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.

The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PADEP), conducted an evaluation of data used by the PADEP to identify groundwater sources under the direct influence of surface water (GUDI) in Pennsylvania (Gross and others, 2022). The data used in this evaluation and the processes used to compile them from multiple sources are described and provided herein. Data were compiled primarily but not exclusively from PADEP resources, including (1) source-information for public water-supply systems and Microscopic Particulate Analysis (MPA) results for public water-supply system groundwater sources from the agency’s Pennsylvania Drinking Water Information System (PADWIS) database (Pennsylvania Department of Environmental Protection, 2016), and (2) results associated with MPA testing from the PADEP Bureau of Laboratories (BOL) files and water-quality analyses obtained from the PADEP BOL, Sample Information System (Pennsylvania Department of Environmental Protection, written commun., various dates). Information compiled from sources other than the PADEP includes anthropogenic (land cover and PADEP region) and naturogenic (geologic and physiographic, hydrologic, soil characterization, and topographic) spatial data. Quality control (QC) procedures were applied to the PADWIS database to verify spatial coordinates, verify collection type information, exclude sources not designated as wells, and verify or remove values that were either obvious errors or populated as zero rather than as “no data.” The QC process reduced the original PADWIS dataset to 12,147 public water-supply system wells (hereafter referred to as the PADWIS database). An initial subset of the PADWIS database, termed the PADWIS database subset, was created to include 4,018 public water-supply system community wells that have undergone the Surface Water Identification Protocol (SWIP), a protocol used by the PADEP to classify sources as GUDI or non-GUDI (Gross and others, 2022). A second subset of the PADWIS database, termed the MPA database subset, represents MPA results for 631 community and noncommunity wells and includes water-quality data (alkalinity, chloride, Escherichia coli, fecal coliform, nitrate, pH, sodium, specific conductance, sulfate, total coliform, total dissolved solids, total residue, and turbidity) associated with groundwater-quality samples typically collected concurrently with the MPA sample. The PADWIS database and two subsets (PADWIS database subset and MPA database subset) are compiled in a single data table (DR_2022_Table.xlsx), with the two subsets differentiated using attributes that are defined in the associated metadata table (DR_2022_Metadata_Table_Variables.xlsx). This metadata file (DR_2022_Metadata.xml) describes data resources, data compilation, and QC procedures in greater detail.

The National Health Data System (SNDS) will make it possible to link:

• health insurance data (SNIIRAM database);
• hospital data (PMSI database);
• the medical causes of death (base of the CépiDC of Inserm);
• disability-related data (from MDPH - CNSA data);
• a sample of data from complementary health insurance organisations.

The first two categories of data are already available and constitute the first version of the SNDS. The medical causes of death should feed the SNDS from the second half of 2017. The first data from the CNSA will arrive from 2018 and the sample of complementary organizations in 2019.

The purpose of the SNDS is to make these data available in order to promote studies, research or evaluations of a nature in the public interest and contributing to one of the following purposes:

• health information;
• the implementation of health policies;
• knowledge of health expenditure;
• informing professionals and establishments about their activities;
• innovation in the fields of health and medico-social care;
• monitoring, surveillance and health security.

This service represents all sources in the source cadastre of North Rhine-Westphalia, independently managed by five institutions, or their sampling points based on the country’s water stationing map (gsk3c). The attribute table provides information about the number, location and data holders of all objects displayed within a source area and shows the reference source. Sources from Geobasis NRW — i.e. from the state survey — are always reference sources. All objects captured in a radius of 10 m around the reference source are merged under a source NRW_ID. Overlapping radii are combined into a larger contiguous source area. If there is no reference source of Geobasis NRW in an area, the source closest to the area centre of gravity represents the reference source.

