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As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; 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.

The global in-memory database market size was valued at USD 10.5643 billion in 2025 and is projected to grow at a compound annual growth rate (CAGR) of 16.19% during the forecast period (2025-2033). The growth of the market is attributed to the increasing adoption of in-memory databases in various industries to improve data processing speed and performance. In-memory databases store data in the computer's main memory (RAM) instead of on a physical disk, which allows for faster data access and retrieval. Key market drivers include the growing volume of data, the need for real-time data analysis, and the increasing adoption of cloud computing. The growing volume of data, often referred to as "big data," is a significant factor driving market growth. The need for real-time data analysis is another key driver, as in-memory databases can provide faster data access than traditional databases. The increasing adoption of cloud computing is also driving market growth, as cloud-based in-memory databases offer scalability and flexibility. Recent developments include: March 2023: SAP revealed SAP Datasphere, the company's next-gen data management system. It gives customers easy access to business-ready data across the data landscape. SAP also announced strategic agreements with top data and AI companies, including Collibra NV, Confluent Inc., Databricks Inc., and DataRobot Inc., to improve SAP Datasphere and allow organizations to build a unified data architecture that securely combines SAP software data and non-SAP data., June 2023: IBM has released a new tool to aid corporations in monitoring their carbon footprint pollution across cloud services and improve their sustainability as they move to hybrid and multi-cloud environments. The IBM Cloud Carbon Calculator, an AI-powered dashboard, is now available to everyone. It can help clients access emissions data for various IBM Cloud tasks, such as AI, high-performance computing (HPC), and financial services., SingleStoreDB for December 2022 was announced last year by IBM and SingleStore. With IBM introducing SingleStoreDB as a solution, businesses are now moving forward in their strategic relationship to deliver the quickest, most scalable data platform that supports data-intensive programs. For Azure, AWS, and Microsoft Azure marketplace, IBM has released SingleStoreDB as a service., In April 2022, McObject issued the eXtremeDB/rt database management system (DBMS) for Green Hills Software’s Integrity RTOS. The first-ever commercial off-the-shelf (COTS) real-time DBMS satisfying basic criteria of temporal and deterministic consistency in data is known as eXtremeDB/rt. It was initially conceived and built as an integrated in-memory database system for embedded systems., November 2022: Redis, provider of real-time in-memory databases, and Amazon Web Services have formed a multi-year strategic alliance. It is a networked open-source NoSQL system that stores data on disk for durability before moving it to DRAM as required. As such, it can be used as a message broker cache, streaming engine, or database., December 2022: The largest Indian stock exchange, National Stock Exchange, opted for Raima Database Manager (RDM) Workgroup 12.0 In-Memory System as its foundational component for upcoming versions of its trading platform front-end called National Exchange for Automated Trading (NEAT)., On January 13th, 2021, Oracle launched Oracle Database 21c – the latest version of the world’s leading converged database available on Oracle Cloud with the Always Free tier of Oracle Autonomous Database included. It includes more than two hundred new features, according to Oracle’s press release, including immutable blockchain tables; In-Database JavaScript; native JSON binary data type; AutoML for in-database machine learning (ML); persistent memory store; enhancements, including improvements regarding graph processing performance that support sharding, multitenant, and security., Stanford engineers have developed a new chip to increase the efficiency of AI computing in August 2022. Stanford engineers have created a more efficient and flexible AI chip that could bring the power of AI into tiny edge devices., In-Memory Database Market Segmentation,

Relational

NoSQL

NewSQL

,

Online Analytical Processing (OLAP)

Online Transaction Processing (OLTP)

,

Transaction

Reporting

Analytics

,

North America

US

Canada

Europe

Germany

France

UK

Italy

Spain

Rest of Europe

Asia-Pacific

China

Japan

India

Australia

South Korea

Australia

Rest of Asia-Pacific

Rest of the World

Middle East

Africa

Latin America

, . Potential restraints include: Security And Data Privacy Concerns 26.

