DUBLIN--(BUSINESS WIRE)--The "Global Graph Database Market Size, Share & Industry Trends Analysis Report By Type, By Vertical, By Component, By Deployment Type, By Organization Size, By Application, By Regional Outlook and Forecast, 2022-2028" report has been added to ResearchAndMarkets.com's offering.
The Global Graph Database Market size is expected to reach $8.1 billion by 2028, rising at a market growth of 22.2% CAGR during the forecast period.
A graph database is a single-purpose, specialized platform for building and manipulating graphs. Another often used word for a graph database is graph analytics, which refers to the process of analyzing data in a graph style with data points acting as relationships and nodes acting as edges. A database that can serve graph formats is required for graph analytics. It can be a specialized graph database or a convergent database that supports several data types, including graphs.
Additionally, a graph database is a database that represents and stores data using graph layouts for semantic queries with edges, nodes, and properties. The graph is an important notion in the system (or relationship or edge). In addition, the graph connects the store's data items to a set of edges and nodes, with the edges indicating the nodes" relationships. The relationship enables data in the storage to be immediately connected and, in many circumstances, retrieved in a single operation. The connections between data are prioritized in graph databases. Because relationships are preserved in the database indefinitely, querying them is easy. Graph databases can easily depict connections and make them helpful for material that is extremely interconnected.
Graph databases are often referred to as NoSQL databases. Graph databases are identical to conventional network model databases and also represent general graphs, however, network-model databases function at a low level of abstraction and dearth of straightforward traversal through a chain of edges.
Market Growth Factors
Rising demand for solutions with the ability to process low-latency queries
Graph database services and tools are widely being utilized all over the world, to the extent that several legacy database providers are attempting to integrate graph database schemas into their prevailing relational database infrastructures. While the strategy might appear to save money in theory, it might actually slow down and degrade the performance of queries run against the database. A graph database is changing traditional brick-and-mortar businesses into digital business powerhouses in terms of digital business activities. Companies face issues when it comes to storing large amounts of connected data in the database that isn't appropriate for the task at hand.
The advent of open knowledge networks
Knowledge networks must have datasets, methods, and documentation to ensure accessibility across applications, support knowledge-intensive applications, and interlink numerous disciplines to create a cross-domain knowledge network. Biometrics, home environment, patient health history, and real-time behavior are all required for applications such as senior patient care and monitoring. In addition to a personalized knowledge graph for healthcare, knowledge networks can interconnect multimodal cross-domain data and information collected from several sources. Certain knowledge graphs in this information network are still proprietary, and use by universities or researchers is usually prohibitively expensive.
Market Restraining Factors
Complex programming and standardization
While graph databases, technically, are NoSQL databases, they must run on a single server in practice because they cannot be distributed across a low-cost cluster. This is what causes a network's performance to rapidly deteriorate. Another potential disadvantage is that developers must write their queries in Java because there is no SQL to retrieve data from graph databases, necessitating the hiring of expensive programmers.
Scope of the Study
Market Segments Covered in the Report:
By Type
- Labeled Property Graph
- Resource Description Framework
By Vertical
- BFSI
- Telecom & amp; IT
- Manufacturing & amp; Automotive
- Retail & amp; Ecommerce
- Government & amp; Public Sector
- Healthcare & amp; Life Sciences
- Media & amp; Entertainment
- Energy & amp; Utilities
- Travel & amp; Hospitality
- Transportation & amp; Logistics
- Others
By Component
- Software
- Services
By Deployment Type
- On-premise
- Cloud
By Organization Size
- Large Enterprises
- Small & amp; Medium Enterprises
By Application
- Fraud Detection & amp; Prevention
- Risk, Compliance & amp; Reporting Management
- Supply Chain Management, Operations Management & amp; Asset Management
- Knowledge Management, Content Management, Data Extraction & amp; Search
- Customer Analytics & amp; Recommendation Engines
- Infrastructure Management, IoT, Industry 4.0
- Scientific Data Management, Metadata & amp; Master Data Management
- Others
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Key Market Players
- IBM Corporation
- Oracle Corporation
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- SAP SE
- Teradata Corporation
- Hewlett Packard Enterprise Company
- MarkLogic Corporation
- TigerGraph
- OpenLink Software, Inc.
For more information about this report visit https://www.researchandmarkets.com/r/2ptcae
