage and utilize information, open knowledge networks have emerged as a crucial tool. These networks facilitate the connection of diverse data sources, enabling more comprehensive and insightful analyses. A significant factor driving this trend is the increasing demand for connected data as well as cyber security, which allows for a more holistic understanding of complex relationships and patterns. Additionally, advancements in graph database technologies are enhancing data modeling capabilities, further fueling interest in open knowledge networks. Graph databases offer advanced functionalities for representing and querying intricate data relationships, making them ideal for building and exploring knowledge networks. Together, these developments growing interest in open knowledge networks, the need for interconnected data, and the enhanced capabilities of graph databases are shaping the future of master data management. The market forecast report provides market size, historical data spanning from 2018 - 2022, and future projections, all presented in terms of value in USD billion for each of the mentioned segments.
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The market is pivotal in revolutionizing enterprise data management and analytics, offering powerful data visualization tools and data modeling. It supports diverse sectors including the finance and logistics industries, optimizing business processes through cloud regions and data centers. Graph databases utilize the property graph model, organizing data into vertices with labels and efficient indexes for quick access. They excel in handling long tasks and executing stored procedures, crucial for real-time analytics in medical information and disease surveillance. For route optimization and warehouse management, they empower logistics professionals with insights from social networks and recommendation engines. Despite challenges like lack of standardization, graph databases remain indispensable for complex data management and fostering innovation in graph technology.
Lack of standardization and programming flexibility is the key driver for the growth of the global graph database market. Graph databases may be significantly affected by the emergence of the open knowledge network (OKN). Using open data and semantic technologies, the open knowledge network (OKN) aims to establish a global, interconnected network of knowledge. It fosters collaboration, innovation, and the sharing of knowledge by allowing the integration and discovery of information from various sources.
Moreover, the OKN aimed to integrate significant data from diverse sources and link them together based on shared concepts. Graph databases excel at combining heterogeneous networks and making connections between related entities. They have features of linking data across different domains, enabling a more comprehensive and interconnected knowledge network. Thus, the emergence of the open knowledge network can fuel the demand for graph databases as a foundational technology to support the integration, querying, and discovery of interconnected knowledge, which, in turn, is expected to boost the growth of the global market in focus during the forecast period.
Increased demand for low-latency queries is the primary trend in the global graph database market. The ability of a database system to respond quickly to queries, typically within milliseconds or even microseconds, is referred to as low-latency query capabilities. The rising demand for low-latency query capabilities in various industries is driven by the need for real-time or near-real-time access to data and the ability to quickly process large volumes of information.
Moreover, the demand for low-latency query capabilities continues to grow due to the graph database vendors and open-source communities. These communities are actively implementing optimizations to improve query response times. These advancements, combined with hardware, address the need for real-time or near-real-time query performance in graph databases. Low latency query capabilities in graph databases enable organizations to access and analyze interconnected data quickly, allowing real-time decision making which is expected to boost the growth of the global graph database market during the forecast period.
Open knowledge network gaining popularity is the major challenge for the global graph database market. During the forecast period, the absence of standardized interfaces, query languages, and application programming interfaces (APIs) is anticipated to grow into a growing ecosystem in which various vendors will offer their own exclusive solutions. Users have difficulty selecting a graph database that meets their specific requirements and seamlessly integrating various components due to this fragmentation.
Moreover, without standardized query languages and APIs, developers are expected to have to work with multiple graph database-specific languages and frameworks during the forecast period. The lack of standardization can result in vendor lock-in, where organizations become heavily dependent on a specific graph database vendor owing to the absence of interoperability standards. This lock-in restricts the flexibility to switch vendors, making it difficult to adapt to take advantage of emerging technologies. The complexity and learning curve associated with proprietary query languages and APIs and concerns about vendor lock-in can deter potential users from exploring and adopting graph databases, slowing down market growth during the forecast period.