The NIMH Repository and Genomics Resource (RGR) stores biosamples, genetic, pedigree and clinical data collected in designated NIMH-funded human subject studies. The RGR database likewise links to other repositories holding data from the same subjects, including dbGAP, GEO and NDAR. The NIMH RGR allows the broader research community to access these data and biospecimens (e.g., lymphoblastoid cell lines, induced pluripotent cell lines, fibroblasts) and further expand the genetic and molecular characterization of patient populations with severe mental illness.

aTwo reports used both CC and SCCS.bAdministrative database: reimbursement database, hospital or institutional records, primary care database (THIN, GRPD).cData collected for the study: self-questionnaire, diary, telephone call, web site, interview, individual health booklet.dPre-existing studies: register, clinical/cohort data.

The Global Alien Species First Record Database represents a compilation of first records of alien species across taxonomic groups and regions.

A first record denotes the year of first observation of an alien species in a region. Note that this often differs from the date of first introduction. The database covers all regions (mostly countries and some islands) globally with particularly intense sampling in Europe, North America and Australasia. First records were gathered from various data sources including online databases, scientific publications, reports and personal collections by a team of >45 researchers. A full list of data sources, an analysis of global and continental trends and more details about the data can be found in our open access publication: Seebens et al. (2017) No saturation in the accumulation of alien species worldwide. Nature Communications 8, 14435.

Note that species names and first records may deviate from the original information, which was necessary to harmonise data files. Original information is provided in the most recent files.

Note that first records are sampled unevenly in space and time and across taxonomic groups, and thus first records are affected by sampling biases. From our experience, analyses on a continental or global scale are rather robust, while analyses on national levels should be interpreted carefully. For national analyses, we strongly recommend to consult the original data sources to check sampling methods, quality etc individually.

The first record database will be irregularly updated and the most recent version is indicated by the version number. _Newer Versions_ are accessible via Zenodo_: https://doi.org/10.5281/zenodo.10039630

Here, we provide several files: (1) The annual number of first records per taxonomic group and continent in an excel file, which represents the aggregated data used for most of the analyses in our paper (Seebens et al. Nat Comm). (2) The R code for the implementation of the invasion model used in the paper. (3) A more detailed data set with the first records of individual species in a region. This data set represents only a subset (~77%) of the full database as some data were not publicly accessible. This data set will be irregularly updated and may differ from the data set used in our paper. All data are free of use for non-commercial purposes with proper citation of Seebens et al. (2017) Nat Comm 8, 14435. (4) A substantially updated version of the First Record Database (vs 1.2) used in our second publication: Seebens et al. (2018) Global rise in emerging alien species results from increased accessibility of new source pools. PNAS 115(10), E2264-E2273.

Please, do not ask the contact person for data, but download it at Zenodo: https://doi.org/10.5281/zenodo.10039630 - Thanks!

List of relationships between nodes.

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.

This data is provided as a one-off project and there are no plans to update it. The data is collected from the 3 main appraisal districts and users may go to them to obtain land records and appraisal data, or contact HPD staff for assistance. This layer contains land use, zoning, and appraisal data for the purposes of long-range planning and scenario modelling, current to October 2016, but based on a variety of sources with different capture dates. The land use information and parcel geography are based on a land use inventory. It also includes estimates of residential units based on building permit, appraisal data, aerials, and a variety of other sources. An ArcGIS lyr file is also provided to allow users to draw this GIS layer in ArcMap.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.

R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions:

Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services?

Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information.

