Graph Database Market

The global graph database market is estimated to be worth USD 2.9 billion in 2023. It is projected to reach USD 7.3 billion by 2028 at a CAGR of 20.2% during the forecast period. Various factors such as the exponential growth of interconnected data in modern applications, such as social networks and recommendation engines, necessitates specialized tools like graph databases to effectively capture and navigate complex relationships  will drive market.

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Graph Database Market Dynamics

Driver:  Need to incorporate real-time big data mining with visualization of the result.

A graph database is designed to handle the relationships between complex and constantly changing data, extract insights in real time, and deliver instantly actionable insights and superior experiences. It enables accurate decision-making processes in real time and ensures that customers can focus on matters they want to prioritize. A graph database facilitates the distribution of any data across data centres or cloud regions, allowing business applications to be always-on, ready to grow, and capable of producing real-time insights and experiences. Furthermore, it aids in the visualisation of insights through data visualisations and delivers real-time forecasts for streaming data. As a result, the graph database is used to improve business processes, decision-making, and achieve the ability to automate, optimise, and guide decisions based on needs in order to meet established corporate goals.

Restraint: Lack of standardization and programming ease

Graph databases come in various flavors and implementations, each with its own query language, data modeling approach, and features. This lack of standardization can create confusion for developers and organizations looking to adopt graph databases. Graph databases allow for flexible data modeling, but this flexibility can also lead to inconsistency in how data is structured. Unlike the standardized schema of relational databases, graph databases allow each node and relationship to have different properties, making it harder to establish a common structure across different graph databases.

Opportunity: Enterprise data unification and rapid proliferation of knowledge graphs

Most businesses face challenges in managing and unifying data. Therefore, to better utilize it, they collect a high volume of data regarding the customers’ activity, demographics, and preferences. This usually results in the creation of vast data silos, which leads to data hoarding when left unscreened. Additionally, several organizations support a breadth of applications – legacy and new, generating distributed and disparate data difficult to manage. Data integration solutions that combine datasets are often not well-suited to changing data requirements. This leads to the inception of ‘Data unification’ deployed by businesses with vast and disparate data generation capabilities.

Knowledge graphs typically work well by unifying data and mapping relationships by drawing connections without moving or copying data, to create a seamless layer atop the existing data infrastructure. These graphs being highly scalable and able to retain each analysis, can act as a reusable asset. While other databases establish connections at the time of querying (by way of expensive JOIN operations), graph databases store the relationships with the data. An enhanced graph database, enriched with business rules enabling inference to be performed upon the connected data, enables streamlined knowledge management across an organization.

Deploying traditional data management approaches does not unearth the dynamic needs required to solve current day and age problems and is time-consuming and too expensive. This is where the application of knowledge graphs for data unification, although currently at a nascent stage, will have profound implications in business strategy excellence in the years ahead.

Challenge: Lack of technical expertise

Graph databases require a different skillset compared to traditional relational databases. Managing and optimizing graph databases involves understanding graph theory, data modeling for relationships, and specialized query languages (such as Cypher or Gremlin). Finding individuals with experience and expertise in these areas can be challenging, especially when the technology is still relatively new. Effectively modeling data in a graph database requires a deep understanding of relationships and how data elements are connected. Without expertise, organizations might struggle to create efficient and intuitive data models, leading to suboptimal performance and usability.

Graph Database Market Ecosystem

The solution segment is expected to account for a larger market size during the forecast period

The offering segment has been further divided into solution and services. The solution segment is expected to account for a larger market size during the forecast period. A graph database solution is a specialized technology designed to efficiently manage and analyze complex relationships within data. Unlike traditional databases that store information in rows and columns, graph databases utilize nodes and edges to represent entities and their connections. The driving factor behind the popularity of graph database solutions is their ability to address scenarios where relationships are as crucial as the data itself.

Hypergraphs to account for higher CAGR  during the forecast period

In the graph database market, a hypergraph is a specialized data model that extends the capabilities of traditional graph databases by allowing multiple types of relationships (hyperedges) to connect any number of nodes. Unlike standard graphs, where edges link only two nodes, hypergraphs enable a more flexible representation of complex, interconnected data. The demand for more sophisticated recommendation systems that consider multi-entity interactions fuels the interest in hypergraphs.

