The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

Embedded Database Management Systems Market size was valued at USD 10.8 Billion in 2024 and is projected to reach USD 18.70 Billion by 2031, growing at a CAGR of 7.1% during the forecasted period 2024 to 2031.

The Embedded Database Management Systems (DBMS) market is driven by the increasing demand for real-time data processing and management across various embedded systems, such as IoT devices, smartphones, automotive systems, and industrial equipment. The rise of connected devices and edge computing has amplified the need for lightweight, efficient, and scalable embedded databases that can operate within resource-constrained environments. Growing adoption of embedded systems in industries like healthcare, automotive, telecommunications, and consumer electronics is also boosting the demand for robust DBMS solutions. Additionally, advancements in AI, machine learning, and data analytics are driving the integration of more sophisticated embedded databases to enable real-time decision-making and enhance device performance.

Information for how to cite the MTE bundle.

The size of the Document Databases 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.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

Reproducibility data for the AntiBody Sequence Database (ABSD) article. This dataset contains the raw data (antibody sequences) extracted on June 20, 2024, from various databases, as well as the several scripts, to ensure the reproducibility of our results. External databases used: ABDB, AbPDB, CoV-AbDab, Genbank, IMGT, PDB, SACS, SAbDab, TheraSAbDab, UniProt, KABAT Scripts usage: each external database has a corresponding script to format all antibody sequences extracted from it. A last script enable merging all extracted antibody sequences while removing redundancy, standardizing and cleaning data.

Discover the explosive growth of the Information Technology Application Innovation Databases market. This in-depth analysis reveals a $50B market in 2025, projected to reach $150B by 2033, driven by cloud computing, big data, and digital transformation. Explore key trends, regional breakdowns, and leading companies shaping this dynamic sector.

According to Cognitive Market Research, the global In-Memory Database market size was USD 7.8 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 19.1% from 2024 to 2031. Market Dynamics of In-Memory Database Market

Key Drivers for In-Memory Database Market

Increasing Volume of Data - The exponential growth of data generated by various sources, including social media, IoT devices, and enterprise applications, is another key driver for the IMDB market. Organizations are increasingly seeking efficient ways to manage and analyze this vast amount of data to gain actionable insights and maintain a competitive edge. In-memory databases are well-suited to handle large volumes of data with high throughput, providing the scalability needed to accommodate the growing data influx. The ability to scale horizontally by adding more nodes to the database cluster ensures that IMDBs can meet the demands of data-intensive applications.
The increasing dependence on real-time analytics and decision-making is anticipated to drive the In-Memory Database market's expansion in the years ahead.

Key Restraints for In-Memory Database Market

The amount of available RAM, which can restrict their scalability for very large datasets, limits the In-Memory Database industry growth.
The market also faces significant difficulties related to the high cost of implementation.

Introduction of the In-Memory Database Market

The In-Memory Database market is experiencing robust growth, driven by the need for high-speed data processing and real-time analytics across various industries. In-memory databases store data directly in the main memory (RAM) rather than on traditional disk storage, allowing for significantly faster data retrieval and manipulation. This technology is particularly advantageous for applications requiring rapid transaction processing and real-time data insights, such as financial services, telecommunications, and e-commerce. Despite its benefits, the market faces challenges, including high implementation costs and limitations on data storage capacity due to RAM constraints. Additionally, concerns about data volatility and the need for continuous power supply further complicate adoption. However, advancements in memory technology, declining costs of RAM, and the increasing demand for real-time analytics are driving market growth. As businesses seek to enhance performance and decision-making capabilities, the In-Memory Database market is poised for continued expansion, providing critical solutions for high-performance data management.

The Database Automation Systems market is booming, projected to reach $45 billion by 2033 with a 12% CAGR. Explore market trends, key players (Oracle, IBM, Microsoft), and regional insights in this comprehensive analysis. Discover the impact of cloud adoption and DevOps on this rapidly evolving sector.

