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.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

With the internet becoming increasingly accessible, the number of e-mails sent and received globally has increased each year since 2017. In 2022, there were an estimated 333 billion e-mails sent and received daily around the world. This figure is projected to increase to 392.5 billion daily e-mails by 2026.

E-Mail marketing Despite the increasing popularity of messengers, chat apps and social media, e-mail has managed to remain central to digital communication and continues to grow in uptake. By 2025, the number of global e-mail users is expected to reach a total of 4.6 billion - an approximate six hundred thousand increase in users, up from 4 billion in 2020. Not only that, when it comes to online advertising e-mail has seen higher click-through-rates than on social media. In Belgium and Germany, these were 5.5 and 4.3 percent respectively - compared to the 1.3 percent global average CTR for social media during the same time period.

Gmail Launched in April 2004, Google’s Gmail has earned its spot as one of the most popular freemail services in the world. According to a 2019 survey, its popularity worldwide was trumped only by Apple’s native iPhone Mail app with 26 percent of all e-mail opens worldwide taking place on the platform. Millennials surveyed in the United Kingdom listed Gmail among their top 5 most important mobile apps, while a similar survey carried out in Sweden saw Gmail tie with WhatsApp for a spot among the top mobile apps nationwide.

The enterprise data integration market is valued at 10.44 Billion in 2021 and it is expected to reach at USD 30.88 Billion by 2030 with CAGR of 12.8%. Market Dynamics For Enterprise Data Integration

Growing number of data generated in the world raises the demand for enterprise data integration. The technologies such as AI, machine learning, Apache Spark, and others are generating large amount of data According to the study, around 44 zettabytes data produced in the 2020. Approximately 2.5 quintillion bytes of data is produced every day. This necessitates the enterprise data integration.

However, lack of expertise can hamper the growth of the market. According to a report by IMF, the tech talent shortage will swell to more than 85 million tech workers by 2030.

Nevertheless, increasing demand for cloud technologies will provide the numerous opportunities for the growth of the market. Cloud computing is become the new pitch for the IT companies. This escalates the demand for enterprise data integration. According to Gartner, the worldwide cloud computing industry will expand by $266.4 billion by 2020, up from $227.4 billion in 2019. Enterprise data integration is the process of the unifying of the data from various sources and consolidated into sensible way. In modern era, the importance of enterprise data integration become important as volume and complexity of data increases. The enterprise data integration provides several benefits such as Faster time to value, increasing productivity, greater visibility, stronger lead generation and others.

This entry covers single-level data and soil-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable). Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

As of the third quarter of 2024, internet users in South Africa spent more than **** hours and ** minutes online per day, ranking first among the regions worldwide. Brazil followed, with roughly **** hours of daily online usage. As of the examined period, Japan registered the lowest number of daily hours spent online, with users in the country spending an average of over **** hours per day using the internet. The data includes the daily time spent online on any device. Social media usage In recent years, social media has become integral to internet users' daily lives, with users spending an average of *** minutes daily on social media activities. In April 2024, global social network penetration reached **** percent, highlighting its widespread adoption. Among the various platforms, YouTube stands out, with over *** billion monthly active users, making it one of the most popular social media platforms. YouTube’s global popularity In 2023, the keyword "YouTube" ranked among the most popular search queries on Google, highlighting the platform's immense popularity. YouTube generated most of its traffic through mobile devices, with about 98 billion visits. This popularity was particularly evident in the United Arab Emirates, where YouTube penetration reached approximately **** percent, the highest in the world.

A. SUMMARY This dataset is used to report on public dataset access and usage within the open data portal. Each row sums the amount of users who access a dataset each day, grouped by access type (API Read, Download, Page View, etc).

B. HOW THE DATASET IS CREATED This dataset is created by joining two internal analytics datasets generated by the SF Open Data Portal. We remove non-public information during the process.

C. UPDATE PROCESS This dataset is scheduled to update every 7 days via ETL.

D. HOW TO USE THIS DATASET This dataset can help you identify stale datasets, highlight the most popular datasets and calculate other metrics around the performance and usage in the open data portal.

Please note a special call-out for two fields: - "derived": This field shows if an asset is an original source (derived = "False") or if it is made from another asset though filtering (derived = "True"). Essentially, if it is derived from another source or not. - "provenance": This field shows if an asset is "official" (created by someone in the city of San Francisco) or "community" (created by a member of the community, not official). All community assets are derived as members of the community cannot add data to the open data portal.

