As of June 2024, the most popular commercial database management system (DBMS) in the world was Oracle, with a ranking score of 1244. MySQL was the most popular open source DBMS at that time, with a ranking score of 1061.

The popularity of cloud database management systems (DBMSs) are on the rise, growing from *** percent in 2016 to *** percent in 2019, based on the ranking scores of DBSMs. Amazon DynamoDB is was the most popular cloud DBMS at the end of 2019, ranking 16th among all DBMSs.

As of December 2022, relational database management systems (RDBMS) were the most popular type of DBMS, accounting for a 72 percent popularity share. The most popular RDBMS in the world has been reported as Oracle, while MySQL and Microsoft SQL server rounded out the top three.

As of June 2024, almost a ******* percent of the licenses for spatial database management systems (DBMSs) were open-source licenses. Over the years, open source DBMSs have become more and more popular. As of the evaluated period, open source DBMSs have become as popular as commercial ones.

Relational Database Software Market Analysis The global Relational Database Software (RDBMS) market is projected to reach USD 2413 million by 2033, expanding at a CAGR of 9.5% from 2025 to 2033. The growth is driven by factors such as increasing demand for real-time data analytics, growth in cloud computing, and proliferation of IoT devices. Market segments include application (large enterprises, SMEs) and types (cloud-based, on-premises). Notable players include Microsoft, MySQL, Oracle, SAP, and IBM. Key Market Trends The adoption of cloud-based RDBMS is a significant trend, as it offers scalability, flexibility, and cost efficiency. Cloud-based RDBMS enables organizations to access and manage data from anywhere, reducing infrastructure costs and maintenance efforts. Increasing data volumes and the need for real-time data analytics are also driving market growth. Organizations are leveraging RDBMS to handle large datasets, derive insights, and improve decision-making. Additionally, the growing popularity of NoSQL databases for specific use cases presents opportunities for market expansion. Regions such as North America and Europe are expected to maintain a significant market share due to early adoption and technological advancements. Emerging markets in Asia Pacific are also witnessing substantial growth, driven by the increasing demand for data management solutions in various industries.

Dataset

This dataset was created by bin zhang

Contents

The Graph Database Market size was valued at USD 1.9 USD billion in 2023 and is projected to reach USD 7.91 USD billion by 2032, exhibiting a CAGR of 22.6 % during the forecast period. A graph database is one form of NoSQL database that contains and represents relationships as graphs. Graph databases do not presuppose the data as relations as most contemporary relational databases do, applying nodes, edges, and properties instead. The primary types include property graphs that permit attributes on the nodes and edges and RDF triplestores that center on subject-predicate-object triplets. Some of the features include; the method's ability to traverse relationships at high rates, the schema change is easy and the method is scalable. Some of the familiar use cases are social media, recommendations, anomalies or fraud detection, and knowledge graphs where the relationships are complex and require higher comprehension. These databases are considered valuable where the future connection between the items of data is as significant as the data themselves. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Document Databases Market Outlook

The global document databases market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 9.7% over the forecast period. This impressive growth can be attributed to the increasing demand for more flexible and scalable database solutions that can handle diverse data types and structures.

One of the primary growth factors for the document databases market is the rising adoption of NoSQL databases. Traditional relational databases often struggle with the unstructured data generated by modern applications, social media, and IoT devices. NoSQL databases, such as document databases, offer a more flexible and scalable solution to handle this data, which has led to their increased adoption across various industry verticals. Additionally, the growing popularity of microservices architecture in application development also drives the need for document databases, as they provide the necessary agility and performance.

Another significant growth factor is the increasing volume of data generated globally. With the exponential growth of data, organizations require robust and efficient database management systems to store, process, and analyze vast amounts of information. Document databases excel in managing large volumes of semi-structured and unstructured data, making them an ideal choice for enterprises looking to harness the power of big data analytics. Furthermore, advancements in cloud computing have made it easier for organizations to deploy and scale document databases, further driving their adoption.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also propelling the growth of the document databases market. AI and ML applications require databases that can handle complex data structures and provide quick access to large datasets for training and inference purposes. Document databases, with their schema-less design and ability to store diverse data types, are well-suited for these applications. As more organizations incorporate AI and ML into their operations, the demand for document databases is expected to grow significantly.

