As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of *******. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

The Databases_DBMS_2024 dataset provides information about leading databases with a worldwide footprint.

The dataset contains records of 417 databases and has information about the DBMS type, multi-model capability, vendor, and vendor country.

The dataset also contains data on DBMS score and rankings, from DB-engines.com.

Kagglers can utilise the dataset to explore the

• Composition of DBMS Types and Multi-model capability
• Distribution of DBMS vendors and Vendor countries, etc.
• Trends and patterns in DBMS rankings and scores

The statistic displays the most popular SQL databases used by software developers worldwide, as of **********. According to the survey, ** percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

List of Top Schools of Distributed Databases sorted by citations.

A guide to choosing the most suitable database types for data analytics across different industries, including examples of common databases.

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2024 and single recent year data pertain to citations received during calendar year 2024. 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. This version (7) is based on the August 1, 2025 snapshot from Scopus, updated to end of citation year 2024. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2025. If an author is not on the list, it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, see attached file on FREQUENTLY ASKED QUESTIONS. Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

ObjectiveTo conduct a rapid review and determine the acceptability, access, and uptake of the COVID-19 vaccine among global migrants.Materials and methodsA rapid review was conducted May 2022 capturing data collected from April 2020 to May 2022. Eight databases were searched: PubMed, Ovid Medline, EMBase, CINAHL, SCOPUS, Google Scholar, LILACS, and the Web of Science. The keywords “migrants” AND COVID-19” AND “vaccine” were matched with terms in MeSH. Peer-reviewed articles in English, French, Portuguese, or French were included if they focused on COVID-19 immunization acceptability, access, or uptake among global migrants. Two independent reviewers selected and extracted data. Extracted data was synthesized in a table of key characteristics and summarized using descriptive statistics.ResultsThe search returned 1,186 articles. Ten articles met inclusion criteria. All authors reported data on the acceptability of the COVID-19 vaccine, two on access, and one on uptake. Eight articles used quantitative designs and two studies were qualitative. Overall, global migrants had low acceptability and uptake, and faced challenges in accessing the COVID-19 vaccine, including technological issues.ConclusionsThis rapid review provides a global overview of the access, acceptability, and uptake of the COVID-19 vaccine among global migrants. Recommendations for practice, policy, and future research to increase access, acceptability, and uptake of vaccinations are discussed.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Other Collections. The data include parameters of reconstructions (air temperature) with a geographic location of Global. The time period coverage is from 1949 to -50 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

A comprehensive list of databases can be found in Pathguide (http://www.pathguide.org). A, automated curation; B, both manual and automated curation; BIND, Biomolecular Interaction Network Database; BioPP, Biological Pathway Publisher; DIP, Database of Interacting Proteins; EcoCyc, Encyclopaedia of E. coli Genes and Metabolism; GNPV, Genome Network Platform Viewer; HPRD, Human Protein Reference Database; KEGG, Kyoto Encyclopedia of Genes and Genomes; M, manual curation; MetaCyc, a Metabolic Pathway database; MINT, Molecular Interation Database; MIPS, Munich Information Center for Protein Sequences; N, No; OPHID, Online Predicted Human Interaction Database; PANTHER, Protein Analysis through Evolutionary Relationship Database; PID, The Pathway Interaction Database; STKE, Signal Transduction Knowledge Environment, UNIHI, Unified Human Interactome; Y, yes. (61 KB DOC)

MariaDB Galera Cluster is the most commonly used database for OpenStack components worldwide, according to the OpenStack User Survey in 2023. As of that time, ** percent of respondents reported the use of MariaDB Galera Cluster for the OpenStack components in their organizations

NoSQL Databases Software Market Outlook

The global NoSQL Databases Software market size was valued at approximately $6.5 billion in 2023 and is projected to reach around $23.1 billion by 2032, growing at a robust CAGR of 15.3% during the forecast period. The growth of this market is driven by an increasing volume of unstructured data, which traditional relational databases struggle to manage efficiently. Cloud adoption, big data analytics, and digital transformation initiatives across industries are significant factors propelling the demand for NoSQL databases.

One of the primary growth drivers for the NoSQL Databases Software market is the exponential increase in data generation from various sources such as social media, IoT devices, and digital content platforms. Traditional relational databases are often inadequate for handling this deluge of unstructured data, prompting organizations to shift towards NoSQL databases that offer flexibility and scalability. Additionally, industries such as retail, healthcare, and finance, which rely heavily on data-driven decision-making, are increasingly adopting NoSQL solutions to manage their vast and varied data sets efficiently.

Cloud computing has also played a crucial role in the growth of the NoSQL Databases Software market. The scalability and cost-effectiveness of cloud-based NoSQL databases make them an attractive option for both large enterprises and SMEs. By leveraging cloud infrastructure, businesses can deploy, manage, and scale their database operations without the need for significant upfront investments in hardware. This ease of deployment and maintenance, coupled with the robust performance of cloud-based NoSQL solutions, has significantly contributed to market expansion.

Moreover, the growing trend of digital transformation across various sectors is another significant driver for the NoSQL Databases Software market. Companies are increasingly adopting digital tools and technologies to streamline operations, enhance customer experiences, and gain competitive advantages. NoSQL databases, with their ability to handle diverse data types and provide real-time analytics, are pivotal in supporting these digital transformation efforts. As more organizations embark on digital transformation journeys, the demand for NoSQL databases is poised to rise.

Regionally, North America holds the largest share of the NoSQL Databases Software market, driven by the early adoption of advanced technologies and the presence of major industry players. The Asia Pacific region, however, is expected to witness the highest growth rate, owing to rapid digitalization, increasing internet penetration, and the growing adoption of cloud computing across emerging economies. Europe also presents significant opportunities, with many organizations in the region focusing on data-driven strategies to enhance operational efficiency and customer engagement.

