The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Fixed 30-year mortgage rates in the United States averaged 6.67 percent in the week ending August 8 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

30 Year Mortgage Rate in the United States decreased to 6.58 percent in August 14 from 6.63 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

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.

This high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. In the 2100 intermediate scenario represented here, the modeled water level is 136 cm (99 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

This archived public use dataset has 11 data elements reflecting United States COVID-19 community levels for all available counties.

The COVID-19 community levels were developed using a combination of three metrics — new COVID-19 admissions per 100,000 population in the past 7 days, the percent of staffed inpatient beds occupied by COVID-19 patients, and total new COVID-19 cases per 100,000 population in the past 7 days. The COVID-19 community level was determined by the higher of the new admissions and inpatient beds metrics, based on the current level of new cases per 100,000 population in the past 7 days. New COVID-19 admissions and the percent of staffed inpatient beds occupied represent the current potential for strain on the health system. Data on new cases acts as an early warning indicator of potential increases in health system strain in the event of a COVID-19 surge.

Using these data, the COVID-19 community level was classified as low, medium, or high.

COVID-19 Community Levels were used to help communities and individuals make decisions based on their local context and their unique needs. Community vaccination coverage and other local information, like early alerts from surveillance, such as through wastewater or the number of emergency department visits for COVID-19, when available, can also inform decision making for health officials and individuals.

For the most accurate and up-to-date data for any county or state, visit the relevant health department website. COVID Data Tracker may display data that differ from state and local websites. This can be due to differences in how data were collected, how metrics were calculated, or the timing of web updates.

Archived Data Notes:

This dataset was renamed from "United States COVID-19 Community Levels by County as Originally Posted" to "United States COVID-19 Community Levels by County" on March 31, 2022.

March 31, 2022: Column name for county population was changed to “county_population”. No change was made to the data points previous released.

March 31, 2022: New column, “health_service_area_population”, was added to the dataset to denote the total population in the designated Health Service Area based on 2019 Census estimate.

March 31, 2022: FIPS codes for territories American Samoa, Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands were re-formatted to 5-digit numeric for records released on 3/3/2022 to be consistent with other records in the dataset.

March 31, 2022: Changes were made to the text fields in variables “county”, “state”, and “health_service_area” so the formats are consistent across releases.

March 31, 2022: The “%” sign was removed from the text field in column “covid_inpatient_bed_utilization”. No change was made to the data. As indicated in the column description, values in this column represent the percentage of staffed inpatient beds occupied by COVID-19 patients (7-day average).

March 31, 2022: Data values for columns, “county_population”, “health_service_area_number”, and “health_service_area” were backfilled for records released on 2/24/2022. These columns were added since the week of 3/3/2022, thus the values were previously missing for records released the week prior.

April 7, 2022: Updates made to data released on 3/24/2022 for Guam, Commonwealth of the Northern Mariana Islands, and United States Virgin Islands to correct a data mapping error.

April 21, 2022: COVID-19 Community Level (CCL) data released for counties in Nebraska for the week of April 21, 2022 have 3 counties identified in the high category and 37 in the medium category. CDC has been working with state officials to verify the data submitted, as other data systems are not providing alerts for substantial increases in disease transmission or severity in the state.

May 26, 2022: COVID-19 Community Level (CCL) data released for McCracken County, KY for the week of May 5, 2022 have been updated to correct a data processing error. McCracken County, KY should have appeared in the low community level category during the week of May 5, 2022. This correction is reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for several Florida counties for the week of May 19th, 2022, have been corrected for a data processing error. Of note, Broward, Miami-Dade, Palm Beach Counties should have appeared in the high CCL category, and Osceola County should have appeared in the medium CCL category. These corrections are reflected in this update.

May 26, 2022: COVID-19 Community Level (CCL) data released for Orange County, New York for the week of May 26, 2022 displayed an erroneous case rate of zero and a CCL category of low due to a data source error. This county should have appeared in the medium CCL category.

June 2, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a data processing error. Tolland County, CT should have appeared in the medium community level category during the week of May 26, 2022. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Tolland County, CT for the week of May 26, 2022 have been updated to correct a misspelling. The medium community level category for Tolland County, CT on the week of May 26, 2022 was misspelled as “meduim” in the data set. This correction is reflected in this update.