Regionally, North America holds the largest market share for document databases, driven by the presence of major technology companies and a high adoption rate of advanced database solutions. Europe is also a significant market, with growing investments in digital transformation initiatives. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid technological advancements and increasing adoption of cloud-based solutions in countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing growth, albeit at a slower pace, due to increasing digitalization efforts and the need for efficient data management solutions.

NoSQL Databases Analysis

NoSQL databases, a subset of document databases, have gained significant traction over the past decade. They are designed to handle unstructured and semi-structured data, making them highly versatile and suitable for a wide range of applications. Unlike traditional relational databases, NoSQL databases do not require a predefined schema, allowing for greater flexibility and scalability. This has led to their adoption in industries such as retail, e-commerce, and social media, where the volume and variety of data are constantly changing.

The key advantage of NoSQL databases is their ability to scale horizontally. Traditional relational databases often face challenges when scaling up, as they require more powerful hardware and complex configurations. In contrast, NoSQL databases can easily scale out by adding more servers to the database cluster. This makes them an ideal choice for applications that experience high traffic and require real-time data processing. Companies like Amazon, Facebook, and Google have already adopted NoSQL databases to manage their massive data workloads, setting a precedent for other organizations to follow.

Another driving factor for the adoption of NoSQL databases is their performance in handling large datasets. NoSQL databases are optimized for read and write operations, making them faster and more efficient than traditional relational databases. This is particularly important for applications that require real-time analytics and immediate data access. For instance, e-commerce platforms use NoSQL databases to provide personalized recommendations to users based on th

Distributed Relational Database Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Distributed Relational Database Market Drivers

The market drivers for the Distributed Relational Database Market can be influenced by various factors. These may include:

Growing Data Volume: Organizations require scalable and effective methods to handle and process massive amounts of data due to the exponential growth in data generation. Scalability and enhanced performance are two features that make distributed relational databases a good option for managing large amounts of data.

Cloud Adoption: The market for distributed relational databases has been greatly impacted by the emergence of cloud computing. Cloud platforms are encouraging the usage of distributed databases in cloud environments with their scalable infrastructure and managed database services. Distributed databases are also included by cloud providers into their services, increasing accessibility.

Global Distributed Relational Database Market Restraints

Several factors can act as restraints or challenges for the Distributed Relational Database Market. These may include:

Complexity in Management: Complex configurations and management are frequently associated with distributed relational databases. It can be difficult to ensure data consistency, manage distributed transactions, and deal with node failures; these tasks may call for specific knowledge and resources.

High Initial Costs: Including infrastructure investments and licensing fees, the implementation of distributed relational databases might come with a hefty upfront cost. These upfront expenses may prevent adoption in smaller businesses or those with tighter budgets.

Databases with information on Latin American universities according to the webometrics ranking.

The cloud database and DBaaS market size is projected to be valued at US$ 18,611.2 million in 2023 and is expected to rise to US$ 69,806.5 million by 2033. The sales of cloud databases and DBaaS are anticipated to expand at a significant CAGR of 14.1% during the forecast period. Various factors propelling the demand for Cloud Database and DBaaS market are discussed below.

Scope of the Report

The Relational Database Management System (RDBMS) software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the expanding need for data management across diverse industries. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% through 2033, reaching approximately $90 billion. This growth is fueled by several key factors. Firstly, the proliferation of big data and the need for efficient data storage and retrieval are propelling demand. Secondly, the migration to cloud-based RDBMS solutions offers scalability, cost-effectiveness, and enhanced accessibility, attracting businesses of all sizes. Furthermore, the growing adoption of advanced analytics and business intelligence tools requires robust RDBMS infrastructure, further bolstering market expansion. The market is segmented by deployment (cloud-based and on-premise) and by enterprise size (large, medium, and small). Cloud-based solutions are dominating the market share, reflecting the ongoing digital transformation across various sectors. While large enterprises continue to be the major consumers, the increasing digitalization of small and medium-sized enterprises is significantly expanding the addressable market. Geographic expansion is another notable trend, with North America and Europe currently holding significant market share, while regions like Asia Pacific are witnessing rapid growth due to increasing digital adoption and infrastructure development. However, factors like data security concerns and the high initial investment for on-premise solutions pose challenges to market expansion. Despite these restraints, the long-term outlook for the RDBMS software market remains positive. Continuous innovation in database technologies, including advancements in NoSQL databases and hybrid cloud deployments, will further shape the market landscape. The emergence of new applications for data analytics, such as artificial intelligence and machine learning, will necessitate advanced database capabilities, leading to further investments in RDBMS solutions. The competitive landscape is marked by established players like Microsoft, Oracle, and IBM alongside emerging vendors offering specialized solutions. This competitive environment drives innovation and fosters a wider range of options for businesses to choose from, based on their specific needs and budgets. The market's future will be characterized by increased sophistication in data management, a stronger focus on security and compliance, and a continuous drive towards greater efficiency and scalability.