In the realm of database management, Columnar Databases Software has emerged as a crucial technology, particularly for analytical workloads. Unlike traditional row-based databases, columnar databases store data in columns, which allows for more efficient data retrieval and processing. This structure is particularly advantageous for performing aggregate functions and reading large volumes of data quickly, making it an ideal choice for business intelligence and data warehousing applications. The ability to handle high-throughput read operations with minimal latency is a key advantage, enabling organizations to perform complex queries and gain insights from their data with greater speed and accuracy.

Type Analysis

The NoSQL Databases Software market is categorized into several types, including Document-Oriented, Key-Value, Column-Oriented, and Graph-Based databases. Document-Oriented databases are designed to store, retrieve, and manage document-oriented information, making them highly suitable for content management systems, blogging platforms, and event logging. These databases use a flexible schema, allowing for the storage of different data types within a single document. This flexibility makes document-oriented databases a popular choice for applications requiring dynamic and evolving data structures.

Key-Value databases are another critical segment within the NoSQL lands

List of Top Schools of Foundations and Trends in Databases sorted by citations.

Selection of databases commonly used in our workflows.

A large-scale gene expression database capturing cellular responses to thousands of perturbations.

Descriptive statistics for the total sample as well as for the different samples separately.

Means and standard deviations for the self-report scales by category.

NewSQL Database Market Outlook

According to our latest research, the global NewSQL Database market size reached USD 2.3 billion in 2024, driven by the rapidly evolving demands for high-performance, scalable, and ACID-compliant database solutions across diverse industries. The market is expected to grow at a robust CAGR of 24.6% from 2025 to 2033, propelling the market value to approximately USD 17.5 billion by 2033. This impressive growth is fueled by the increasing need for real-time analytics, digital transformation initiatives, and the proliferation of cloud-native applications, as enterprises seek to overcome the limitations of traditional relational and NoSQL databases while maintaining transactional integrity and scalability.

One of the primary growth factors for the NewSQL Database market is the surge in digital transformation projects and the exponential growth of data volumes across sectors such as BFSI, healthcare, retail, and telecommunications. Organizations are increasingly seeking database solutions that can handle high-throughput workloads, provide strong consistency, and support mission-critical applications. NewSQL databases, with their unique blend of scalability, performance, and ACID compliance, are ideally positioned to address these requirements. The rise of IoT, big data analytics, and real-time transaction processing in industries has further accelerated the adoption of NewSQL databases, as traditional RDBMS and NoSQL solutions often struggle to meet the demands for speed, reliability, and data consistency in modern enterprise environments.

Another significant driver of market growth is the widespread adoption of cloud computing and the increasing popularity of hybrid and multi-cloud strategies. Enterprises are leveraging cloud-based NewSQL solutions to achieve greater flexibility, cost efficiency, and operational agility. Cloud deployment models enable organizations to scale their database infrastructure dynamically in response to fluctuating workloads, while hybrid and multi-cloud configurations provide enhanced resilience and data sovereignty. The seamless integration capabilities of NewSQL databases with popular cloud platforms, combined with their ability to deliver real-time analytics and transactional consistency, are making them the preferred choice for forward-thinking organizations looking to modernize their data architecture.

Furthermore, the growing focus on customer experience, regulatory compliance, and competitive differentiation is driving investments in advanced database technologies. Industries such as BFSI and healthcare require robust data management solutions to ensure security, privacy, and compliance with stringent regulations. NewSQL databases offer advanced security features, strong encryption, and fine-grained access controls, which are essential for protecting sensitive data and meeting regulatory mandates. The ability of NewSQL platforms to support complex queries, automate scaling, and deliver consistent performance under heavy loads is also contributing to their rising adoption in sectors where data integrity and uptime are mission-critical.

From a regional perspective, North America currently dominates the NewSQL Database market due to the presence of leading technology providers, early adoption of advanced IT solutions, and significant investments in cloud infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid digitalization, expanding e-commerce, and government initiatives promoting smart infrastructure. Europe is also emerging as a key market, with enterprises increasingly prioritizing data sovereignty and compliance with GDPR regulations. The competitive landscape is intensifying as both established vendors and innovative startups introduce cutting-edge NewSQL solutions tailored to industry-specific needs and regional regulatory requirements.

In addition to the cloud and hybrid strategies, the integration of Spatial Database technology is becoming increasingly relevant in the NewSQL landscape. Spatial Databases are designed to store and query data related to objects in space, making them invaluable for industries that rely on geospatial data, such as logistics, urban planning, and environmental monitoring. The ability to handle complex spatial queries and integrate seamlessly

Top 10 Programming Lang, IDE & Database (2004-2021)

These data are gathered from PYPL, TOPDB & Top IDE index, which can be accessed on pypl.github.io. The more a language, IDE or database is searched, the more popular the language is assumed to be. It is a leading indicator. The raw data comes originally from Google Trends. If you believe in collective wisdom, the PYPL Popularity of Programming Language index, TOPDB index & Top IDE index can help you decide which language to study, or which one to use in a new software project.

Data Structure & Visualization

Here I attach the data in 3 formats: Excel (.xlsx), CSV (.csv) & JSON (.json). The data shows how many shares & popularity of the programming language, database & IDE based on Google Trends. Below are example of line chart race that I build using these data.

Top 10 Programming Language Based on PYPL Index (2004-2021)

Top 10 IDE Based on TOP IDE Index (2004-2021)

Top 10 Databases Based on TOPDB Index (2004-2021)

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The majority of respondents stated that their company used more than one database for their operations. This indicates the complexity of maintaining security of IT infrastructure at organizations. Microsoft Azure database (** percent) and Microsoft SQL Server (** percent) were the most commonly used databases among respondents.