June 9, 2022: COVID-19 Community Level (CCL) data released for Mississippi counties for the week of June 9, 2022 should be interpreted with caution due to a reporting cadence change over the Memorial Day holiday that resulted in artificially inflated case rates in the state.

July 7, 2022: COVID-19 Community Level (CCL) data released for Rock County, Minnesota for the week of July 7, 2022 displayed an artificially low case rate and CCL category due to a data source error. This county should have appeared in the high CCL category.

July 14, 2022: COVID-19 Community Level (CCL) data released for Massachusetts counties for the week of July 14, 2022 should be interpreted with caution due to a reporting cadence change that resulted in lower than expected case rates and CCL categories in the state.

July 28, 2022: COVID-19 Community Level (CCL) data released for all Montana counties for the week of July 21, 2022 had case rates of 0 due to a reporting issue. The case rates have been corrected in this update.

July 28, 2022: COVID-19 Community Level (CCL) data released for Alaska for all weeks prior to July 21, 2022 included non-resident cases. The case rates for the time series have been corrected in this update.

July 28, 2022: A laboratory in Nevada reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate will be inflated in Clark County, NV for the week of July 28, 2022.

August 4, 2022: COVID-19 Community Level (CCL) data was updated on August 2, 2022 in error during performance testing. Data for the week of July 28, 2022 was changed during this update due to additional case and hospital data as a result of late reporting between July 28, 2022 and August 2, 2022. Since the purpose of this data set is to provide point-in-time views of COVID-19 Community Levels on Thursdays, any changes made to the data set during the August 2, 2022 update have been reverted in this update.

August 4, 2022: COVID-19 Community Level (CCL) data for the week of July 28, 2022 for 8 counties in Utah (Beaver County, Daggett County, Duchesne County, Garfield County, Iron County, Kane County, Uintah County, and Washington County) case data was missing due to data collection issues. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 4, 2022: Due to a reporting cadence change, case rates for all Alabama counties will be lower than expected. As a result, the CCL levels published on August 4, 2022 should be interpreted with caution.

August 11, 2022: COVID-19 Community Level (CCL) data for the week of August 4, 2022 for South Carolina have been updated to correct a data collection error that resulted in incorrect case data. CDC and its partners have resolved the issue and the correction is reflected in this update.

August 18, 2022: COVID-19 Community Level (CCL) data for the week of August 11, 2022 for Connecticut have been updated to correct a data ingestion error that inflated the CT case rates. CDC, in collaboration with CT, has resolved the issue and the correction is reflected in this update.

August 25, 2022: A laboratory in Tennessee reported a backlog of historic COVID-19 cases. As a result, the 7-day case count and rate may be inflated in many counties and the CCLs published on August 25, 2022 should be interpreted with caution.

August 25, 2022: Due to a data source error, the 7-day case rate for St. Louis County, Missouri, is reported as zero in the COVID-19 Community Level data released on August 25, 2022. Therefore, the COVID-19 Community Level for this county should be interpreted with caution.

September 1, 2022: Due to a reporting issue, case rates for all Nebraska counties will include 6 days of data instead of 7 days in the COVID-19 Community Level (CCL) data released on September 1, 2022. Therefore, the CCLs for all Nebraska counties should be interpreted with caution.

September 8, 2022: Due to a data processing error, the case rate for Philadelphia County, Pennsylvania,

Roads, railways and utility easements are integral components of human society, allowing for the safe and efficient transport of people and goods. There are few places on earth that are not currently traversed or impacted by the vast networks of linear infrastructure. The ecological impacts of linear infrastructure and vehicles are numerous, diverse and, in most cases, deleterious. Recognition and amelioration of these impacts is becoming widespread around the world, and new roads and other linear infrastructure are increasingly planned to avoid high-quality areas and designed to minimise or mitigate the deleterious effects. Importantly, the negative effects of the existing infrastructure are also being reduced during routine maintenance and upgrade projects, as well as targeted retrofits to fix specific problem areas. (1) Global road length, number of vehicles and rate of per capita travel are high and predicted to increase significantly over the next few decades.(2) The ‘road-effect zone’ is a useful conceptual framework to quantify the negative ecological and environmental impacts of roads and traffic.(3) The effects of roads and traffic on wildlife are numerous, varied and typically deleterious. (4) The density and configuration of road networks are important considerations in road planning. (5) The costs to society of wildlife-vehicle collisions can be high. (6) The strategies of avoidance, minimisation, mitigation and offsetting are increasingly being adopted around the world – but it must be recognised that some impacts are unavoidable and unmitigable. (7) Road ecology is an applied science which underpins the quantification and mitigation of road impacts. The global rates of road construction and private vehicle ownership as well as travel demand will continue to rise for the foreseeable future, including at a rapid rate in many developing countries. The challenge currently facing society is to build a more efficient transportation system that facilitates economic growth and development, reduces environmental impacts and protects biodiversity and ecosystem functions. The legacy of the decisions we make today and the roads and railways we construct tomorrow will be with us for many years to come.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Time Series Databases Software Market Outlook