The market forecasting report includes the adoption lifecycle of the market, covering from the innovator’s stage to the laggard’s stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.
Customer Landscape
Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
Amazon Inc: The company offers database such as Amazon neptune graph data, which is similar to a Resource Description Framework RDF quad.
The market research and growth report also includes detailed analyses of the competitive landscape of the market and information about key companies, including:
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
The market 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.
The large enterprises segment is estimated to witness significant growth during the forecast period. Large enterprises database often deal with complex and interconnected data that can be challenging to represent and query using traditional relational databases. Graph databases provide a natural and intuitive way to model and store relationships between data entities, enabling more effective analysis and insights. In order to make well-informed business decisions, businesses frequently require real-time analytics and insights. Fast querying and analysis are made possible by graph databases, allowing businesses to get valuable insights from their data in real time.
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The large enterprises segment accounted for USD 2.20 billion in 2018. An Enterprise Graph Database refers to a specialized type of database designed to manage and store data in a graph format. The term "enterprise" in Enterprise Graph Database typically indicates scalability, robustness, and suitability for large-scale operations within an organization. It stores information in nodes, edges, and properties, enabling efficient representation of relationships between various entities. Enterprise Graph Databases are used across various sectors and applications, including fraud detection, network and IT operations, recommendation engines, social networks, knowledge graphs, and more.
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North America is estimated to contribute 34% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The growing need for connected data has driven the demand in North America. US businesses are rising the value of leveraging data for gaining actionable insights and making informed decisions. Additionally, organizations can use graph databases to find hidden relationships, patterns, and dependencies in their data, resulting in more accurate and useful analytics. Graph databases are used a lot by industries like finance, healthcare, retail, logistics, and social media to drive their data-driven strategies. As a result, the regional market will expand during the forecast period as a result of the growing demand for advanced analytics, the prevalence of interconnected data, and the increasing use of graph technology.
The market innovates database products with advanced data modeling and analytics tools. It connects people, things, and events across locations for industries like the finance and logistics industries. Graph databases optimize shipment tracking, resolve bottlenecks, and enhance delivery routes by analyzing complex component relationships. They provide efficient services through robust think tanks and facilitate real-time insights into dynamic data scenarios. The graph database market thrives on its ability to simplify programming ease, and break down data silos through enterprise data unification, data center chips and data integration solutions. It addresses challenges like data hoarding by leveraging graph theory and Cypher for efficient data unification. These databases utilize advanced storage engines for optimal performance and incorporate ranking and search engines to enhance data retrieval. With applications across industries such as the finance and logistics industries, they foster technical expertise through engagement in IT technology forums and social networking sites, facilitating insights from job boards and enhancing operational efficiencies.
Market Scope |
|
Report Coverage |
Details |
Base year |
2023 |
Historic period |
2018 - 2022 |
Forecast period |
2024-2028 |
Growth momentum & CAGR |
Accelerate at a CAGR of 24.4% |
Market growth 2024-2028 |
USD 11.81 billion |
Market structure |
Fragmented |
YoY growth 2023-2024(%) |
20.44 |
Regional analysis |
North America, Europe, APAC, South America, and Middle East and Africa |
Performing market contribution |
North America at 34% |
Key countries |
US, China, UK, Germany, and France |
Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
Key companies profiled |
Amazon.com Inc., ArangoDB Inc., DataStax Inc., Dgraph, Franz Inc, InfluxData Inc., JanusGraph , Memgraph Ltd, Microsoft Corp., Neo4j Inc., Ontotext USA Inc., Oracle Corp., Redis Ltd. , Stardog Union Inc., TigerGraph, and vesoft inc |
Market dynamics |
Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, Market condition analysis for forecast period |
Customization purview |
If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by End-user
7 Market Segmentation by Type
8 Customer Landscape
9 Geographic Landscape
10 Drivers, Challenges, and Opportunity/Restraints
11 Competitive Landscape
12 Competitive Analysis
13 Appendix
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