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The Worldwide Graph Database Industry is Expected to Reach $8.1 Billion by 2028
DUBLIN, July 11, 2022 /PRNewswire/ -- 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.
Graph databases have a variety of storing mechanisms. In a graph database, relationships are first-class citizens that can be directed, labeled, and given properties. Several graph databases rely on a SQL engine and use a table to store the graph data. Others store data in a key-value store or a document-oriented database, making them fundamentally NoSQL. However, a table is a logical element, which adds another layer of abstraction among the graph database management system, the graph database, and the physical devices on which the data is stored.
COVID-19 Impact Analysis
The COVID-19 outbreak caused a significant downfall to various economies all over the world. The outbreak of the novel coronavirus slowed down numerous businesses globally. In addition, due to the rapid spread of the infection, governments all over the world were forced to impose countrywide lockdowns.
Due to the travel restrictions under the lockdown, the supply chain of various goods, as well as intermediate goods, was significantly disrupted. Moreover, the lockdown also caused a considerable hindrance to various manufacturing facilities worldwide. In addition, the COVID-19 outbreak exposed flaws in business models throughout various verticals, it also provided various chances for businesses to expand and digitalize beyond geographies as the use and incorporation of technologies like cloud, analytics, AI, IoT, and blockchain surged throughout the lockdown time.
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 egacy 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. Alternatively, developers can use SparcQL or one of the other query languages developed in order to support graph databases, but this is expected to necessitate learning a new skill. As a result, graph database systems suffer from a lack of standardization and programming ease. There are visualization tools for graph databases, although they are still in the early stages of development.
Key Topics Covered:
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market composition and scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Strategies Deployed in Graph Database Market
Chapter 4. Global Graph Database Market by Type
4.1 Global Labeled Property Graph Market by Region
4.2 Global Resource Description Framework Market by Region
Chapter 5. Global Graph Database Market by Vertical
5.1 Global BFSI Market by Region
5.2 Global Telecom & IT Market by Region
5.3 Global Manufacturing & Automotive Market by Region
5.4 Global Retail & Ecommerce Market by Region
5.5 Global Government & Public Sector Market by Region
5.6 Global Healthcare & Life Sciences Market by Region
5.7 Global Media & Entertainment Market by Region
5.8 Global Energy & Utilities Market by Region
5.9 Global Travel & Hospitality Market by Region
5.1 Global Transportation & Logistics Market by Region
5.11 Global Other Vertical Market by Region
Chapter 6. Global Graph Database Market by Component
6.1 Global Software Market by Region
6.2 Global Services Market by Region
Chapter 7. Global Graph Database Market by Deployment Type
7.1 Global On-premise Market by Region
7.2 Global Cloud Market by Region
Chapter 8. Global Graph Database Market by Organization Size
8.1 Global Large Enterprises Market by Region
8.2 Global Small & Medium Enterprises Market by Region
Chapter 9. Global Graph Database Market by Application
9.1 Global Fraud Detection & Prevention Market by Region
9.2 Global Risk, Compliance & Reporting Management Market by Region
9.3 Global Supply Chain Management, Operations Management & Asset Management Market by Region
9.4 Global Knowledge Management, Content Management, Data Extraction & Search Market by Region
9.5 Global Customer Analytics & Recommendation Engines Market by Region
9.6 Global Infrastructure Management, IoT, Industry 4.0 Market by Region
9.7 Global Scientific Data Management, Metadata & Master Data Management Market by Region
9.8 Global Others Market by Region
Chapter 10. Global Graph Database Market by Region
Chapter 11. Company Profiles
11.1 IBM Corporation
11.1.1 Company Overview
11.1.2 Financial Analysis
11.1.3 Regional & Segmental Analysis
11.1.4 Research & Development Expenses
11.1.5 SWOT Analysis
11.2 Amazon Web Services, Inc. (Amazon.com, Inc.)
11.2.1 Company Overview
11.2.2 Financial Analysis
11.2.3 Segmental and Regional Analysis
11.2.4 Recent strategies and developments:
11.2.4.1 Product Launches and Product Expansions:
11.3 Microsoft Corporation
11.3.1 Company Overview
11.3.2 Financial Analysis
11.3.3 Segmental and Regional Analysis
11.3.4 Research & Development Expenses
11.4 Oracle Corporation
11.4.1 Company Overview
11.4.2 Financial Analysis
11.4.3 Segmental and Regional Analysis
11.4.4 Research & Development Expense
11.4.5 Recent strategies and developments:
11.4.5.1 Product Launches and Product Expansions:
11.4.6 SWOT Analysis
11.5 SAP SE
11.5.1 Company Overview
11.5.2 Financial Analysis
11.5.3 Segmental and Regional Analysis
11.5.4 Research & Development Expense
11.5.5 SWOT Analysis
11.6 Teradata Corporation
11.6.1 Company Overview
11.6.2 Financial Analysis
11.6.3 Regional Analysis
11.6.4 Research & Development Expense
11.6.5 Recent strategies and developments:
11.6.5.1 Product Launches and Product Expansions:
11.6.6 SWOT Analysis
11.7 Hewlett Packard Enterprise Company
11.7.1 Company Overview
11.7.2 Financial Analysis
11.7.3 Segmental and Regional Analysis
11.7.4 Research & Development Expense
11.7.5 Recent strategies and developments:
11.7.5.1 Partnerships, Collaborations, and Agreements:
11.7.6 SWOT Analysis
11.8 TigerGraph
11.8.1 Company Overview
11.8.2 Recent strategies and developments:
11.8.2.1 Product Launches and Product Expansions:
11.9 OpenLink Software, Inc.
11.9.1 Company Overview
11.9.2 Recent strategies and developments:
11.9.2.1 Product Launches and Product Expansions:
11.10. MarkLogic Corporation
11.10.1 Company Overview
11.10.2 Recent strategies and developments:
11.10.2.1 Acquisition and Mergers:
For more information about this report visit https://www.researchandmarkets.com/r/4b5q6l
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