The global time series databases software market is experiencing significant expansion, with market size estimated at approximately USD 1.5 billion in 2023 and projected to reach USD 4.2 billion by 2032, registering a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is driven by the increasing need for real-time analytics and the management of time-stamped data across various industry verticals. The proliferation of IoT devices and the growing importance of time-stamped data in decision-making processes are key factors contributing to this upward trajectory. As businesses seek to leverage these capabilities, the demand for efficient time series databases continues to rise.

One of the major growth factors driving the time series databases software market is the burgeoning IoT ecosystem. With millions of devices generating vast amounts of data every second, there is an unprecedented demand for systems that can efficiently process, store, and analyze time-stamped data. IoT applications, such as smart cities, connected vehicles, and industrial automation, rely heavily on real-time data insights to optimize operations and improve outcomes. Consequently, organizations are investing in advanced time series databases to harness the potential of IoT-driven data streams effectively. This trend is expected to accelerate as IoT adoption continues to grow across various sectors.

Another pivotal growth factor is the increasing emphasis on predictive analytics and machine learning across industries. Time series databases play a crucial role in these areas by enabling businesses to analyze historical data patterns and predict future trends. In sectors like finance, healthcare, and energy, the ability to forecast future events accurately can lead to improved decision-making and strategic planning. For instance, financial institutions utilize time series databases for stock market analysis, while healthcare providers use them for patient monitoring and prognosis. This growing reliance on predictive analytics is expected to fuel the demand for time series database solutions in the coming years.

The need for high-performance and scalable data architectures is also contributing to market growth. Traditional relational databases are often ill-equipped to handle the unique challenges posed by time-stamped data, such as high write and query loads and the need for efficient compression and data retention strategies. Time series databases are specifically designed to address these challenges, offering features such as efficient storage, fast retrieval, and seamless integration with analytics tools. As organizations grapple with increasingly large datasets, the adoption of time series databases is anticipated to rise, driven by the demand for scalable and cost-effective solutions.

Regionally, North America holds a significant share of the time series databases software market, driven by the presence of numerous tech-savvy industries and a strong focus on digital transformation. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, the expansion of smart city initiatives, and increasing investments in IoT infrastructure. Europe also presents substantial growth prospects due to the growing adoption of advanced analytics solutions across various sectors. Meanwhile, Latin America and the Middle East & Africa are gradually embracing these technologies, albeit at a slower pace, as infrastructure and digital initiatives continue to develop. Each region's growth trajectory is influenced by local economic conditions, technology adoption rates, and regulatory frameworks.

Deployment Type Analysis

The analysis of deployment types in the time series databases software market reveals a dynamic landscape shaped by varying organizational needs and technological preferences. On-premises deployment remains a viable option for many businesses, particularly those in regulated industries where data security and control are paramount. Organizations in sectors such as finance and healthcare often prefer on-premises solutions to maintain stringent control over their data environments. These deployments offer the advantage of complete data custody and the flexibility to tailor configurations to specific organizational requirements. However, these benefits come with the trade-offs of higher upfront costs and the need for in-house technical expertise to manage and maintain the infrastructure effectively.

On the other hand, the cloud-based deployment model is witnessing

About the dataset

This dataset uses information from the DWP benefit system to provide estimates of children living in poverty for wards in London. In order to be counted in this dataset, a family must have claimed Child Benefit and at least one other household benefit (Universal Credit, tax credits or Housing Benefit) during the year. The numbers are calibrated to the Households Below Average Income (HBAI) dataset used to provide the government's headline poverty statistics. The definition of relative low income is living in a household with equivalised* income before housing costs (BHC) below 60% of contemporary national median income. The income measure includes contributions from earnings, state support and pensions.

