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Global Navigation Satellite System (GNSS) real-time 1 to multi-second sampled data available from the Crustal Dynamics Data Information System (CDDIS). Global Navigation Satellite System (GNSS) provide autonomous geo-spatial positioning with global coverage. GNSS real-time data sets from ground receivers at the CDDIS consist primarily of the data from the U.S. Global Positioning System (GPS) and the Russian GLObal NAvigation Satellite System (GLONASS). Other GNSS (Europe’s Galileo, China’s Beidou, Japan’s Quasi-Zenith Satellite System/QZSS, the Indian Regional Navigation Satellite System/IRNSS, and worldwide Satellite Based Augmentation Systems/SBASs) are similar to the U.S. GPS in terms of the satellite constellation, orbits, and signal structure; CDDIS began streaming real-time data from these systems in 2015. The real-time observation data from a global permanent network of ground-based receivers are transmitted from the CDDIS in 1 to multi-second intervals in raw receiver or RTCM (Radio Technical Commission for Maritime Services) format.

Iowa Department of Transportation's Intelligent Transportation System (ITS) Detector Sensors. Sensor Feed: Includes location of sensors, current travel speed, traffic counts, occupancy counts, and more.Work Zone Alert Feed: Includes work zones that have dropped below the normal speed and are determined to have a critical traffic speed abnormality.

The Near Real-time Data Access (NeRDA) Portal is making near real-time data available to our stakeholders and interested parties. We're helping the transition to a smart, flexible system that connects large-scale energy generation right down to the solar panels and electric vehicles installed in homes, businesses and communities right across the country. In line with our Open Networks approach, our Near Real-time Data Access (NeRDA) portal is live and making available power flow information from our EHV, HV, and LV networks, taking in data from a number of sources, including SCADA PowerOn, our installed low voltage monitoring equipment, load model forecasting tool, connectivity model, and our Long-Term Development Statement (LTDS). Making near real-time data accessible from DNOs is facilitating an economic and efficient development and operation in the transition to a low carbon economy. NeRDA is a key enabler for the delivery of Net Zero - by opening network data, it is creating opportunities for the flexible markets, helping to identify the best locations to invest flexible resources, and connect faster. You can access this information via our informative near real-time Dashboard and download portions of data or connect to our API and receive an ongoing stream of near real-time data.

The Real-Time Streaming Processing Platform market is experiencing robust growth, projected to reach $432.2 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 17.8% from 2025 to 2033. This expansion is fueled by the increasing need for immediate insights from large volumes of streaming data across diverse sectors. The rise of IoT devices, the proliferation of big data, and the urgent demand for real-time decision-making in applications like fraud detection, personalized marketing, and predictive maintenance are key drivers. The market is segmented by service type (fully-managed and self-managed) and application (financial services, healthcare, manufacturing, communications, retail, and public sector), offering various solutions to cater to specific business needs. Growth in cloud-based solutions and the adoption of advanced analytics technologies are significant trends shaping the market landscape. However, challenges remain, including data security concerns, the complexity of implementation, and the need for skilled professionals to manage and interpret real-time data streams. The competitive landscape is dynamic, with major players like Google, Microsoft, Amazon Web Services (AWS), and others vying for market share through innovation and strategic partnerships. The North American market currently holds a significant portion of the global market share due to early adoption of cloud technologies and advanced analytics. However, regions like Asia-Pacific are poised for rapid growth, driven by increasing digitalization and government initiatives promoting technological advancement. The competitive landscape is characterized by both established technology giants and innovative startups, leading to continuous product development and the introduction of new features such as enhanced scalability, improved security, and streamlined integration with existing data infrastructure. The market's future growth trajectory hinges on further advancements in artificial intelligence (AI), machine learning (ML), and edge computing, which are expected to further enhance the capabilities and applications of real-time streaming processing platforms.

According to the 2024 Global Streaming Study, over half of respondents globally preferred live programming, such as sporting events or concerts, to be available on video streaming services. Consumers in China, in particular, would like to watch this kind of content on streaming platforms, while only 35 percent of Dutch respondents showed in interest in live-streaming content.

Learn how defense and intelligence users can leverage ArcGIS GeoEvent Server and ArcGIS GeoAnalytics Server to connect to real-time data feeds and run analytics on the stored data. From tracking units in the field to analyzing intelligence feeds and weather, ArcGIS GeoEvent Server enables users to stay current on what is happening. When you want to analyze massive amounts of stored track and report data, ArcGIS GeoAnalytics Server uses distributed computing to return spatiotemporal insight helping you make better planning decisions.

The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested:

GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided.

GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present.

Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information.
polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation.
patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs)
Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites.
gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground.
gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties.
Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification.
grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides).

Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016

The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _\(Terms of Applicability\) https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 .

