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.
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.
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.
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.
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.
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