This API is designed to find the rankings by any geography type within the state with a specific census metric (population or household) and ranking metric (any of the metrics from provider, demographic, technology or speed). Only the top ten and bottom ten rankings would be returned through the API if the result set is greater than 500; otherwise full ranking list be returned.

Real Time Database Market Outlook

The global real-time database market size was valued at USD 8.6 billion in 2023 and is projected to reach USD 23.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 11.6% during the forecast period. The growth of this market is driven by increasing demand for high-speed data processing and real-time analytics across various industry verticals.

One of the primary growth factors for the real-time database market is the rapid digital transformation across industries. Businesses are increasingly adopting real-time databases to handle large volumes of data generated from various sources such as IoT devices, social media, and enterprise applications. The need for real-time data processing and analytics to make immediate, data-driven decisions is pushing organizations to invest heavily in advanced database solutions. Additionally, the rise in the adoption of cloud computing and the growing popularity of Big Data technologies are further propelling the market's growth.

Technological advancements and innovations are also contributing significantly to the growth of the real-time database market. The development of new database technologies, such as in-memory databases, NewSQL, and distributed databases, has enabled organizations to manage and process data more efficiently and effectively. These technologies offer improved performance, scalability, and reliability, which are crucial for handling the increasing data demands of modern businesses. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) capabilities into database systems is enhancing their ability to provide real-time insights and predictive analytics.

The increasing need for real-time fraud detection and prevention is another major factor driving the market. Industries such as BFSI (Banking, Financial Services, and Insurance) and retail are particularly vulnerable to fraudulent activities and cyber threats. Real-time databases can help these industries detect and mitigate fraudulent activities in real-time, thereby reducing financial losses and enhancing security. The growing emphasis on data privacy and regulatory compliance is also encouraging organizations to adopt real-time databases that offer robust security features and enable adherence to data protection regulations.

NEWSQL In Memory Database technologies are gaining traction as a vital component in the real-time database market. These databases combine the robustness of traditional SQL databases with the flexibility and speed of NoSQL solutions, providing a hybrid approach that is particularly beneficial for applications requiring high transaction rates and low latency. In-memory processing allows data to be stored and accessed directly from the main memory, significantly reducing the time required for data retrieval and processing. This is especially advantageous for industries that demand real-time analytics and decision-making capabilities, such as financial services and telecommunications. The ability to handle complex queries efficiently makes NEWSQL In Memory Databases a preferred choice for enterprises looking to optimize their data management strategies and enhance their competitive edge.

From a regional perspective, North America holds the largest market share in the real-time database market, driven by the presence of major technology companies and high adoption rates of advanced database solutions. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid digital transformation in emerging economies such as China and India. The increasing investments in IT infrastructure and the rising number of small and medium enterprises (SMEs) adopting real-time databases are also contributing to the market's growth in this region.

Database Type Analysis

The real-time database market can be segmented by database type into SQL and NoSQL databases. SQL databases, also known as relational databases, have been the traditional choice for structured data storage and management. They are known for their robustness, reliability, and ability to handle complex queries and transactions. SQL databases are widely used across various industries, including BFSI, healthcare, and government, due to their strong support for ACID (Atomicity, Consistency, Isolation, Durability) properties and their ability to enforce data integrity. Despite the emergence of new database technologies, SQL databases

Embedded Database Management Systems Market size was valued at USD 10.8 Billion in 2024 and is projected to reach USD 18.70 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031.

The Embedded Database Management Systems (DBMS) market is driven by the increasing demand for real-time data processing and management across various embedded systems, such as IoT devices, smartphones, automotive systems, and industrial equipment. The rise of connected devices and edge computing has amplified the need for lightweight, efficient, and scalable embedded databases that can operate within resource-constrained environments. Growing adoption of embedded systems in industries like healthcare, automotive, telecommunications, and consumer electronics is also boosting the demand for robust DBMS solutions. Additionally, advancements in AI, machine learning, and data analytics are driving the integration of more sophisticated embedded databases to enable real-time decision-making and enhance device performance.

