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

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

The databases resulting from articles that created the database and released it are not mentioned here because they will be mentioned later. All Latin America means Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Suriname, Uruguay, Venezuela.

The Open Database of Buildings (ODB) is a collection of open data on buildings made available under the Open Government License - Canada. The ODB brings together 530 datasets originating from 107 government sources of open data. The database aims to enhance access to a harmonized collection of building features across Canada.

The global academic research database market is booming, projected to hit $388.2 million in 2025, with a robust CAGR driving growth. This in-depth analysis explores market size, key players (Scopus, Web of Science, PubMed), and future trends shaping this vital sector for researchers and educators.

Check Market Research Intellect's Open Source Database Market Report, pegged at USD 15 billion in 2024 and projected to reach USD 30 billion by 2033, advancing with a CAGR of 8.8% (2026-2033).Explore factors such as rising applications, technological shifts, and industry leaders.

The Open Source Database Solution market is poised for significant expansion, projected to reach an estimated value of $16 billion by 2025, with a robust Compound Annual Growth Rate (CAGR) of 13%. This impressive trajectory is largely fueled by the increasing adoption of cloud-native architectures and the growing demand for cost-effective, flexible, and scalable data management solutions across enterprises of all sizes. Small and Medium-sized Enterprises (SMEs) are increasingly embracing open-source databases to democratize access to advanced data capabilities, while large enterprises are leveraging them for enhanced agility and to avoid vendor lock-in, particularly within hybrid and private cloud environments. The inherent benefits of open-source, such as community support, transparency, and a wealth of customization options, are compelling factors driving this market's upward momentum. Key applications span a wide spectrum, from operational data management to analytical workloads, underscoring the versatility and adaptability of these solutions. The market's growth is further propelled by the continuous innovation within the open-source database ecosystem, with regular updates and the introduction of new features addressing evolving industry needs. Companies like AWS, Google, and Microsoft are actively contributing to and integrating open-source database technologies into their cloud offerings, further solidifying their position and accessibility. However, challenges such as the need for specialized expertise for deployment and maintenance, and concerns around security and data governance in highly regulated industries, present potential restraints. Despite these hurdles, the overarching trend towards data-driven decision-making and the inherent advantages of open-source solutions are expected to outweigh these limitations, ensuring sustained and dynamic market growth throughout the forecast period of 2025-2033. This report provides an in-depth analysis of the Open Source Database Solution Market, projecting a substantial compound annual growth rate (CAGR) within the multi-million dollar valuation range. The study encompasses a comprehensive Study Period of 2019-2033, with the Base Year and Estimated Year set at 2025, and a Forecast Period from 2025-2033, building upon Historical Period data from 2019-2024. This detailed examination will equip stakeholders with actionable insights to navigate this dynamic and rapidly evolving market.

The size of the Open Source Time Series Database market was valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2032, with an expected CAGR of XX% during the forecast period.

Open-Source Database Software Market size was valued at USD 11250.75 million in 2024 and the revenue is expected to grow at a CAGR of 10.75% from 2025 to 2032

Although ** percent of the respondents reported no use of database as a service (DBaaS) in their organizations in 2020, the use of this cloud database technology appears to have increased from 2019 and is expected to increase significantly in the coming years, as the need for streamlining and commoditizing services grows.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

This database dump includes all published structured data organized used by Open Context (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.

IMPORTANT

This database dump contains data from roughly 190+ different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

List of Academic Databases Freely Open by the Academia Sinica

The Carbon Storage Open Database is a collection of spatial data obtained from publicly available sources published by several NATCARB Partnerships and other organizations. The carbon storage open database was collected from open-source data on ArcREST servers and websites in 2018, 2019, 2021, and 2022. The original database was published on the former GeoCube, which is now EDX Spatial, in July 2020, and has since been updated with additional data resources from the Energy Data eXchange (EDX) and external public data resources. The shapefile geodatabase is available in total, and has also been split up into multiple databases based on the maps produced for EDX spatial. These are topical map categories that describe the type of data, and sometimes the region for which the data relates. The data is separated in case there is only a specific area or data type that is of interest for download. In addition to the geodatabases, this submission contains: 1. A ReadMe file describing the processing steps completed to collect and curate the data. 2. A data catalog of all feature layers within the database. Additional published resources are available that describe the work done to produce the geodatabase: Morkner, P., Bauer, J., Creason,more » C., Sabbatino, M., Wingo, P., Greenburg, R., Walker, S., Yeates, D., Rose, K. 2022. Distilling Data to Drive Carbon Storage Insights. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104945 Morkner, P., Bauer, J., Shay, J., Sabbatino, M., and Rose, K. An Updated Carbon Storage Open Database - Geospatial Data Aggregation to Support Scaling -Up Carbon Capture and Storage. United States: N. p., 2022. Web. https://www.osti.gov/biblio/1890730 Morkner, P., Rose, K., Bauer, J., Rowan, C., Barkhurst, A., Baker, D.V., Sabbatino, M., Bean, A., Creason, C.G., Wingo, P., and Greenburg, R. Tools for Data Collection, Curation, and Discovery to Support Carbon Sequestration Insights. United States: N. p., 2020. Web. https://www.osti.gov/biblio/1777195 Disclaimer: This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.« less

The size of the Open-Source Database Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py

The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM).

In 2023, the Open Context team finished migration of from a legacy database schema to a revised and refactored database schema with stricter referential integrity and better consistency across tables. During this process, the Open Context team de-duplicated records, cleaned some metadata, and redacted attribute data left over from records that had been incompletely deleted in the legacy schema.

This database dump includes all Open Context data organized with the legacy schema (table names that start with the 'oc_' or 'link_' prefixes) along with all Open Context data after cleanup and migration to the new database schema (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories.

These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included.

IMPORTANT

This database dump contains data from roughly 180 different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

The global academic research databases market is booming, projected to reach $259.3 million in 2025, with a CAGR of 5.9% through 2033. Discover key drivers, trends, and regional insights from this comprehensive market analysis covering Scopus, Web of Science, and more. Explore market segmentation by access type and user application.

Variability in mean payment per physician, number of physicians, and aggregated payments for transactions in the Open Payments database, 2014–2018, for each top-category specialty available for allopathic and osteopathic physicians.

This database has been developed in the context of the Horizon Europe project TRANSIENCE to support the development of MIC3, a consistent, fully open-source model ecosystem to assess industrial circularity, decarbonisation, and sustainability. The database contains P&S characteristics, specifications, material compositions, and supply chain-related information. It assesses material compositions (bulk materials) of various building types, passenger vehicles, batteries, wind turbines, solar PV systems, and electronic devices, using the data from the prospective life cycle assessment framework premise building on ecoinvent 3.10 (system model: 'allocation, cutoff by classification') and literature. Additional data on critical materials for a large set of low-carbon energy technologies has also been included. Moreover, important translations of socioeconomic indicators into physical demand are provided for selected products, end-uses, and energy services.

The size of the Open Database Cloud Platform Solution market was valued at USD 9777 million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.