The Streaming Data Processing System Software market is experiencing robust growth, projected to reach a market size of $19.61 billion in 2025. While the exact Compound Annual Growth Rate (CAGR) isn't provided, considering the rapid advancements in data analytics, cloud computing, and the increasing volume of real-time data generated across various sectors, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 15%. This signifies substantial market expansion driven by the critical need for businesses to process and analyze streaming data for informed decision-making. Key market drivers include the rise of IoT devices, the demand for real-time insights in diverse industries (financial services, healthcare, manufacturing), and the increasing adoption of cloud-based solutions for scalability and cost-effectiveness. Trends such as edge computing, AI/ML integration within streaming data platforms, and the growing focus on data security are further fueling market growth. Despite these positive factors, challenges such as the complexity of implementation, the need for specialized skills, and data privacy concerns represent potential restraints. The market segmentation reveals a strong preference for cloud-based solutions over on-premises deployments, reflecting the benefits of scalability, agility, and reduced infrastructure costs. The financial services, healthcare, and manufacturing sectors represent significant market segments, driven by their reliance on real-time data analysis for fraud detection, patient monitoring, and supply chain optimization respectively. The competitive landscape is highly dynamic, with established players like Google, Microsoft, and AWS alongside specialized vendors and emerging companies constantly innovating to meet evolving market demands. Geographical distribution shows North America and Europe as leading markets, while Asia-Pacific is expected to demonstrate significant growth potential in the coming years, driven by the rapid digitalization of economies in countries like China and India. The overall market outlook remains positive, with continued growth anticipated throughout the forecast period, driven by technological advancements and the ever-increasing reliance on real-time data analysis across diverse industries.

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Streaming Analytics Market Size 2024-2028

The streaming analytics market size is forecast to increase by USD 39.7 at a CAGR of 34.63% between 2023 and 2028.

The market is experiencing significant growth due to the increasing need to improve business efficiency in various industries. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies is a key trend driving market growth. These technologies enable real-time data processing and analysis, leading to faster decision-making and improved operational performance. However, the integration of streaming analytics solutions with legacy systems poses a challenge. IoT platforms play a crucial role In the market, as IoT-driven devices generate vast amounts of data that require real-time analysis. Predictive analytics is another area of focus, as it allows businesses to anticipate future trends and customer behavior, leading to proactive decision-making.Overall, the market is expected to continue growing, driven by the need for real-time data processing and analysis in various sectors.

What will be the Size of the Streaming Analytics Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for real-time insights from big data generated by emerging technologies such as IoT and API-driven applications. This market is driven by the strategic shift towards digitization and cloud solutions among large enterprises and small to medium-sized businesses (SMEs) across various industries, including retail. Legacy systems are being replaced with modern streaming analytics platforms to enhance data connectivity and improve production and demand response. The financial impact of real-time analytics is substantial, with applications in fraud detection, predictive maintenance, and operational efficiency. The integration of artificial intelligence (AI) and machine learning algorithms further enhances the market's potential, enabling businesses to gain valuable insights from their data streams.Overall, the market is poised for continued expansion as more organizations recognize the value of real-time data processing and analysis.

How is this Streaming Analytics Industry segmented and which is the largest segment?

The streaming analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn premisesTypeSoftwareServicesGeographyNorth AmericaCanadaUSAPACChinaJapanEuropeUKMiddle East and AfricaSouth America

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

Cloud-deployed streaming analytics solutions enable businesses to analyze data in real time using remote computing resources, such as the cloud. This deployment model streamlines business intelligence processes by collecting, integrating, and presenting derived insights instantaneously, enhancing decision-making efficiency. The cloud segment's growth is driven by benefits like quick deployment, flexibility, scalability, and real-time data visibility. Service providers offer these capabilities with flexible payment structures, including pay-as-you-go. Advanced solutions integrate AI, API, and event-streaming analytics capabilities, ensuring compliance with regulations, optimizing business processes, and providing valuable data accessibility. Cloud adoption in various sectors, including finance, healthcare, retail, and telecom, is increasing due to the need for real-time predictive modeling and fraud detection.SMEs and startups also benefit from these solutions due to their ease of use and cost-effectiveness. In conclusion, cloud-based streaming analytics solutions offer significant advantages, making them an essential tool for organizations seeking to digitize and modernize their IT infrastructure.

Get a glance at the Streaming Analytics Industry report of share of various segments Request Free Sample

The Cloud segment was valued at USD 4.40 in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 34% to the growth of the global market during the forecast period.

Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

In North America, the region's early adoption of advanced technology and high data generation make it a significant market for streaming analytics. The vast amounts of data produced in this tech-mature region necessitate intelligent analysis to uncover valuable relationships and insights. Advanced software solutions, including AI, virtualiza

During the period studied, the most watched live stream was the Just Chatting/ IRL streams in Poland.