Data Analytics Tools Market Outlook

The global data analytics tools market size was valued at approximately USD 25 billion in 2023 and is projected to reach around USD 92 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% during the forecast period. The rapid expansion of this market is largely attributed to the surging volume of data generation, advancements in artificial intelligence (AI) and machine learning (ML) technologies, and the increasing adoption of data-driven decision-making across various industries.

The growing volume of data generated by digital devices and online activities is a major driver for the data analytics tools market. Every day, businesses and individuals produce an immense amount of data through various channels such as social media, IoT devices, mobile applications, and more. This exponential data growth presents a significant opportunity for organizations to harness insights through data analytics tools, thereby driving demand for advanced analytics solutions. Additionally, the proliferation of cloud computing has made data storage more accessible and scalable, further bolstering the need for sophisticated analytics tools to process and analyze large datasets.

Another critical growth factor is the integration of AI and ML technologies into data analytics tools. These technologies enhance the capabilities of traditional analytics by enabling more accurate predictions, automated data processing, and deeper insights. Organizations are increasingly leveraging AI and ML to gain a competitive edge by uncovering hidden patterns, optimizing operations, and improving customer experiences. The continuous advancements in these technologies are expected to fuel the growth of the data analytics tools market significantly over the forecast period.

Businesses across various industries are rapidly adopting data-driven decision-making practices to stay competitive in a fast-evolving market landscape. Data analytics tools empower organizations to make informed decisions based on actionable insights derived from data. This shift towards data-centric strategies is evident in sectors such as BFSI, healthcare, retail, and manufacturing, where data analytics is used to enhance operational efficiency, personalize customer interactions, and drive innovation. The increasing recognition of data as a valuable asset is a key factor propelling the demand for advanced analytics solutions.

The emergence of Big Data Analytics Software has revolutionized the way organizations handle vast amounts of data. This software enables businesses to efficiently process and analyze large datasets, uncovering valuable insights that drive strategic decision-making. By leveraging advanced algorithms and machine learning capabilities, Big Data Analytics Software helps organizations identify trends, predict future outcomes, and optimize operations. As the volume of data continues to grow exponentially, the demand for robust analytics solutions that can handle complex data structures and deliver real-time insights is on the rise. This trend is particularly evident in industries such as finance, healthcare, and retail, where timely data-driven decisions are crucial for maintaining a competitive edge.

Regionally, North America holds a significant share of the data analytics tools market, driven by the early adoption of advanced technologies, a strong presence of key market players, and substantial investments in research and development. Europe follows closely, with a growing emphasis on digital transformation and data-driven initiatives. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the expanding IT infrastructure, increasing internet penetration, and growing awareness about the benefits of data analytics. Latin America and the Middle East & Africa are also anticipated to show steady growth due to rising technological adoption and supportive government policies.

Component Analysis

The data analytics tools market can be segmented by component into software and services. The software segment dominates the market, driven by the increasing demand for advanced analytics platforms and solutions that enable organizations to process and analyze large volumes of data efficiently. Analytics software includes various products such as business intelligence (BI) tools, data visualization tools, and advanced analytics platforms that cater to different analytical needs of business

Italy: Oil production, thousand barrels per day: The latest value from 2023 is 81.06 thousand Barrels Per Day, a decline from 83.15 thousand Barrels Per Day in 2022. In comparison, the world average is 429.63 thousand Barrels Per Day, based on data from 190 countries. Historically, the average for Italy from 1984 to 2023 is 87.61 thousand Barrels Per Day. The minimum value, 42 thousand Barrels Per Day, was reached in 1984 while the maximum of 114.52 thousand Barrels Per Day was recorded in 2005.

