NEWSQL In Memory Database Market Outlook

The global market size for NEWSQL In Memory Databases was estimated at USD 3.8 billion in 2023 and is projected to reach USD 10.9 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 12.3% during the forecast period. The growth of this market is primarily driven by the increasing demand for high-speed data processing and real-time analytics across various industries. As businesses continue to generate vast amounts of data, there is a growing need for efficient database management solutions that can handle these large data volumes with low latency. The adoption of NEWSQL In Memory databases, which combine the scalability of NoSQL with the ACID compliance of traditional SQL databases, is thus on the rise.

The demand for real-time data analytics and processing is a significant growth driver for the NEWSQL In Memory Database market. As industries such as BFSI, healthcare, and retail increasingly rely on data-driven decision-making processes, the need for fast and efficient database solutions becomes paramount. NEWSQL In Memory databases provide the ability to process large datasets quickly, enabling businesses to gain insights and make decisions in real time. This is particularly crucial in sectors like finance and healthcare, where timely information can significantly impact outcomes.

The advent of technologies such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) also fuels the growth of the NEWSQL In Memory Database market. These technologies generate immense amounts of data, requiring robust database solutions that can handle high-throughput and low-latency transactions. NEWSQL In Memory databases are well-suited for these applications, providing the necessary speed and scalability to manage the data efficiently. Furthermore, the rising adoption of cloud computing and the shift towards digital transformation in various industries further bolster the market's expansion.

Another crucial factor contributing to the market's growth is the increasing emphasis on customer experience and personalized services. Businesses are leveraging data to understand customer behavior, preferences, and trends to offer tailored experiences. NEWSQL In Memory databases enable organizations to analyze customer data in real time, enhancing their ability to provide personalized services. This is evident in the retail sector, where businesses use real-time analytics to optimize inventory, improve customer engagement, and boost sales.

In-Memory Grid technology plays a pivotal role in enhancing the performance of NEWSQL In Memory databases. By storing data in the main memory, In-Memory Grids significantly reduce data retrieval times, allowing for faster data processing and real-time analytics. This capability is particularly beneficial in scenarios where rapid access to data is crucial, such as in financial transactions or healthcare diagnostics. The integration of In-Memory Grid technology with NEWSQL databases not only boosts speed but also improves scalability, enabling businesses to handle larger datasets efficiently. As industries continue to demand high-speed data processing solutions, the adoption of In-Memory Grids is expected to rise, further driving the growth of the NEWSQL In Memory Database market.

On a regional level, North America holds a significant share of the NEWSQL In Memory Database market, driven by the presence of major technology companies and early adoption of advanced database solutions. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid digitalization and increasing investments in technology infrastructure. Europe also shows substantial potential, with a growing focus on data-driven strategies and compliance with stringent data regulations.

Type Analysis

The NEWSQL In Memory Database market can be segmented by type into operational and analytical databases. Operational databases are designed to handle real-time transaction processing, making them ideal for applications that require fast and efficient data entry and retrieval. These databases are commonly used in industries such as finance, retail, and telecommunications, where the ability to process transactions quickly is critical. The demand for operational NEWSQL In Memory databases is growing as businesses increasingly rely on real-time data for decision-making and operational efficiency.

Quick Stats API is the programmatic interface to the National Agricultural Statistics Service's (NASS) online database containing results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

In-Memory Database Market size was valued at USD 9.84 Billion in 2024 and is projected to reach USD 35.52 Billion by 2031, growing at a CAGR of 19.20% during the forecast period 2024-2031.

Global In-Memory Database Market Drivers

Demand for Real-Time Analytics: Companies are depending more and more on real-time data to make prompt, well-informed choices. Because they speed up data processing, in-memory databases are crucial for real-time analytics applications. Growth of Big Data and IoT: Large volumes of data are generated by the spread of big data and the Internet of Things (IoT), which must be quickly processed and analyzed. Large data volumes can be handled by in-memory databases more effectively than by conventional disk-based databases. Both Scalability and Performance Requirements: Databases that can scale to accommodate growing data loads without sacrificing performance are essential for growing enterprises. Growing businesses can benefit from the great scalability and performance of in-memory databases. Developments in Memory Technologies: As memory technologies like RAM and flash memory continue to progress, in-memory databases are becoming more widely available and reasonably priced for a greater variety of uses. Quicker Decision-Making Is Required: Businesses must act fast in the current competitive environment in order to stay ahead. Decision-making processes can go more quickly because to in-memory databases' faster data access and processing speeds. Demand for Real-Time Personalization: To improve consumer experiences, real-time personalization is becoming more and more necessary as e-commerce and online services expand in popularity. Large volumes of client data may be instantly analyzed by in-memory databases, allowing them to provide tailored content and recommendations.