USAID Software Database reporting tool created in Oracle Application Express (APEX). This version provides read only access to a database view of the JIRA SAR (Software Approval Request) database (aka, the USAID Software List Database) instead of the previous static Oracle database.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. These data represent the OZMIN …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. These data represent the OZMIN Oracle relational database containing geological and resource information for Australian mineral deposits. OZMIN has been compiled from published references and has been designed so that attribute information can be retrieved and analysed in relation to spatial data contained in geographic information systems. The national mineral deposits dataset contains data on over one thousand major and historically significant mineral deposits for 60 mineral commodities (including coal). Data available via mapping interfaces on the Geoscience Australia website are updated weekly whilst data available via download are a snapshot at the "Ending Date" of the current database entries. Full Metadata available at: http://www.ga.gov.au/meta/ANZCW0703003393.html Dataset History The data within this dataset is derived directly from the corporate ORACLE OZMIN Mineral Deposits database. An ASCII extraction of the Geoscience Australia ORACLE database is generated as ASCII comma-delimited files for each table that is part of or used by the OZMIN database. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. An MS ACCESS database format is also replicated from the ORACLE database and uses the same table structure. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. The spatial representation of this database in (ArcView and MapInfo format) is extracted and generated using ArcInfo GIS software to meet the published data standard within the Geoscience Australia data dictionary. The extraction of the spatial GIS datasets is done within ArcInfo using advanced AML code (ORACOV.AML) developed by Dmitar Butrovski, Geoscience Australia. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-b68d-7506-e044-00144fdd4fa6/OZMIN+Mineral+Deposits+Database Dataset Citation Geoscience Australia (2013) OZMIN Mineral Deposits Database. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/34247a24-d3cf-4a98-bb9d-81671ddb99de.

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..

The AGSO Web server now has a page that allows public access to many of AGSO's Oracle database lookup tables. These tables are the key to the nomenclature and classifications used in our geoscientific databases, and provide a valuable resource to many Australian geologists. For example, the geological time scale table provides a comprehensive list of time terms used in Australia and elsewhere, their rank, scope, parent term and older and younger age boundaries in millions of years PB - according to the latest information. Or, the OZMIN mineral deposits attributes table, with nearly 2000 terms, provides a complete and authoritative classification of Australian ore deposits, as well as other attributes such as alteration, mineralisation style, gangue minerals, ore texture and relationships to host. with nearly 4500 terms, the largest of the 37 tables so far included is the extent-names table for our metadata system. The smallest, with just 9 terms, is the analyte categories table for the GWATER database. The table may be downloaded from the Web, or alternatively you may purchase them as ASCII files, as per AGSO Catalog No. 24488.

This record references the database of the final data from the Huon Estuary Study. The data from the project's access database has been transfered into an oracle database which is available on-line to project members. The access database tables have also been exported to text files for archiving to cdrom (held in CMR Data Centre-May 2004.) Data Dictionary files have also been stored on cdrom along with technical reports which describe the data modelling undertaken for the project. The database contains ammonia, arsenic, ctd header, dissolved oxygen, mercury, nutrient, organic matter, pigment, salinity, sediment, suspended solids, taxon counts and dominance data in their final processed form. Data in the database was collected at 61 stations on a seasonal regime (HES Surveys) Weekly/fortnightly (CM Surveys)at 5 of those stations. A sediment survey in May 1997 (SED1) and a Contaminants Survey in August/September 1998 (HES10A) were also carried out. The study was undertaken over the period 1996-1998. Refer to the other Huon Estuary Study metadata records to explore datasets which were not loaded into the database such as the ctd, snapshot and moored instrument data.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of 1244.08. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

This Record provides a description of the development and use of the version of the AGSO Library catalogue accessible from AGSO's World Wide Web site. The Library's in-house catalogue of books and serials is part of the Datatrek Professional Series library management system. Although this system operates from a Novell network server, the lack of integration of AGSO's Novell networks and lack of access to them from the Unix system means that direct access to Datatrek system is only available in the Library itself. Also, the Datatrek system has no provision for access, either directly or indirectly, from the Internet. In the interests of making the Library catalogue more readily available to both AGSO's staff and AGSO's clients, a decision was made to develop a version of the Datatrek catalogue as an Oracle database, and that that version would be made available on the World Wide Web of the Internet with a forms-based search interface usable on any Web browser.

The global database management system (DBMS) market revenue grew to 80 billion U.S. dollars in 2020. Cloud DBMS accounted for the majority of the overall market growth, as database systems are migrating to cloud platforms.

Database market

The database market consists of paid database software such as Oracle and Microsoft SQL Server, as well as free, open-source software options like PostgreSQL and MongolDB. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Database management software

Knowledge of the programming languages related to these databases is becoming an increasingly important asset for software developers around the world, and database management skills such as MongoDB and Elasticsearch 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.

[Keywords] Market include Ebay, IBM, Intel, Amazone, Oracle

This data set includes photos and multimedia data of Xizang's salt lakes. The data comes from field survey and shooting, books and network collection. The dataset mainly comes from the organization of historical data and field investigation and shooting. The current collection time span is from October 2019 to June 2023. The total data volume is 3.70GB. The dataset is stored in various ways such as CDs, hard drives, and servers. The data structure types stored on CDs and hard drives are jpg, mp4, and text formats (ACCESS 2002-2003). The server storage uses the Oracle database.