Further detail on the estimates of dependent children living in relative low income, including alternative geographical breakdowns and additional variables, such as age of children, family type and work status are available from DWP's statistical tabulation tool Stat-Xplore. Minor adjustments to the data have been applied to guard against the identification of individual claimants.

This dataset replaced the DWP children in out-of-work benefit households and HMRC children in low income families local measure releases.

This dataset includes estimates for all wards in London of numbers of dependent children living in relative low income families for each financial year from 2014/15 to the latest available (2022/23). The figures for the latest year are provisional and are subject to minor revision when the next dataset is released by DWP.

Headlines

Number of children

The number of dependent children living in relative low income across London, rose from below 310,000 in the financial year ending 2015 to over 420,000 in the financial year ending 2020, but has decreased since then to below 350,000, which is well below the number for financial year ending 2018. While many wards in London have followed a similar pattern, the numbers of children in low income families in some wards have fallen more sharply, while the numbers in other wards have continued to grow.

Proportion of children in each London ward

Ward population sizes vary across London, the age profile of that population also varies and both the size and make-up of the population can change over time, so in order to make more meaningful comparisons between wards or over time, DWP have also published rates, though see note below regarding caution when using these figures.

A dependent child is anyone aged under 16; or aged 16 to 19 in full-time non-advanced education or in unwaged government training. Ward level estimates for the total number of dependent children are not available, so percentages cannot be derived. Ward level estimates for the percentage of children under 16 living in low income families are usually published by DWP but, in its latest release, ward-level population estimates were not available at the time, so no rates were published. To derive the rates in this dataset, the GLA has used the ONS's latest ward-level population estimates (official statistics in development). Percentages for 2021/22 are calculated using the 2021 mid year estimates, while percentages for 2022/23 are calculated using the 2022 mid year estimates. As these are official statistics in development, rates therefore need to be treated with some caution.

Notes

*equivalised income is adjusted for household size and composition in order to compare living standards between households of different types.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Cloud-Based Sensory Substitution Dataset Bank Market Outlook

According to our latest research, the global market size for the Cloud-Based Sensory Substitution Dataset Bank Market reached USD 412.7 million in 2024, with a robust CAGR of 18.5% projected during the forecast period. By 2033, the market is anticipated to reach USD 1,964.2 million, reflecting the accelerating adoption of cloud-based solutions and the growing investment in sensory substitution technologies worldwide. The primary growth factor for this market is the increasing demand for advanced assistive technologies and the rapid evolution of cloud infrastructure, enabling global accessibility and scalability for sensory substitution datasets.

The growth of the Cloud-Based Sensory Substitution Dataset Bank Market is driven by several critical factors, foremost among them being the escalating prevalence of sensory impairments across the globe. With an aging population and rising incidences of vision and hearing loss, there is a pressing need for innovative assistive technologies that can bridge sensory gaps. Cloud-based dataset banks enable researchers and developers to access, train, and validate sensory substitution algorithms more efficiently, fostering rapid advancements in healthcare and assistive technology. The integration of artificial intelligence and machine learning with these datasets is further accelerating the development of more intuitive and effective sensory substitution devices, creating new opportunities for both established players and emerging startups in the market.

Another significant growth driver is the proliferation of cloud computing and the increasing digitalization of healthcare and research infrastructures. Cloud-based platforms offer unparalleled scalability, flexibility, and cost-effectiveness, allowing organizations to store, process, and share large volumes of sensory substitution data securely and efficiently. This has led to a surge in collaborative research initiatives, with academic institutions, hospitals, and technology companies leveraging shared datasets to drive innovation. The adoption of cloud-based sensory substitution dataset banks is also being propelled by favorable government policies and funding initiatives aimed at fostering accessibility and inclusivity for individuals with sensory disabilities, further stimulating market expansion.

The market is also benefiting from the growing emphasis on personalized medicine and user-centric assistive technologies. As the demand for customized sensory substitution solutions rises, cloud-based dataset banks are playing a pivotal role by providing diverse, high-quality data that supports the development of tailored devices and applications. This trend is particularly pronounced in the healthcare and education sectors, where sensory substitution technologies are being integrated into therapeutic interventions and learning environments. Additionally, the increasing awareness and acceptance of sensory substitution devices among end-users, coupled with ongoing advancements in hardware and software components, are contributing to sustained market growth.