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

An updated and improved version of a global, vertically resolved, monthly mean zonal mean ozone database has been calculated – hereafter referred to as the BSVertOzone database, the BSVertOzone database. Like its predecessor, it combines measurements from several satellite-based instruments and ozone profile measurements from the global ozonesonde network. Monthly mean zonal mean ozone concentrations in mixing ratio and number density are provided in 5 latitude zones, spanning 70 altitude levels (1 to 70km), or 70 pressure 5 levels that are approximately 1km apart (878.4hPa to 0.046hPa). Different data sets or "Tiers" are provided: "Tier 0" is based only on the available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly; the "Tier 0.5" monthly mean zonal means are calculated from a filled version of the Tier 0 database where missing monthly mean zonal mean values are estimated from correlations at level 20 against a total column ozone database and then at levels above and below on correlations with lower and upper levels respectively. The Tier 10 0.5 database includes the full range of measurement variability and is created as an intermediate step for the calculation of the "Tier 1" data where a least squares regression model is used to attribute variability to various known forcing factors for ozone. Regression model fit coefficients are expanded in Fourier series and Legendre polynomials (to account for seasonality and latitudinal structure, respectively). Four different combinations of contributions from selected regression model basis functions result in four different "Tier 1" data set that can be used for comparisons with chemistry-climate model simulations that do not 15 exhibit the same unforced variability as reality (unless they are nudged towards reanalyses). Compared to previous versions of the database, this update includes additional satellite data sources and ozonesonde measurements to extend the database period to 2016. Additional improvements over the previous version of the database include: (i) Adjustments of measurements to account for biases and drifts between different data sources (using a chemistry-transport model simulation as a transfer standard), (ii) a more objective way to determine the optimum number of Fourier and Legendre expansions for the basis 20 function fit coefficients, and (iii) the derivation of methodological and measurement uncertainties on each database value are traced through all data modification steps. Comparisons with the ozone database from SWOOSH (Stratospheric Water and OzOne Satellite Homogenized data set) show excellent agreements in many regions of the globe, and minor differences caused by different bias adjustment procedures for the two databases. However, compared to SWOOSH, BSVertOzone additionally covers the troposphere.

The Jellyfish Database Initiative (JeDI) is a scientifically-coordinated global database dedicated to gelatinous zooplankton (members of the Cnidaria, Ctenophora and Thaliacea) and associated environmental data. The database holds 476,000 quantitative, categorical, presence-absence and presence only records of gelatinous zooplankton spanning the past four centuries (1790-2011) assembled from a variety of published and unpublished sources. Gelatinous zooplankton data are reported to species level, where identified, but taxonomic information on phylum, family and order are reported for all records. Other auxiliary metadata, such as physical, environmental and biometric information relating to the gelatinous zooplankton metadata, are included with each respective entry. JeDI has been developed and designed as an open access research tool for the scientific community to quantitatively define the global baseline of gelatinous zooplankton populations and to describe long-term and large-scale trends in gelatinous zooplankton populations and blooms. It has also been constructed as a future repository of datasets, thus allowing retrospective analyses of the baseline and trends in global gelatinous zooplankton populations to be conducted in the future.

References:

Lucas, C.J., et al. 2014. Gelatinous zooplankton biomass in the global oceans: geographic variation and environmental drivers. Global Ecol. Biogeogr. (DOI: 10.1111/geb.12169)

Condon, R. H., et al. 2013. Recurrent jellyfish blooms are a consequence of global oscillations. PNAS vol. 110(3) 1000-1005. www.pnas.org/cgi/doi/10.1073/pnas.1210920110)

Condon, R. H., et al. 2012.Questioning the Rise of Gelatinous Zooplankton in the World’s Oceans. BioScience vol. 62(2) 160-169. (doi:10.1525/bio.2012.62.2.9)