In this article, I show how to fit a generalized linear model to N observations on p variables stored in a relational database, using one sampling query and one aggregation query, as long as N12+δ observations can be stored in memory, for some δ>0. The resulting estimator is fully efficient and asymptotically equivalent to the maximum likelihood estimator, and so its variance can be estimated from the Fisher information in the usual way. A proof-of-concept implementation uses R with MonetDB and with SQLite, and could easily be adapted to other popular databases. I illustrate the approach with examples of taxi-trip data in New York City and factors related to car color in New Zealand. Supplementary materials for this article are available online.

This database contains reactor physic data gathered from high-fidelity sodium fast reactor MCNP models. Each reactor design contains values such as k-eff, beta-eff, sodium void coefficient, Doppler coefficient, etc. The data is stored as an h5 database and can easily be converted to a Pandas dataframe for manipulation.

The University of Hawaii Sea Level Center (UHSLC) assembles and distributes the Fast Delivery (FD) dataset of hourly- and daily-averaged tide gauge water-level observations. Tide gauge operators, or data creators, provide FD data to UHSLC after a level 1 quality assessment (see processing_level attribute). The UHSLC provides an independent quality assessment of the time series and makes FD data available within 4-6 weeks of collection. This is a "fast" turnaround time compared to Research Quality (RQ) data, which are available on an annual cycle after a level 2 quality assessment. RQ data replace FD data in the data stream as the former becomes available. This file contains hybrid time series composed of RQ data when available with FD data appended to the end of each RQ series. acknowledgement=The UHSLC Fast Delivery database is supported by the National Oceanic and Atmospheric Administration (NOAA) Office of Climate Observations (OCO). cdm_data_type=TimeSeries cdm_timeseries_variables=uhslc_id, latitude, longitude Conventions=CF-1.10, ACDD-1.3, COARDS Easternmost_Easting=358.862 featureType=TimeSeries geospatial_lat_max=82.492 geospatial_lat_min=-69.0 geospatial_lat_units=degrees_north geospatial_lon_max=358.862 geospatial_lon_min=3.412 geospatial_lon_units=degrees_east infoUrl=https://uhslc.soest.hawaii.edu/data/ institution=University of Hawaii Sea Level Center Northernmost_Northing=82.492 processing_level=Fast Delivery (FD) data undergo a level 1 quality assessment (e.g., unit and timing evaluation, outlier detection, combination of multiple channels into a primary channel, etc.). In this file, FD data are appended to Research Quality (RQ) data that have received a level 2 quality assessment (e.g., tide gauge datum evaluation, assessment of level ties to tide gauge benchmarks, comparison with nearby stations, etc.). sourceUrl=(local files) Southernmost_Northing=-69.0 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=latitude, longitude, station_name, station_country, station_country_code, record_id, uhslc_id, gloss_id, ssc_id, last_rq_date time_coverage_end=2025-05-31T12:00:00Z time_coverage_start=1846-01-04T12:00:00Z Westernmost_Easting=3.412

High Speed Data Transfer System Market Outlook

The global market size for High Speed Data Transfer Systems was valued at USD 15 billion in 2023 and is forecasted to reach USD 45 billion by 2032, growing at a CAGR of approximately 13% during the forecast period. This remarkable growth can be attributed to the increasing demand for higher bandwidth, the proliferation of connected devices, and the advent of technologies like 5G and IoT that necessitate rapid and reliable data transfer.

One of the primary growth factors for the high-speed data transfer system market is the explosion of data generated worldwide. With the rise of big data analytics, cloud computing, and the Internet of Things (IoT), there is an unprecedented need for efficient and fast data transfer solutions. Enterprises are increasingly investing in robust data transfer systems to manage and process vast amounts of data effectively, driving the market growth. Additionally, the emergence of 5G technology is revolutionizing data transfer speeds, providing new opportunities for market expansion.

Another significant driver is the increasing adoption of high-speed data transfer systems in various sectors such as healthcare, BFSI (Banking, Financial Services, and Insurance), and media and entertainment. These industries require rapid and secure data transfer to enhance their operational efficiencies and provide better services to customers. The healthcare sector, in particular, is seeing substantial investments in data transfer systems to facilitate telemedicine, electronic health records, and real-time patient monitoring, further propelling market growth.

