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Global Graph Database Market to reach USD 6.31 Billion by 2027.

Global Graph Database Market is valued approximately at USD 1.55 billion in 2020 and is anticipated to grow with a healthy growth rate of more than 22.2% over the forecast period 2021-2027. The global Graph Database market is being driven by rising requirement of incorporating the real-time big data mining along with visualization of result, also the rising demand for the solutions which can process with low-latency queries and adoption of AI-based graph database tools and services. Furthermore, enterprise data unification and rapid proliferation of knowledge graphs and semantic knowledge graphs for addressing complex scientific research will provide new opportunities for the global Graph Database industry. For instance, as per Statista, The big data and business analytics (BDA) across the world was valued at around 168.8 billion U.S. dollars in 2018 and reached USD 189.1 billion in year 2019 and it is anticipated to grow to USD 215.7 billion by year 2021. In 2021, more than half of BDA spending will drive towards services in which IT services is expected to make up about USD 85 billion and business services will account for the remainder. As a result, increased use of big data analytics in services will serve as a catalyst for the Graph Database industry in the future. However, lack of standardization and programming ease may impede market growth over the forecast period of 2021-2027.Asia Pacific, North America, Europe, Latin America, and Rest of the World are the key region considered for the regional analysis of global graph database market. The growing number of graph database players across regions makes North America the leading region across the world in terms of market share. Whereas North America is also anticipated to exhibit the highest growth rate over the forecast period 2021-2027, due to rising technological advancement in the region. Major market player included in this report are: IBM Oracle Microsoft AWS SAP Neo4j Marklogic Tigergraph Stardog Datastax The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:By Component: Software Services By Type: Resource description framework Labelled property graph By Deployment: Cloud On premisesBy Region: North America U.S. Canada Europe UK Germany France Spain Italy ROEAsia Pacific China India Japan Australia South Korea RoAPAC Latin America Brazil Mexico Rest of the WorldFurthermore, years considered for the study are as follows:Historical year – 2018, 2019 Base year – 2020 Forecast period – 2021 to 2027.Target Audience of the Global Graph Database Market in Market Study:Key Consulting Companies & Advisors Large, medium-sized, and small enterprises Venture capitalists Value-Added Resellers (VARs) Third-party knowledge providers Investment bankers Investors

Product Code: ICTEITS-62137339
Publish Date: 30-08-2021
Page: 200

Global Graph Database Market is valued approximately at USD 1.55 billion in 2020 and is anticipated to grow with a healthy growth rate of more than 22.2% over the forecast period 2021-2027. The global Graph Database market is being driven by rising requirement of incorporating the real-time big data mining along with visualization of result, also the rising demand for the solutions which can process with low-latency queries and adoption of AI-based graph database tools and services. Furthermore, enterprise data unification and rapid proliferation of knowledge graphs and semantic knowledge graphs for addressing complex scientific research will provide new opportunities for the global Graph Database industry. For instance, as per Statista, The big data and business analytics (BDA) across the world was valued at around 168.8 billion U.S. dollars in 2018 and reached USD 189.1 billion in year 2019 and it is anticipated to grow to USD 215.7 billion by year 2021. In 2021, more than half of BDA spending will drive towards services in which IT services is expected to make up about USD 85 billion and business services will account for the remainder. As a result, increased use of big data analytics in services will serve as a catalyst for the Graph Database industry in the future. However, lack of standardization and programming ease may impede market growth over the forecast period of 2021-2027.

Asia Pacific, North America, Europe, Latin America, and Rest of the World are the key region considered for the regional analysis of global graph database market. The growing number of graph database players across regions makes North America the leading region across the world in terms of market share. Whereas North America is also anticipated to exhibit the highest growth rate over the forecast period 2021-2027, due to rising technological advancement in the region.
Major market player included in this report are:

IBM
Oracle
Microsoft
AWS
SAP
Neo4j
Marklogic
Tigergraph
Stardog
Datastax
The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:
Software
Services
By Type:
Resource description framework
Labelled property graph
By Deployment:
Cloud
On premises

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027.