The global open-source database software market size was valued at USD 34.52 billion in 2025 and is expected to expand at a compound annual growth rate (CAGR) of 18.7% from 2025 to 2033, reaching USD 188.42 billion by 2033. The growing adoption of cloud-based solutions, the increasing need for data management and analytics, and the rising popularity of open-source software are key factors driving the market's growth. The cloud-based segment held the largest market share in 2025 and is expected to continue its dominance during the forecast period. The on-premises segment is expected to witness a steady growth rate due to the need for on-premise data storage and management in various industries. The large enterprise segment is expected to hold a significant market share due to the increasing adoption of open-source database software by large enterprises to manage their vast amounts of data. The small and medium-sized enterprises (SMEs) segment is also expected to grow at a significant rate as SMEs increasingly adopt open-source database software to improve their data management capabilities and reduce costs. Key players in the market include MySQL, Redis, MongoDB, Couchbase, Apache Hive, MariaDB, Neo4j, SQLite, Titan, and others.

The size of the Graph Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 64282.28 million by 2032, with an expected CAGR of 18.20% during the forecast period. A Graph Database is a type of NoSQL database designed to represent and store data in the form of graphs, consisting of nodes, edges, and properties. This database model is optimized for handling data that is highly interconnected, allowing for the representation of relationships and networks with ease. The nodes in a graph database represent entities such as people, places, or events, while the edges represent the relationships or connections between these entities. Properties can be attached to both nodes and edges to store additional information, providing a rich structure for complex data sets. Unlike traditional relational databases, which use tables to organize data in rows and columns, graph databases use graph theory to model the relationships between data points, which enables more efficient querying and analysis, especially for large and complex data structures. This growth is attributed to factors such as increased data complexity, need for real-time insights, and advancements in AI and ML. Graph databases provide efficient storage and analysis of highly interconnected data, making them valuable for fraud detection, social network analysis, and recommendation systems. Key players include Oracle Corporation, IBM Corporation, and Amazon Web Services, Inc. Recent developments include: June 2021: Neo4j has released its most recent graph database version, 4.3. Graph data analysis, relationship asset indexes, new smart 10 scheduling, and parallelized backup are some of the features included in the most recent version of the graph database., April 2021: The MarkLogic Data Hub Central low-code/no-code user interface was introduced by MarkLogic Corp. With the ease and agility of using the data infrastructure, MarkLogic's launch provides organizations with a clear roadmap for cloud modernization., October 2020: Microsoft Corporation unveiled a brand-new artificial intelligence platform that can caption and describe photos. Azure Cognitive Services offers the system..

The size of the Relational Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 45481.69 million by 2032, with an expected CAGR of 12.50% during the forecast period. This growth trajectory is primarily driven by the advent of hybrid seeds, which offer superior yield and improved disease resistance. Government initiatives aimed at promoting food security and the adoption of advanced technologies further fuel market expansion. Key applications for hybrid seeds encompass field crops, horticulture, and fodder crops. Leading players in the market include Monsanto, DuPont Pioneer, Syngenta, and Bayer CropScience. Recent developments include: October 2022: Oracle released latest advancements in database technology with the announcement of Oracle Database 23c Beta. It accommodates diverse data types, workloads, and development styles. The release incorporates numerous innovations across Oracle's database services and product portfolio., October 2023: Microsoft has launched a public preview of a new Azure SQL Database free offering, marking a significant addition to its cloud services. Users can access a 32 GB general purpose, serverless Azure SQL database with 100,000 vCore seconds of compute free monthly..

Introduction

This is dataset of the 10,000 most popular movies across the world, irrespective of language and recency. These have been extracted using TMDb API.

About the Dataset

What is TMDB's API? The closed-source API service is for those people interested in using their movies, TV shows or actor images and/or data in their application. TMDb's API is a system that they provide for developers and their team to programmatically fetch and use TMDb's data and/or images. Their API is free to use as long as you attribute TMDb as the source of the data and/or images. Also, they update their API from time to time.

This dataset lists 10,000 most popular movies across the globe. Information held inside the dataset - A. Dataset 1 : Movies dataset - 1. title - Title of the Movie in English. 2. overview - A small summary of the plot. 3. original_lang - Original language it was shot in. 4. rel_date - Date of release. 5. popularity - Popularity. 6. vote_count - Votes received. 7. vote_average - Average of all votes received.

B. Dataset 2 : Genres dataset 1. id 2. Movie ID 3. Genre

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.