North America to account for largest market size during the forecast period

North America is expected to have the largest market share in the graph database market. Key factors favoring the growth of the graph database market in North America include the increasing technological advancements in the region. Also, the presence of major cloud providers and technology companies in North America contributes to the accessibility and adoption of graph database services, allowing organizations to harness their benefits without significant infrastructure overhead.

Key Market Players

The graph database vendors have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgradations, partnerships and agreements, business expansions, and mergers and acquisitions to strengthen their offerings in the market. The major vendors in the global graph database market include Oracle Corporation (US), IBM Corporation (US), Amazon Web Services, Inc. (US), DataStax (US), Ontotext (Bulgaria), Stardog Union (US), Hewlett Packard Enterprise (US), ArangoDB (US), Blazegraph (US), Microsoft Corporation (US), SAP SE (Germany), Teradata Corporation (US), Openlink Software (US), TIBCO Software, Inc. (US), Neo4j, Inc. (US), GraphBase (Australia), Cambridge Semantics (US), TigerGraph, Inc. (US), Objectivity Inc. (US), Bitnine Co, Ltd. (US), Franz Inc. (US), Redis Labs (US), Graph Story (US), Dgraph Labs (US), Eccenca (Germany), and Fluree (US). The study includes an in-depth competitive analysis of these key players in the graph database market with their company profiles, recent developments, and key market strategies.

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Scope of the Report

This research report categorizes the graph database market based on offering, solution type, deployment mode, analysis type, model type, vertical, and regions.

By Offering:

• Solution
  • Solution Type
  • Deployment mode
• Services
  • Professional Services
  • Managed Services

By Analysis Type:

• Community Analysis
• Connectivity Analysis
• Centrality Analysis
• Path Analysis

By Model Type:

• RDF
• Labeled Propert Graph
• Hypergraphs

By Vertical:

• BFSI
• Retail and eCommerce
• Telecom and IT
• Healthcare, Pharmaceuticals, and Life Sciences
• Government and Public Sector
• Manufacturing and Automotive
• Media and Entertainment
• Energy and Utilities
• Travel and Hospitality
• Transportation and Logistics
• Others

By Region:

• North America
  • US
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • ANZ
  • Rest of Asia Pacific
• Middle East and Africa
  • UAE
  • Saudi Arabia
  • Israel
  • South Africa
  • Rest of Middle East and Africa
• Latin America
  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

Recent Developments:

• In May 2023, AWS and Neo4j Partner and AWS Marketplace Seller which defined the graph database space and open-source standards. Neo4j holds the AWS Data and Analytics Competency.
• In May 2023, SAP and Google Cloud announced a significant expansion of their partnership, including the introduction of a comprehensive open data offering designed to simplify data landscapes and unleash the power of business data. The new offering will bring together SAP and Google Cloud’s data and analytics technology, increasing the openness and value of enterprise data and advancing enterprise AI development.
• In April 2023, Neo4j announced partnership with Imperium Solutions to fulfil growing demand for graph technology in Singapore. Imperium Solutions will ensure customers can gain maximum value from the world’s leading graph database provider, Neo4j, who helps solve complex, enterprise-level problems and efficiently uncovers relationships and patterns in expansive datasets.
• In February 2023, IBM is announced the acquisition of StepZen Inc, which developed a GraphQL server with a unique architecture that helps developers build GraphQL APIs quickly and with less code. StepZen was also designed to be highly flexible. It is compatible with other API approaches and is available as-a-Service (SaaS) while also supporting deployments in private clouds and on-premises data centers.
• In December 2022, LSEG and Microsoft announced a 10-year strategic partnership for next-generation data and analytics solutions, as well as cloud infrastructure solutions; Microsoft will make an equity investment in LSEG through share acquisition. New collaboration with Microsoft Azure, AI, and Microsoft Teams to architect LSEG's data infrastructure and build intuitive next-generation productivity, data and analytics, and modeling solutions.

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