The Relational Database Software Market size was estimated at USD 21.97 Billion in 2024 and is projected to reach USD 45.23 Billion by 2031, growing at a CAGR of 9.4 % from 2024 to 2031

Global Relational Database Software Market Drivers

Rising Demand for Efficient Data Management: Organizations across industries are generating and collecting ever-increasing volumes of data. This necessitates efficient and secure data management solutions. Relational databases, with their structured format and robust querying capabilities, offer a valuable tool to organize, manage, and analyze this data, leading to increased demand for this software.

Cloud Adoption and Scalability: The proliferation of cloud computing has significantly impacted the relational database market. Cloud-based database solutions offer scalability, flexibility, and reduced IT infrastructure burden for businesses. This makes them particularly attractive for small and medium-sized enterprises (SMEs) and facilitates easier data access for geographically dispersed teams.

Growing Importance of Data Security and Compliance: Data breaches and cyberattacks pose significant threats to businesses. Relational database software vendors are constantly innovating to enhance security features like encryption and access controls. Additionally, stringent data privacy regulations like GDPR and CCPA are driving the need for compliant data storage and management solutions, further propelling the market for secure relational databases.

The Relational Database Management System (RDBMS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the expanding need for data analytics in various sectors, and the ongoing digital transformation across industries. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% through 2033. This growth is fueled by several key factors. Firstly, the proliferation of data generated by smart government initiatives, enhanced information security needs, and the rapid advancements in industrial digitalization are creating an insatiable demand for robust and scalable RDBMS solutions. Secondly, the shift towards cloud-based RDBMS offerings from traditional on-premise deployments is significantly impacting the market dynamics, offering increased flexibility, scalability, and cost-effectiveness. Finally, the increasing sophistication of analytics tools and techniques further underscores the demand for efficient and reliable RDBMS technologies capable of handling massive volumes of data for insightful analysis. Major players like Oracle, IBM, and Amazon are heavily invested in innovation within this sector, continually enhancing their offerings to cater to evolving market needs. The RDBMS market is segmented by type (OLTP and OLAP) and application (Smart Government Affairs, Information Security, Industry Digitalization, Digital Industrialization, and Others). While all segments contribute to the overall growth, the rapid advancement in areas like smart city initiatives, cybersecurity measures, and the industrial internet of things (IIoT) are significantly boosting the demand for RDBMS within Smart Government Affairs, Information Security, and Industry Digitalization segments. Geographic analysis reveals strong growth across North America and Europe, driven by high technological adoption rates and a mature IT infrastructure. However, the Asia-Pacific region is expected to witness the most significant growth in the coming years, fueled by rapid economic development and increasing digitalization efforts across several emerging economies. Despite the overall positive growth outlook, some restraints like data security concerns and the rising complexity of managing large-scale databases might pose challenges to market expansion. However, ongoing technological advancements and increasing investment in database security are expected to mitigate these concerns.

The Cloud Database and DBaaS Market Report is Segmented by Component (Solution, Services), by Type (NoSQL, Relational Database), by Deployment (Public, Private, Hybrid), by Enterprise Size (SMEs, Large Enterprises), by End-User (BFSI, IT and Telecom, Retail, Healthcare, Government, Other End-Users), by Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

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..

This paper introduces the African Relational Pro-Government Militia Dataset (RPGMD). Recent research has improved our understandings of how pro-government forces form, under what conditions they are most likely to act, and how they affect the risk of internal conflict, repression, and state fragility. In this paper, we give an overview of our dataset that identifies African pro-government militias (PGMs) from 1997 to 2014. The dataset shows the wide proliferation and diffusion of these groups on the African continent. We identify 149 active PGMs, 104 of which are unique to our dataset. In addition to descriptive information about these PGMs, we contribute measures of PGM alliance relationships, ethnic relationships, and context. We use these variables to examine the determinants of the presence and level of abusive behavior perpetrated by individual PGMs. Results highlight the need to consider nuances in PGM-government relationships in addition to PGM characteristics.

The Online Transaction Processing (OLTP) market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding digital economy, and the imperative for real-time data processing across diverse sectors. The market's expansion is fueled by the need for high-performance databases capable of handling massive transaction volumes, particularly within sectors like Smart Government, Information Security, and Digital Industrialization. The preference for agile and scalable NoSQL databases is growing, challenging the traditional dominance of Relational Database Management Systems (RDBMS). However, the legacy systems still hold a significant market share, particularly in established industries, leading to a dynamic market landscape with both established players and innovative newcomers vying for dominance. We estimate the 2025 market size at $150 billion, based on observable market trends and growth patterns within adjacent technology sectors. A compound annual growth rate (CAGR) of 12% is projected through 2033, indicating a substantial increase in market value and influence over the next decade. This growth is further segmented by database type (RDBMS and NoSQL), application (Smart Government, Information Security, etc.), and geographic region. The restraints on market growth primarily stem from concerns regarding data security and compliance, the complexities of data migration, and the high initial investment required for implementing advanced OLTP solutions. Despite these challenges, the overall trend demonstrates significant potential. The increasing reliance on real-time data analytics, coupled with the rising adoption of Internet of Things (IoT) technologies, will further accelerate the demand for robust and scalable OLTP systems. This necessitates a focus on developing advanced security measures, streamlined integration processes, and cost-effective cloud-based solutions to overcome existing limitations and unlock the full potential of the OLTP market. North America currently holds a leading market share due to high technological adoption and established digital infrastructure, but Asia Pacific is expected to witness significant growth in the coming years due to rapid digitalization efforts in major economies like India and China.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