This dataset spatially represents the OZROX Field Geology Database.The OZROX database contains location and field description information. Field descriptions include, information on lithology, stratigraphic unit, alteration, magnetic susceptibility, hand-held radiometric spectrometer response and structural measurements.OZROX has over 100 000 field sites derived mainly from AGSO - Geoscience Australia's mapping, with additional contributions from Universities and State Surveys. Many of Geoscience Australia's laboratory databases link to OZROX in the corporate Oracle relational database system.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is an excel spreadsheet extract of all GAL coal deposits and resources was derived by the Bioregional Assessment Programme from the 2012 OZMIN database from Geoscience …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset is an excel spreadsheet extract of all GAL coal deposits and resources was derived by the Bioregional Assessment Programme from the 2012 OZMIN database from Geoscience Australia. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. Dataset History Black coal deposits and resources from the 2012 OZMIN database, which lie within the GAL subregion were extracted in tabular form. http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_a05f7892-b68d-7506-e044-00144fdd4fa6/OZMIN+Mineral+Deposits+Database The data within this dataset is derived directly from the corporate ORACLE OZMIN Mineral Deposits database. An ASCII extraction of the Geoscience Australia ORACLE database is generated as ASCII comma-delimited files for each table that is part of or used by the OZMIN database. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. An MS ACCESS database format is also replicated from the ORACLE database and uses the same table structure. Only data that is part of the current release of OZMIN (Release 3 - October 2000) is included. The spatial representation of this database in (ArcView and MapInfo format) is extracted and generated using ArcInfo GIS software to meet the published data standard within the Geoscience Australia "http://www.ga.gov.au/standards/datadict.html" data dictionary. The extraction of the spatial GIS datasets is done within ArcInfo using advanced AML code (ORACOV.AML). Dataset Citation Bioregional Assessment Programme (2014) OZMIN black coal deposits and resources 2012 GAL. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/8d304612-8415-40c9-9fac-86978115655c. Dataset Ancestors Derived From OZMIN Mineral Deposits Database

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The Great Lakes Research Vessel Operations data release is taken from the Research Vessel Catch (RVCAT) database curated at the Great Lakes Science Center (GLSC). RVCAT has been used as the primary data collection tool aboard the GLSC’s research vessel operations. Content: The data set has been collected on various vessel operations on all the Great Lakes and select connecting waterways between the years 1958-2018. Data collection begins in early spring and ends in late fall. Each vessel operation was completed for a specific purpose, or target mission, which are enumerated in this data set. In addition to vessel operations data, RVCAT collects trawl and gillnet catch data, sample information of fish species caught, as well as effort applied. The data set also contains data on benthos samples collected. Although not included in this data release, the GLSC collects or collected zebra mussel, hydroacoustics, ichthyoplankton, and zooplankton data in relation to vessel operations. Data Structure: These data are collected at the GLSC in the RVCAT Oracle database. The RVCAT database that is the basis for the following data release has been broken down into over 100 tables in order to facilitate greater accessibility. For ease of use, these tables are organized into conceptual groups – Operations, Trawl, Gillnet, Mensuration, Mysis, Benthos Ponar, and Reference. The database structure is described within the metadata record (XML) and entity relationship diagram within each concept group web page. Data Release Method: An updated version of the data will be published at least yearly, which will include new operations data, show any changes to the database structure, and have more quality control applied to the data from previous releases. The metadata will also be updated alongside these data releases in order to describe the data at that time of publication. See the revision history document to follow the structural changes to the database over each data release. Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time of release. Also, a large section of this database includes legacy data that if issues arise for cannot be addressed, but nevertheless adds great value to the database. When approaching the following data release, it is strongly suggested to approach the Great Lakes Science Center's researchers for input. Distribution Liability Statement: Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.

OZMIN is a new mineral deposits database which has been developed in AGSO for use in the National Geoscience Mapping Accord (NGMA) and in national metallogenic research. OZMIN has been structured so that the attribute data can be retrieved and analysed in relation to spatial data contained in a geographic information system (GIS). It utilises the Oracle relational database management system. This Record provides the necessary documentation for users to access and enter data specific to mineral deposits, prospects, and occurrences; their host rocks; and important features of their geological setting. The Record explains the relationship of OZMIN to other AGSO relational databases, the attributes used in the database and their legal values. It also describes the various screen forms used in the database and provides information on those corporate authority tables commonly accessed by OZMIN.

A set of inter-connecting Access databases and an Oracle BI data warehouse used by specialist HR data experts. Provide the single source of Hannigan-compliant anonymised HR data for key performance indicator reporting across the Department and its businesses on a monthly basis.