This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A01 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.The 1AVIRS product contains science and housekeeping sensor count data directly from the Visible and Infrared Scanner (VIRS) aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 131 MB. There are 16 files of VIRS 1A data produced per day.The Visible and Infrared Scanner (VIRS) is a five-channel visible/infrared radiometer, which builds on the heritage of the Advanced Very High Resolution Radiometer (AVHRR) instrument flown aboard the NOAA series of Polar-Orbiting Operational Environmental Satellites (POES). The VIRS detects radiation at 1 visible, 2 near infrared and 2 thermal infrared wavelengths, allowing determination of cloud coverage, cloud top height and temperature, and precipitation indices. The central wavelengths for the VIRS channels are 0.63, 1.60,3.75, 10.8, and 12.0 microns. All channels are in operation during the daytime, but only channels 3, 4 and 5 operate during the nighttime.Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation

The USA: Jet fuel production, thousand barrels per day: The latest value from 2014 is 1541 thousand barrels per day, an increase from 1499 thousand barrels per day in 2013. In comparison, the world average is 28.65 thousand barrels per day, based on data from 206 countries. Historically, the average for the USA from 1980 to 2014 is 1397.01 thousand barrels per day. The minimum value, 968 thousand barrels per day, was reached in 1981 while the maximum of 1606.49 thousand barrels per day was recorded in 2000.

This entry covers pressure-level data at the original time resolution (once every 12 hours). Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

The USA: Gasoline production, thousand barrels per day: The latest value from 2014 is 9571 thousand barrels per day, an increase from 9234 thousand barrels per day in 2013. In comparison, the world average is 115.28 thousand barrels per day, based on data from 206 countries. Historically, the average for the USA from 1980 to 2014 is 7681.79 thousand barrels per day. The minimum value, 6338 thousand barrels per day, was reached in 1982 while the maximum of 9571 thousand barrels per day was recorded in 2014.

USGS researchers with the Patterns in the Landscape – Analyses of Cause and Effect (PLACE) project are releasing a collection of high-frequency surface water map composites derived from daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Using Google Earth Engine, the team developed customized image processing steps and adapted the Dynamic Surface Water Extent (DSWE) to generate surface water map composites in California for 2003-2019 at a 250-m pixel resolution. Daily maps were merged to create 6, 3, 2, and 1 composite(s) per month corresponding to approximately 5-day, 10-day, 15-day, and monthly products, respectively. The resulting maps are available as downloadable files for each year. Each file includes 72, 36, 24, or 12 bands that coincide with the number of maps generated in the 5-day, 10-day, 15-day, and monthly products, respectively. The bands are ordered chronologically, with the first band representing the beginning of the calendar year and the last band representing the end of the year. Each set of maps is labeled according to year and product type. There are 17 GeoTIF (.tif) raster data files for each composite product.

This is the new (GPM-formated) TRMM product. It replaces the old TRMM_1A11 Version 07 is the current version of the data set. Previous versions have been superseded by Version 07.The 1ATMI product contains science and housekeeping sensor count data directly from the TRMM Microwave Imager (TMI) Instrument aboard the TRMM satellite. The data has been unpacked from the spacecraft packets and geolocated. A Level 1A file contains data for a single orbit and has a file size of about 33 MB. There are 16 files of TMI 1A data produced per day.Spatial coverage is between 38 degrees North and 38 degrees South owing to the 35 degree inclination of the TRMM satellite. This orbit provides extensive coverage in the tropics and allows each location to be covered at a different local time each day, enabling the analysis of the diurnal cycle of precipitation.

This data collection consists of archived Space Weather Follow On (SWFO) Daily Median Retrospective data from the Compact Coronagraph-1 (CCOR1) on Geostationary Operational Environmental Satellite 19 (GOES-19). The archival process includes daily files. The CCOR-1 instrument generates 96 files per day per level in one tar file that contains FITS. These data are produced by the NCEI for the restrospective products. The instrument observes broadband optical light that is scattered from coronal electrons and heliospheric dust. GOES-19 launched in late April 2024. Other products available are auxiliary files (pkt-l0_g19, orb-pr_g19, and sc-att_g19); CCOR1 files (ccor1-l0a_g19, ccor1-l0b_g19, ccor1-l1a_g19, ccor1-l1b_g19, ccor1-l2_g19, ccor1-l3_g19); and, National Centers for Environmental Data (NCEI) Retrospective Science products (sci_ccor1-l1a_g19, sci_ccor1-l1b_g19, and sci_ccor1-l2_g19, sci_ccor1-l3_g19, and sci_ccor1-mm_g19).