The rise of data centers and the need for efficient data management are also contributing to the market's expansion. Data centers serve as the backbone of the modern digital economy, housing critical data and applications for businesses and consumers alike. The demand for high-speed data transfer systems in data centers is growing as enterprises seek to improve data accessibility, reduce latency, and ensure seamless data flow across networks. This trend is expected to continue, leading to significant market growth over the forecast period.

From a regional perspective, North America is anticipated to hold the largest market share due to the early adoption of advanced technologies and the presence of key market players. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing investments in infrastructure development, rapid urbanization, and the growing number of internet users. Europe, Latin America, and the Middle East & Africa are also projected to experience substantial growth, supported by technological advancements and increasing demand for high-speed data transfer solutions.

Component Analysis

The high-speed data transfer system market is segmented by component into hardware, software, and services. The hardware segment includes devices such as routers, switches, and cables that facilitate data transfer. This segment is expected to dominate the market due to the continuous advancements in networking technology and the increasing need for robust and reliable hardware solutions. The demand for high-performance hardware components is rising, driven by the need for faster data transfer speeds and improved network efficiency.

The software segment encompasses various applications and platforms that enable efficient data transfer and management. This includes data transfer protocols, network management software, and data compression tools. The software segment is expected to witness significant growth, driven by the increasing adoption of cloud-based solutions and the need for advanced data management capabilities. Software solutions play a crucial role in optimizing data transfer processes, reducing latency, and ensuring data security, thereby driving market growth.

The services segment includes consulting, integration, and maintenance services that support the deployment and management of high-speed data transfer systems. This segment is also poised for substantial growth as enterprises seek expert guidance to implement and maintain these complex systems. The demand for professional services is increasing as businesses aim to optimize their data transfer infrastructures, improve operational efficiencies, and ensure seamless data flow across networks.

Overall, the component analysis highlights the critical role that hardware, software, and services play in the high-speed data transfer system market. Each component segment is expe

This Data set include the FAST data that referred to the FRB 20190520B discovery Paper. In total 79 bursts were detected in FAST observation. The first 4 bursts are from FAST drift scan survey (CRAFTS project), and the other are using tracking mode.

Context

This is a list of 10,000 fast-food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

If you like the dataset, do upvote!

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Comprehensive dataset of 367,275 Fast food restaurants in India as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Xavvy fuel is the leading source for location data and market insights worldwide. We specialize in data quality and enrichment, providing high-quality POI data for restaurants and quick-service establishments in the United States.

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ... ^

30+ Services • Delivery • Wifi • ChargePoints • …

10+ Payment options • Visa • MasterCard • Google Pay • individual Apps • ...

Our data offering is highly customizable and flexible in delivery – whether one-time or regular data delivery, push or pull services, and various data formats – we adapt to our customers' needs.

Brands included: • McDonalds • Burger King • Subway • KFC • Wendy's • ...

The total number of restaurants per region, market share distribution among competitors, or the ideal location for new branches – our restaurant data provides valuable insights into the food service market and serves as the perfect foundation for in-depth analyses and statistics. Our data helps businesses across various industries make informed decisions regarding market development, expansion, and competitive strategies. Additionally, our data contributes to the consistency and quality of existing datasets. A simple data mapping allows for accuracy verification and correction of erroneous entries.

Especially when displaying information about restaurants and fast-food chains on maps or in applications, high data quality is crucial for an optimal customer experience. Therefore, we continuously optimize our data processing procedures: • Regular quality controls • Geocoding systems to refine location data • Cleaning and standardization of datasets • Consideration of current developments and mergers • Continuous expansion and cross-checking of various data sources

Integrate the most comprehensive database of restaurant locations in the USA into your business. Explore our additional data offerings and gain valuable market insights directly from the experts!

Comprehensive dataset of 20,874 Fast food restaurants in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Xtract.io's Subway restaurant POI data delivers a comprehensive view of the brand's extensive fast food chain locations across the United States and Canada. Franchise investors, business analysts, and market researchers can utilize this QSR location data to understand Subway's market penetration, identify potential growth areas, and develop targeted strategic insights for quick service restaurant analysis.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive restaurant location intelligence landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurant chain locations -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Fast Food POI Data? At LocationsXYZ, we: -Deliver restaurant POI data with 95% accuracy -Refresh QSR location data every 30, 60, or 90 days to ensure the most recent information -Create on-demand fast food chain datasets tailored to your specific needs -Handcraft boundaries (geofences) for restaurant locations to enhance accuracy -Provide restaurant POI data and polygon data in multiple file formats

Unlock the Power of Restaurant Location Data With our point-of-interest data for food service establishments, you can: -Perform thorough market analyses for QSR expansion -Identify the best locations for new restaurant stores -Gain insights into consumer behavior and dining patterns -Achieve an edge with competitive intelligence in the fast food industry

LocationsXYZ has empowered businesses with geospatial insights and restaurant location intelligence, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge Subway restaurant POI data.