The WISE 3-Band Cryo Source Working Database (WDB) contains positions and photometry in the 3.4, 4.6 and 12 μm bands for 261,418,479 sources extracted from observations made during the WISE 3-Band Cryo survey phase, 6 August 2010 through 29 September 2010. WISE scanned approximately 30% of the sky during this period when the telescope and focal planes operated at a slightly higher temperature, but were still cooled by solid hydrogen in the inner cryogen tank. CAUTION: The 3-Band Cryo Source WDB is not a well-vetted, reliable list of infrared sources like the WISE All-Sky Release Source Catalog. The WDB contains both detections of real astronomical objects, as well as spurious detections of image artifacts, noise excursions, transient events such as cosmic rays, satellite trails and hot pixels. The WDB also contains redundant extractions of objects that fall in the overlap region between the 3-Band Cryo Atlas Tiles.The WISE 3-Band Cryo Source WDB is best used as a resource to learn more about objects that are found in the All-Sky Release Source Catalog. The 3-Band Cryo observations offer a second, independent epoch of measurement for objects in 30% of the sky, so can be used to test for object motion, flux variability and reliability in the case of very faint sources. 3-Band Cryo WDB entries have been cross-correlated with the All-Sky Catalog and associated Catalog source information is provided in the 3-Band Cryo WDB records.

The data in this dataset was extracted from the Information Wanted online database. It represents a unique data archive based on the numerous advertisements from the Boston-based Pilot newspaper placed by immigrants (and others) looking for lost friends and relatives from 1831 to 1920. The richness of the data includes demographic data on a large number of Irish immigrants gleaned from the ads such as place of origin in Ireland, occupation, port of entrance in the U.S. and in some cases several migrations in the U.S. and around the world. The current scope of the archive, which represents over 41,000 records, reflects interesting and tumultuous times, such as the Great Irish Famine and the United States Civil War. The database includes records primarily from 1831 through 1869. The author of the dataset, Ruth-Ann Harris, was a professor of history who served as head of the Irish Studies Department at Northeastern University before coming to Boston College as an adjunct professor of history in the Irish Studies Program. In 2005, Harris gave her database to Boston College, and the Office of Marketing and Communications developed the database and hosted it on the website, Information Wanted. Distribution represents the date on which the CSV data was downloaded from the web database.

The ROSPSPCTOTAL database table contains a list of sources detected by the Standard Analysis Software System (SASS) in public, unfiltered, pointed PSPC datasets. In addition to the parameters returned by SASS (like position, count rate, likelihood, etc.) each source in the table has associated with it a set of source and sequence "flags." These flags are provided by the ROSAT data centers in the US, Germany and the UK to help the user of the ROSPSPCTOTAL database table quickly judge the reliability of a given source. The ROSPSPCTOTAL database table is a superset of the ROSPSPC database table. The ROSPSPC table excludes sources that meet the following parameter criteria: false_det = 'T' or deferred = 'T' or not_checked = 'T'. See the documentation below for descriptions of these parameters. The catalog consists of all primary source parameters from the automated detection algorithm employed by the SASS. In addition each observation has been quality checked, both by automatic algorithms and by detailed visual inspection. The results of this quality checking are contained as a set of logical-value flags for a set of principal source parameters. If a source parameter is suspect, the associated flag is set to "TRUE"; parameters with no obvious problems maintain the default, "FALSE", value. This database table was last updated in August 2001. More information about the ROSAT Results Archive for PSPC sources can be obtained at the following web pages:

 http://heasarc.gsfc.nasa.gov/docs/rosat/rra/RRA.html http://hea-www.harvard.edu/rosat/rra.html http://www.aip.de/groups/xray/rosat/rra.html http://ledas-www.star.le.ac.uk/rra 

This database table consists of a preliminary source list for the Einstein Observatory's High Resolution Imager (HRI). The source list, obtained from EINLINE, the Einstein On-line Service at the Smithsonian Astrophysical Observatory (SAO), contains basic information about the sources detected with the HRI. This is a service provided by NASA HEASARC .

Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations. A custom missing-data algorithm was used to standardize the United Nations University's World Income Inequality Database and data from other sources; data collected by the Luxembourg Income Study served as the standard. The SWIID provides comparable Gini indices of gross and net income inequality for 192 countries for as many years as possible from 1960 to the present along with estimates of uncertainty in these statistics. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broadly cross-national research on income inequality than previously available sources: it offers coverage double that of the next largest income inequality dataset, and its record of comparability is three to eight times better than those of alternate datasets.