This dataset contains Ocean Color (OC) Science Quality Environmental Data Records (EDR) Level-3 products from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (SNPP) satellite. Level-3 EDR data are produced by NOAA CoastWatch/OceanWatch (CW) from Level-2 products. The Level-2 OC EDR are produced by NESDIS Center for Satellite Applications and Research (STAR) OC team using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system. Science quality OC EDR are produced using the significantly improved VIIRS Sensor Data Records (SDR or Level-1B data), which are generated by the OC team (named OC-SDR) using both the solar and lunar approaches, and assimilated ancillary input data (as opposed to model predicted data used in near-real time data production). MSL12 and the OC-SDR calibration improvements were developed by the STAR OC team [Wang et al., 2013; Sun and Wang, 2015; Wang et al., 2016; Wang et al., 2017]. The Science Quality Level-2 VIIRS OC EDR are produced daily on a delayed mode (present day minus 15 days) with global spatial coverage. In addition to this forward stream processing, a consistent, full-mission dataset was generated with the same processing covering the time period from the first post-launch useable data in January 2012 to present day minus 15 days and also with global spatial coverage. The CW Level-3 processing uses MSL12 version 1.2 Level-2 (the swath or granule data) as input to produce global mapped 4 km spatial resolution, daily, weekly and monthly time binned (averaged) output data product files in NetCDF format. These CW Level-3 products include the following parameters: normalized water-leaving radiances for 6 VIIRS visible bands (i.e., bands M1-M5 at 410 nm, 443 nm; 486 nm; 551 nm; 671 nm respectively and band I1 at 638 nm), chlorophyll-a concentration (Chl-a), the diffuse attenuation coefficient at 490 nm (Kd(490)), and the diffuse attenuation coefficient for photosynthetically available radiation (Kd(PAR)). Note that the MSL12 is the NOAA enterprise processing system for all OC data (from multiple sensors and/or satellite missions) which has replaced the Integrated Data Processing Segment (IDPS) from the Joint Polar Satellite System (JPSS) program for VIIRS data. These Level-3 records are a primary source of information for numerous regional and global marine resource stewardship efforts and are used in applications in support of the NOAA mission and other applications such as fish stock assessments, local habitat characterization, phytoplankton pigment concentration and whale distribution, weather predictions and forecasting, as well as basic physical and biological oceanographic studies of our changing ocean environments. For additional information about OC data, other data formats and a variety of search tools, visit CoastWatch.NOAA.gov.

This dataset contains the version 3.2 CYGNSS level 2 science data record which provides the time-tagged and geolocated average wind speed (m/s) and mean square slope (MSS) with 25x25 kilometer resolution from the Delay Doppler Mapping Instrument aboard the CYGNSS satellite constellation. This version supersedes Version 3.1: https://doi.org/10.5067/CYGNS-L2X31. The reported sample locations are determined by the specular points corresponding to the Delay Doppler Maps (DDMs). A subset of DDM data used in the direct processing of the average wind speed and MSS is co-located inside of the Level 2 data files. Only one netCDF data file is produced each day (each file containing data from up to 8 unique CYGNSS spacecraft) with a latency of approximately 6 days (or better) from the last recorded measurement time. The L2 Geophysical Model Function (GMF) that maps L1 observables to ocean surface wind speed and the Significant Wave Height (SWH) second order correction to the wind speed retrievals were rederived to be consistent with the v3.2 L1 calibration. The method used for deriving the GMF and SWH correction is the same as for v3.1. An additional swell wave correction has been added to better account for the long wave dependence at low wind speeds. The FDS and YSLF retrieval algorithms are otherwise the same as v3.1. The v3.2 L2 YSLF wind speed is now designated as an intermediate product and should not be used ‘as is’. Additional quality control filters have been added to the Level 3 gridded product derived from the L2 YSLF wind speed to detect and remove outlier L2 samples, and use of the L3 product is recommended.The CYGNSS is a NASA Earth System Science Pathfinder Mission that is intended to collect the first frequent space‐based measurements of surface wind speeds in the inner core of tropical cyclones. Made up of a constellation of eight micro-satellites, the observatories provide nearly gap-free Earth coverage using an orbital inclination of approximately 35° from the equator, with a mean (i.e., average) revisit time of seven hours and a median revisit time of three hours. This inclination allows CYGNSS to measure ocean surface winds between approximately 38° N and 38° S latitude. This range includes the critical latitude band for tropical cyclone formation and movement.