From a regional perspective, North America currently leads the Cloud-Based Sensory Substitution Dataset Bank Market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of leading technology companies, robust healthcare infrastructure, and significant investments in research and development. Europe is witnessing substantial growth due to strong government support and a thriving academic research ecosystem, while Asia Pacific is emerging as a lucrative market driven by rapid digitalization, rising healthcare expenditure, and increasing awareness of sensory substitution technologies. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a slower pace, as infrastructure development and adoption rates continue to improve.

Component Analysis

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Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Jun 2025 about savings, personal, rate, and USA.

Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

Stock Price Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

According to Cognitive Market Research, the global non-native database management systems market size will be USD 2215.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 13.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 886.20 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 664.65 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 509.57 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 110.78 million in 2024 and will grow at a compound annual growth rate (CAGR) of 12.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 44.31 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2031.
The cloud-based management systems category is the fastest growing segment of the non-native database management systems industry

Market Dynamics of Non-Native Database Management Systems Market

Key Drivers for Non-Native Database Management Systems Market

Advancements in Artificial Intelligence and Machine Learning Technologies Fuels Market Growth

Advancements in artificial intelligence (AI) and machine learning (ML) technologies are significantly fueling the growth of the non-native database management systems market. These technologies enhance database functionality by enabling advanced data analytics, predictive modeling, and automated decision-making. AI-driven databases can self-optimize, streamline query processing, and ensure seamless data management, leading to improved operational efficiency. Machine learning algorithms facilitate the analysis of large and complex datasets, providing actionable insights for businesses. Additionally, these advancements support the integration of unstructured and semi-structured data, addressing diverse enterprise needs. As organizations increasingly adopt AI and ML to gain competitive advantages, the demand for innovative and intelligent non-native database management systems continues to rise, driving market expansion. For instance, in August 2024, The National Institute of Technology Calicut (NITC) made a significant advancement in data management with the launch of its web portal, 'Nivahika.' During a Senate Hall ceremony, the portal was praised for ensuring accurate and consistent data handling, particularly for ministry reports and national rankings, with the potential to set a benchmark for other NITs.

Expanding E-Commerce and Digital Banking Ecosystems Is Propelling Market Growth

The expanding e-commerce and digital banking ecosystems are significantly propelling the growth of the non-native database management systems market. As online platforms and financial services continue to evolve, they generate vast volumes of data, requiring efficient storage, processing, and analysis. Non-native database management systems offer the scalability and flexibility needed to manage complex and diverse data structures. The rise in digital payment solutions, online shopping, and mobile banking has amplified the demand for robust database systems to ensure seamless operations and enhanced user experiences. Additionally, the focus on real-time data insights, fraud detection, and personalized services further accelerates adoption. Consequently, the non-native database management systems market is experiencing robust growth fueled by these dynamic ecosystems.

Restraint Factor for the Non-Native Database Management Systems Market

High Costs of Maintenance and Regular Updates for Non-Native Systems Limits Market Growth

The non-native database management systems market faces challenges due to the high costs of maintenance and regular updates. Organizations often require specialized expertise to manage these systems effectively, which increases operational expenses. Additionally, frequent updates to keep up with evolving technologies and security requirements further add to the financial burden. These costs can be particularly prohibitive for small and medium-sized enterprises (S...

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Enterprise Database Market Outlook

The enterprise database market size is projected to see significant growth over the coming years, with a valuation of USD 91.5 billion in 2023, and is expected to reach USD 171.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This growth is driven by the increasing demand for efficient data management solutions across various industries and the rise in digital transformation initiatives that require robust database systems. The growth factors include advancements in cloud computing, the growing need for real-time data analytics, and the integration of artificial intelligence and machine learning in data management.

One of the primary growth factors in the enterprise database market is the increasing adoption of cloud-based solutions. Organizations are rapidly moving towards cloud environments due to their scalability, cost-effectiveness, and flexibility. Cloud databases offer better accessibility and reduced infrastructure costs, making them an attractive option for businesses of all sizes. Additionally, with the proliferation of data generated from various sources such as social media, IoT devices, and online transactions, the need for scalable and efficient data storage solutions is more critical than ever. Cloud-based databases provide the requisite infrastructure to handle this data surge efficiently, further propelling market growth.