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

This repo contains the npz files of the database that is required by the RANGE model. This dataset is associated with the paper RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings (CVPR 2025). Code: https://github.com/mvrl/RANGE

The world has digitized rapidly, especially since the advent of the internet. Banks, financial institutions, hospitals, insurance companies, and e-commerce platforms rely heavily on databases to manage customer accounts, transactions, and sensitive financial data. With the advancements in the technology sector, the database monitoring software market is poised to be valued at a staggering US$ 2.40 billion in 2024.

Category-wise Insights

Country-wise Insights

The US Consumer Household Database — Weekly Refreshed is AmeriList’s premier consumer dataset, built for marketers, agencies, and enterprises that demand accurate, scalable, and timely U.S. consumer data. Covering over 200 million households nationwide and enriched with 200+ lifestyle, demographic, and behavioral attributes, this file is one of the most complete and frequently updated consumer databases available today.

Why Choose This Database?

Today’s marketing success depends on reaching the right audience at the right time. With this dataset, you gain: - Nationwide coverage of U.S. households (≈95%). - Unmatched attribute depth including age, income, marital status, homeownership, and lifestyle interests. - Freshness you can trust with weekly updates to keep your campaigns aligned with real-world consumer changes. - Multi-channel readiness with delivery via CSV, API, SFTP, or cloud integrations (AWS, GCP, Azure).

Key Features - 200M+ U.S. households for broad reach. - 200+ attributes spanning demographics, lifestyle, purchase signals, and household composition. - Household-level granularity with linkable fields for segmentation and modeling. - Evaluation samples under NDA to test match rates and validate quality.

Use Cases This dataset powers a wide range of data-driven marketing strategies:

• Direct Mail Lists: Reach targeted households with personalized campaigns.
• CRM Enrichment: Append missing consumer attributes to strengthen customer records.
• Audience Segmentation: Build granular segments for more relevant messaging.
• Look-Alike Modeling: Expand your audience with predictive targeting.
• Digital Marketing: Activate high-value segments across social, programmatic, and CTV campaigns.

Industries That Benefit

• Retail & E-commerce: Personalize offers for higher conversions.
• Financial Services & Insurance: Target households by income, homeownership, or life stage.
• Healthcare & Wellness: Engage consumers with relevant health and lifestyle attributes.
• Automotive: Reach in-market households for new and used vehicles.
• Real Estate & Home Services: Connect with homeowners, renters, and movers.

Licensing & Access

The US Consumer Household Database is offered via 12-month subscription, with continuous weekly updates included. Evaluation samples are available under NDA. Flexible licensing models ensure it fits enterprises of all sizes.

Why AmeriList? For over 20 years, AmeriList has been a trusted leader in direct marketing data solutions. Our expertise in consumer databases, mailing lists, and CRM enrichment ensures not only the accuracy of the data but also the strategic value it delivers. With a focus on quality, compliance, and ROI, AmeriList helps brands and agencies unlock the full potential of consumer marketing.

The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

Forecast: Fixed Investment in Computer Software and Databases in Italy 2023 - 2027 Discover more data with ReportLinker!

The global Database Management System (DBMS) market is projected to reach a valuation of approximately USD 123 billion by 2033, growing at a compound annual growth rate (CAGR) of 11.5% from 2025 to 2033.

Benthic fauna data has been collected from 1881 to the present by the National Marine Fisheries Service Laboratories at Woods Hole, MA and Sandy Hook, NJ. The data includes the work by Wigley and Theroux on the macrofauna of the Northeastern United States. Other major studies include Ocean Pulse, the Northeast Monitoring Program, New York Bight, 12 Mile Dumpsite, Long Island Sound and Raritan Bay surveys. Parameters included in these surveys include depth, sediment type, gear type, number, weight, family, class, genus, species name, and abundance. A total of 21,000 sample sites are included in this data set with 4,000 meters being the maximum depth sampled.

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

Dataset

This dataset was created by Timerkhanov Yuriy

Released under CC0: Public Domain

Contents