Target Audience of the Global Graph Database Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Graph Database Market , by Region, 2019-2027 (USD Billion)
1.2.2. Graph Database Market , by Component, 2019-2027 (USD Billion)
1.2.3. Graph Database Market , by Type, 2019-2027 (USD Billion)
1.2.4. Graph Database Market , by Deployment, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Graph Database Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Graph Database Market Dynamics
3.1. Graph Database Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Need to incorporate real-time big data mining with visualization of result
3.1.1.2. Growing demand for solutions that can process low-latency queries
3.1.1.3. Adoption of AI-based graph database tools and services to drive the market
3.1.2. Market Restraint
3.1.2.1. Lack of standardization and programming ease
3.1.3. Market Opportunities
3.1.3.1. Enterprise data unification and rapid proliferation of knowledge graphs
3.1.3.2. Semantic knowledge graphs for addressing complex scientific research
Chapter 4. Global Graph Database Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
Chapter 5. Global Graph Database Market , by Component
5.1. Market Snapshot
5.2. Global Graph Database Market by Component, Performance – Potential Analysis
5.3. Global Graph Database Market Estimates & Forecasts by Component 2018-2027 (USD Billion)
5.4. Graph Database Market , Sub Segment Analysis
5.4.1. Software
5.4.2. Services
Chapter 6. Global Graph Database Market , by Type
a. Market Snapshot
6.1. Global Graph Database Market by Type, Performance – Potential Analysis
6.2. Global Graph Database Market Estimates & Forecasts by Type2018-2027 (USD Billion)
6.3. Graph Database Market , Sub Segment Analysis
6.3.1. Resource description framework
6.3.2. labelled property graph
Chapter 7. Global Graph Database Market , by Deployment
b. Market Snapshot
7.1. Global Graph Database Market by Deployment, Performance – Potential Analysis
7.2. Global Graph Database Market Estimates & Forecasts by Deployment 2018-2027 (USD Billion)
7.3. Graph Database Market , Sub Segment Analysis
7.3.1. Cloud
7.3.2. On premises
Chapter 8. Global Graph Database Market , Regional Analysis
8.1. Graph Database Market , Regional Market Snapshot
8.2. North America Graph Database Market
8.2.1. U.S. Graph Database Market
8.2.1.1. Component breakdown estimates & forecasts, 2018-2027
8.2.1.2. Type breakdown estimates & forecasts, 2018-2027
8.2.1.3. Deployment breakdown estimates & forecasts, 2018-2027
8.2.2. Canada Graph Database Market
8.3. Europe Graph Database Market Snapshot
8.3.1. U.K. Graph Database Market
8.3.2. Germany Graph Database Market
8.3.3. France Graph Database Market
8.3.4. Spain Graph Database Market
8.3.5. Italy Graph Database Market
8.3.6. Rest of Europe Graph Database Market
8.4. Asia-Pacific Graph Database Market Snapshot
8.4.1. China Graph Database Market
8.4.2. India Graph Database Market
8.4.3. Japan Graph Database Market
8.4.4. Australia Graph Database Market
8.4.5. South Korea Graph Database Market
8.4.6. Rest of Asia Pacific Graph Database Market
8.5. Latin America Graph Database Market Snapshot
8.5.1. Brazil Graph Database Market
8.5.2. Mexico Graph Database Market
8.6. Rest of The World Graph Database Market
Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. IBM
9.2.1.1. Key Information
9.2.1.2. Overview
9.2.1.3. Financial (Subject to Data Availability)
9.2.1.4. Product Summary
9.2.1.5. Recent Developments
9.2.2. Oracle
9.2.3. Microsoft
9.2.4. AWS
9.2.5. SAP
9.2.6. Neo4j
9.2.7. Marklogic
9.2.8. Tigergraph
9.2.9. Stardog
9.2.10. Datastax

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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