Another significant driver for the enterprise database market is the rise of big data analytics. As businesses strive to harness the power of data for insights and decision-making, the demand for robust database systems capable of handling large volumes of data has intensified. Enterprises are looking for databases that not only store data but also enable advanced analytics to derive actionable insights. This trend is particularly prevalent in industries like retail, healthcare, and BFSI, where data-driven decisions can lead to improved customer experiences, better risk management, and optimized operations. The integration of artificial intelligence and machine learning with enterprise databases is further enhancing their capabilities, allowing for predictive analytics and automating data processing tasks.

The growing emphasis on data security and compliance is also contributing to the expansion of the enterprise database market. With the increasing incidences of data breaches and stringent regulatory requirements, organizations are prioritizing secure database solutions that offer robust data protection measures. Databases with built-in security features such as encryption, access control, and regular auditing are in high demand. Furthermore, industry-specific compliance standards like GDPR in Europe and HIPAA in the US are driving businesses to invest in databases that ensure compliance and mitigate the risk of penalties, thus fueling market growth.

Regionally, North America is expected to dominate the enterprise database market due to the presence of major technology companies and early adoption of advanced technologies. The Asia Pacific region, however, is anticipated to witness the fastest growth rate during the forecast period, driven by rapid industrialization, the proliferation of SMEs, and increasing investments in digital infrastructure by countries like China, India, and Japan. The growing focus on smart cities and digital transformation initiatives in these countries is further boosting the demand for enterprise databases. Europe also holds a significant share of the market, with widespread adoption of cloud technologies and heightened focus on data privacy and security driving market expansion.

Industrial Databases play a crucial role in the enterprise database market, particularly as industries undergo digital transformation. These databases are designed to manage and process large volumes of industrial data generated from various sources such as manufacturing processes, supply chain operations, and IoT devices. The ability to handle real-time data analytics and provide actionable insights is essential for industries aiming to optimize operations and enhance productivity. As industries continue to adopt smart manufacturing practices, the demand for industrial databases that offer scalability, reliability, and integration with advanced technologies like AI and machine learning is on the rise. This trend is expected to contribute significantly to the growth of the enterprise database market, as businesses seek to leverage data for competitive advantage and operational efficiency.

In-memory OLAP Database Market Outlook

The global in-memory OLAP database market size is projected to witness significant growth, rising from approximately USD 2.3 billion in 2023 to an estimated USD 6.7 billion by 2032, at a compound annual growth rate (CAGR) of 12.7% during the forecast period. This remarkable growth can be attributed to the increasing demand for real-time data analytics and the rapid adoption of cloud services, which are revolutionizing data storage and processing methods. The in-memory OLAP databases, known for their ability to offer fast query responses and real-time insights, are crucial for organizations aiming to leverage big data and analytics to gain a competitive edge.

One of the primary growth factors driving the in-memory OLAP database market is the escalating demand for real-time analytics. In today's fast-paced business environment, organizations are increasingly relying on data-driven decision-making processes to maintain competitiveness. The ability to analyze data in real-time allows businesses to react swiftly to market changes, capture new opportunities, and mitigate risks effectively. In-memory OLAP databases enable this capability by storing data in RAM, which significantly reduces the latency associated with data retrieval and processing. As businesses continue to prioritize speed and agility, the adoption of in-memory OLAP solutions is expected to rise substantially.

Another key factor contributing to the market growth is the rising popularity of cloud-based deployment models. Cloud computing has transformed how organizations approach IT infrastructure, offering scalability, flexibility, and cost-efficiency. In-memory OLAP databases deployed on the cloud provide the added advantage of seamless integration with existing data ecosystems and the capability to handle large data volumes without the need for extensive hardware investments. This shift towards cloud-based solutions is particularly pronounced among small and medium enterprises (SMEs) that seek to leverage advanced analytics without incurring the high costs associated with on-premises infrastructure.

The increasing complexity of data and the diversification of data sources also contribute to the market expansion. Enterprises are now dealing with vast amounts of data generated from various sources, including social media, IoT devices, and enterprise applications. The use of in-memory OLAP databases facilitates the efficient analysis of this diverse data, providing businesses with multidimensional insights that are crucial for strategic decision-making. Additionally, as industries such as healthcare, manufacturing, and telecommunications continue to embrace digital transformation, the demand for sophisticated data management and analytics solutions is expected to propel the in-memory OLAP database market further.