The size and share of the market is categorized based on Type (Relational Database Server, Time Series Database Server, Object Oriented Database Server, Navigational Database Server) and Application (Education, Financial Services, Healthcare, Government, Life Sciences, Manufacturing, Retail, Utilities, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Database Automation Market size was worth around USD 1.74 Billion in 2023 and is predicted to grow to around USD 16.52 Billion by 2032

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Analysis of ‘Parking regulations (except non-metered color curb)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9f4197c7-7777-4bca-a72f-e2a8fd7c049f on 13 February 2022.

--- Dataset description provided by original source is as follows ---

"A. SUMMARY Parking regulations by blockface for the City of San Francisco. Includes the following regulations: Residential Parking Permits, Time limits, Government Permit, No overnight, Oversized Vehicle. Does not include non metered color curb or curb cuts. Update as of 1/1/2018: started recording No parking any time (regulations like ""TOW AWAY NO PARKING""), Limited No Parking (tow-away or no parking for certain periods of the day),

B. METHODOLOGY Mix of manual updates and data compilation.

C. UPDATE FREQUENCY Updated as MTA Board resolutions are passed that impact parking regulations.

D. OTHER CRITICAL INFO This dataset has not been comprehesively updated or vetted for accuracy. Dataset does not include color curb regulations such as loading zones or blue zones. Does not include detailed information for metered parking such as cap color or operating hours, which is contained in a separate relational database maintained by SFpark. No parking any time blockfaces were verified using Google Streetview, and only for Mission Bay blockfaces (Feburary 2017). "

--- Original source retains full ownership of the source dataset ---

Access to high quality exploration data is essential to effectively assess exploration risk. To encourage exploration in its many under-explored regions, Australia has traditionally maintained better access to government geoscience and petroleum exploration data than almost anywhere else in the world. Access to petroleum exploration information has been facilitated by legislation requiring data submission and availability, and by the provision of pre-competitive studies by government agencies. This is coupled with an aggressive, globally and yearly promoted, acreage release program. Recent initiatives however have improved access even more.

The Australian government has an active new program of data acquisition in poorly explored areas and the recently announced Spatial Information and Data Access Policy requires that basic data be made available at the marginal cost of transfer, or is free if via the internet.

Available information includes basic field data, comprising well, seismic and other survey data, interpretative data developed as part of petroleum prospectivity assessments by industry, and pre-competitive data sets and studies carried out by government. To facilitate access, and use of these data sets, the Australian government has made publicly available, relational digital databases containing information such as source rock potential, reservoir properties, shows, biostratigraphy, and well, and survey details. Parameters from databases can be plotted on-line. The information is free via the internet, and data can be downloaded in a variety of formats for use by explorers. Seismic field data, for reprocessing or interpretation, can be ordered from the on-line survey database at minimal cost and is heavily used by industry. Currently, 5 terabytes of seismic field data are borrowed each year from the Australian Government for reprocessing. The main borrowers are petroleum companies followed by data contractors.

So that explorers can access any onshore or offshore information, a single geoscience portal on the internet has been developed http://www.geoscience.gov.au/. Moreover, the Australian Government, through Geoscience Australia is conducting regional studies of petroleum prospectivity of the offshore jurisdiction to assist explorers.

Access to petroleum exploration data has been subject to legislation since the 1950s when the Petroleum Search Subsidy Act subsidised exploration and required that exploration data to be submitted for subsequent release after a relatively brief confidentiality period. The requirement to lodge exploration data was retained in the Petroleum (Submerged Lands) Act in 1967, whereas, subsidy for exploration was then discontinued. The Petroleum (Submerged Lands) Act in its current form is still in operation in Australia. The current ready access to petroleum exploration data has been of considerable assistance to companies in their exploration and in discovery of significant petroleum reserves in offshore Australia. Australia had one of the highest rates in the world in discovery of barrels of oil equivalent per year. The methods of making exploration data as conveniently accessible to explorers as possible are constantly being addressed with a view to further encouraging exploration and to maintaining Australia?s high exploration success.