The major vendors offering graph database tools and services include IBM (US), Microsoft (US), Oracle (US), AWS (US), Neo4j (US), OrientDB (UK), TIBCO Software (US), Franz (US), Teradata Corporation (US), OpenLink Software (US), MarkLogic (US), TigerGraph (US), DataStax (US), Ontotext (Bulgaria), Stardog (US), Cray (US), ArangoDB (Germany), Sparcity Technologies (Spain), Memgraph (UK), Bitnine (Korea), Objectivity (US), MongoDB (US), Cambridge Semantics (US), Fluree (US), and Blazegraph (US).The global graph database market is expected to gain significant traction and is projected to grow at a Compound Annual Growth Rate (CAGR) of 24.0% during the forecast period to reach a market size of USD 2,409.1 million by 2023. Major factors for the market growth include the incorporation of AI technology in the graph database software and services and an increasing need for better response time and accuracy to discover new data correlations.
The report "Graph Database Market by Type (RDF and Property Graph), Application (Risk Management & Fraud Detection, Customer Analytics, Recommendation Engines), Component (Tools and Services), Deployment Type, Industry Vertical, and Region - Global Forecast to 2023", defines, describes, and forecasts the global market. The report analyzes the major factors influencing the market growth, such as drivers, restraints, opportunities, and challenges. Moreover, the report tracks and analyzes competitive developments, including partnerships, collaborations, acquisitions, new product developments, and R&D activities in the market.
Graph compute engine is a technology that enables global graph computational algorithms to run against large and complex datasets. These technologies are designed to identify clusters in the data, and are normally optimized for scanning and processing large volumes of data in batches. Various types of graph compute engines include in-memory and distributed graph compute engines, such as Cassovary, Pegasus, or Giraph. Moreover, in a graph database model, nodes possess different types of relationships that represent relationships between the domain entities. All the nodes in a dataset can be represented in a single graph database as there is no limit to the number and kind of relationships in a node. Graph databases derive their value from the relationships. The intelligence properties on the relationships between nodes are used to query the graph.
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The on-premises deployment model is expected to be adopted at a high rate as compared to on-demand deployment model. The on-premises deployment model is expected to dominate the market during the forecast period, as it helps users have control over the platform, software, and data.
Small and Medium-sized Enterprises (SMEs) are organizations with less than 1,000 employees. SMEs are focusing on deploying advanced graph database solutions that supplement the deployment of Master Data Management (MDM) solutions to secure and govern their data as per the regulatory aspects. Effective data management solutions enable market vendors to provide cost-effective graph database tools to meet their customer requirements.
Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. Many Asian countries, including China and Singapore, are leveraging information-intensive technologies to gain a competitive edge over other players. They are digitizing and promoting the emerging technologies, such as mobility, IoT, cloud, BI, and analytics. This has provided significant exposure to graph database tools and services in this region. The rapid digitization among these countries is resulting in the production of large volumes unstructured data. The region has shown an unexplored potential for the adoption of data security solutions, which can be attributed to the evolving regulations that are encouraging many multinational operators to enter into the graph database market for business expansion.
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