The fast data entry tool market is experiencing robust growth, driven by the increasing need for efficient and accurate data processing across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility; the growing demand for automation to reduce manual data entry errors and improve productivity; and the increasing digitization across industries, generating massive volumes of data requiring swift and precise entry. SMEs are a significant segment, adopting these tools to streamline operations and compete effectively. Large enterprises, meanwhile, leverage fast data entry tools for comprehensive data management and integration with existing systems, improving overall business intelligence. The market is segmented by deployment type (cloud-based and on-premises), with cloud-based solutions gaining significant traction due to their flexibility and cost-effectiveness. While the on-premises market retains a presence, especially in sectors with stringent data security requirements, the cloud segment is expected to dominate market share in the coming years. Geographic distribution shows strong growth across North America and Europe, followed by a steadily increasing adoption in the Asia-Pacific region. Competitive pressures are shaping the market landscape. Established players like HubSpot and UiPath are leveraging their existing customer bases and robust functionalities to maintain leadership, while emerging innovative companies are pushing boundaries with cutting-edge features and AI-driven solutions. Factors such as high initial investment costs and the need for specialized skills in implementation can pose restraints. However, the long-term cost savings achieved through improved efficiency and reduced error rates are significant drivers of market expansion. Future growth will likely be shaped by the increasing integration of AI and machine learning capabilities within fast data entry tools, further enhancing automation, accuracy, and overall efficiency. The market is poised for substantial growth in the forecast period (2025-2033), reflecting a continued demand for seamless and high-speed data entry across diverse industries and geographies.

Comprehensive dataset of 44,880 Fast food restaurants in France as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

The Forensic Anthropology Skeletal Trauma (FAST) database is a novel resource, funded by the National Institute of Justice, which provides trauma analysis data for education, training, and case comparisons. Students, academics, and practitioners can gain an interdisciplinary perspective of skeletal trauma through an examination of outcomes from experimental research utilizing human specimens with known loading mechanisms. The largest obstacle for the field of forensic anthropology is exposure to trauma analysis. Few researchers get quality hands-on training with trauma cases and even fewer have experience with cases involving unequivocally known loading and injury mechanisms. Improvement in skeletal trauma analyses and interpretations is dependent on dissemination in a user-friendly format that allows for training and education and supports forensic professionals in practice. FAST features pre- and post-test imaging, data collected from advanced instrumentation during the impact event, and fracture analysis data. The Forensic Anthropology Skeletal Trauma Database provides a unique opportunity to explore a large sample of skeletal trauma on various regions of the human body and gain insight into objective trauma interpretation. The ability for students and professionals, at all stages in their career, to be exposed to skeletal trauma with known parameters has the potential to be transformative for the field. This freely available resource is an innovative solution to break down pre-existing barriers students and professionals have in accessing trauma specimens. Our goal is to continue to develop FAST through inclusion of past and future experimental skeletal trauma research.

A new, relational database to be used for disease gene discovery, gene annotation and reporting, and searching for genes for future studies in model organisms. It incorporates 5 layers of information about the genes residing in it- the expression information from a gene (as reported in Unigene), the cytological location of the gene (if available), the ortholog of each gene in the available species within the database, the divergence information between species for each gene, and functional information as reported by OMIM and the Enzyme Commission (EC) reference number of genes. Tables have also been created to help record polymorphism data and functional information about specific changes within or between species, such as measured by Granthams distance (1) or model organism studies.

Calibrated Fluxgate Data acquired by the Fast Auroral SnapshoT Small Explorer, FAST, Magnetometer Instrument. Data have been calibrated, despun, and detrended against the International Geomagnetic Reference Field, IGRF, using IGRF Coefficients for the Date of Acquisition. Data are provided in several Coordinate Systems. Non detrended Data in Spacecraft and Geocentric Equatorial Inertial Coordinates are provided. Ephemeris Data are also provided.