Regionally, North America continues to lead the market owing to its advanced IT infrastructure and high adoption rates of innovative technologies. Companies in the region are early adopters of in-memory OLAP solutions due to their need for high-speed data processing and competitive market dynamics. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period due to the increasing digitalization initiatives, burgeoning economies, and expanding IT sectors in countries like China, India, and Japan. These regions are investing heavily in infrastructure development and technological advancements, making them lucrative markets for in-memory OLAP database vendors.

The concept of a Relational In Memory Database plays a pivotal role in enhancing the performance of in-memory OLAP databases. By storing data in RAM, these databases ensure that data retrieval and processing are executed at lightning speed, which is crucial for real-time analytics. The relational aspect allows for structured data storage and efficient querying, which is essential for businesses that rely on complex data relationships and need to perform intricate data analyses swiftly. As organizations increasingly prioritize speed and efficiency in data processing, the adoption of relational in-memory databases is expected to rise, providing a robust backbone for OLAP solutions.

Component Analysis

In the in-memory OLAP database market, components are primarily divided into software and services. The software segment holds a significant share and is expected to maintain its dominance throughout the forecast period. The continuo

Enterprise Database Software Market Outlook

The global enterprise database software market size is expected to grow from USD 86.5 billion in 2023 to USD 145.8 billion by 2032, at a compound annual growth rate (CAGR) of 5.8% during the forecast period. The escalating demand for data-driven decision-making and advanced data analytics is a key growth factor for this market. Organizations are increasingly leveraging enterprise database software to streamline operations, ensure data integrity, and gain competitive advantages through predictive analytics and business intelligence.

One of the primary growth drivers for the enterprise database software market is the exponential growth in data generation across various industries. With the advent of the Internet of Things (IoT), social media, and cloud computing, data is being produced at an unprecedented rate. Enterprises are seeking robust and scalable database solutions to manage this influx of data efficiently. Additionally, the increasing importance of data compliance and security regulations, such as GDPR and CCPA, is pushing organizations to adopt advanced database management systems that offer enhanced data governance and protection features.

Another significant growth factor is the proliferation of cloud computing and the shift towards cloud-based database solutions. Cloud databases offer numerous benefits, including reduced total cost of ownership, high scalability, flexibility, and ease of use. As businesses continue to embrace digital transformation strategies, the demand for cloud-based database solutions is expected to soar. The integration of artificial intelligence and machine learning capabilities within these databases is further driving their adoption, enabling organizations to extract actionable insights from their data more efficiently and accurately.

The rise of big data analytics and the need for real-time data processing is also fueling the demand for enterprise database software. Organizations are increasingly relying on big data analytics to uncover hidden patterns, correlations, and trends within their data. This requires robust database solutions that can handle large volumes of data and support complex queries in real-time. The advent of in-memory database technology and advancements in database architectures, such as NoSQL and NewSQL, are addressing these requirements, driving the growth of the enterprise database software market.

Regionally, North America holds a significant share of the enterprise database software market, attributed to the presence of major technology players and early adoption of advanced database solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid digitization of economies, increasing investment in IT infrastructure, and the growing emphasis on data-driven decision-making are contributing to this growth. Countries like China and India are emerging as key markets for enterprise database software, driven by the expanding industrial base and the proliferation of small and medium enterprises.

Deployment Type Analysis

In the deployment type segment, the enterprise database software market is categorized into on-premises and cloud-based solutions. On-premises deployment refers to database solutions installed and operated within an organization's own data centers. This traditional deployment model offers higher control over data and security, making it a preferred choice for industries with stringent compliance requirements, such as BFSI and healthcare. However, this model also involves significant upfront costs for hardware, software, and maintenance, which can be a barrier for small and medium enterprises.

The cloud-based deployment model, on the other hand, is witnessing rapid adoption due to its numerous advantages. Cloud databases eliminate the need for substantial capital investment in infrastructure, as they are hosted on the service provider's servers. This model offers high scalability, allowing organizations to scale their database resources up or down based on demand. Additionally, cloud databases facilitate remote access, enabling employees to access data from anywhere, thus supporting the growing trend of remote work. The pay-as-you-go pricing model of cloud databases also makes them an attractive option for small and medium enterprises looking to optimize their IT budgets.

The integration of advanced technologies, such as artificial intelligence and machine learning, within cloud databases is further propelling their adoption. These techn