Streaming Analytics Market Valuation – 2024-2031

Streaming Analytics Market size was valued at USD 30.12 Billion in 2024 and is projected to reach USD 252.12 Billion by 2031, growing at a CAGR of 33.56 % from 2024 to 2031

Streaming Analytics Market Drivers

Real-time Insights: Businesses are increasingly demanding real-time insights from their data streams to make swift, data-driven decisions.

Internet of Things (IoT): The proliferation of IoT devices generates massive volumes of data in real-time, requiring efficient processing and analysis.

Cloud Computing: Cloud-based streaming analytics platforms offer scalable and cost-effective solutions, eliminating the need for significant upfront investments.

Streaming Analytics Market Restraints

Data Quality and Security: Ensuring data quality and security is a significant challenge in streaming analytics, especially with increasing data privacy regulations and cybersecurity threats.

Complex Implementation: Implementing streaming analytics solutions can be complex, requiring specialized skills and expertise. Integrating these solutions with existing systems and data pipelines can also be challenging.

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Global Live Streaming Platform market size 2025 was XX Million. Live Streaming Platform Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

We propose a novel technology to leverage rapidly evolving cloud based infrastructure to improve time constrained situational awareness for real-time decision making. Our "CloudTurbine" innovation eliminates the distinction between files and streams to distribute live streaming sensor and video data over cloud file sharing services.
Streaming and static data have long been considered separately, with unique mechanisms for data transmittal and viewing of each. Files are the greatest common denominator linking static data across all computers. However, real-time streaming data distribution is widely presumed to be sensor-centric; i.e. up-front requirements to "keep up" with live data trump all other considerations.
A great unification of cloud based services for static data has recently occurred. There are now many providers of "file sharing" cloud based services. The paradigm for all is simple: (1) put data in a local file folder, (2) it automatically shows up at other linked systems via a cloud service. Wouldn't it be nice if one could unify an approach to streaming data that leveraged this file-sharing cloud infrastructure? That is precisely what we propose. Building upon a functional prototype, we propose to characterize, evaluate, refine and adapt CloudTurbine technology to NASA and commercial applications. CloudTurbine is a streaming data interface to and from standard file sharing cloud services. It delegates much of the data transmittal, security, and server resources to the cloud service provider. It provides robust continuous streaming for high data and frame rates while trading off manageable amounts of delivery latency (on the order of seconds). In so doing, it eliminates the distinction between files and streams, and enables a simple, cost effective new paradigm for streaming data middleware.

Information on the amount of water flowing in streams and rivers is critical to the management of water resources, emergency response to flooding, fisheries management, and many other uses. This layer provides access to near real-time stream gauge readings compiled from a variety of agencies and organizations.Dataset SummaryThe Live Stream Gauges layer contains near real-time measurements of water depth from multiple reporting agencies recording at sensors across the world. This layer updates every hour. Flow forecasts are provided where available. These sensor feeds are owned and maintained by the GIS community via the Community Maps Program. For details on the coverage in this map and to find out how to contribute your organization's gauges, please email environment@esri.com.Contributors to the Live Stream Gauges Service:United States Geological Survey (USA)National Weather Service (USA) * Includes Stage Status *Washington State Department of Ecology (USA)San Joaquin County (USA)Maricopa County Flood Control District (USA)Minnesota Department of Natural Resources (USA)PEGELONLINE (Germany) * Includes Stage Status *Bureau of Meteorology (Australia)Horizons Regional Council (New Zealand) Environment Agency (UK)Nebraska Department of Natural Resources (USA) * Includes Stage Status *Iowa Flood Center (USA)Oregon Water Resource Department (USA)Dartmouth Flood Observatory (Global) * Includes Stage Status * Suspended *Meteorological Service of Canada (Canada)Volusia County Florida (USA) * Suspended *Somali Water and Land Information Management (Somalia) * Includes Stage Status *Office of Public Works (Ireland)RevisionsFeb 13, 2024: Dartmouth Flood Observatory, suspended by government cutbacksDec 13, 2024: Added 'Status Classification' field, allowing symbol level draw order based on severity of flood status!Aug 26, 2024: Corrected update issue with USGS source data reported by several users.Aug 14, 2024: Updated USGS feed to pull from JSON data source, see: https://waterservices.usgs.gov/Jul 24, 2024: Added Office of Public Works (Ireland) dataJul 10, 2024: National Weather Service (NOAA) source reinstated after provider fix!Jul 8, 2024: Volusia County Florida, suspended during administrative holdJul 5, 2024: National Weather Service (NOAA) source stopped updating, suspended waiting on provider to correctMay 28, 2024: National Weather Service (NOAA) source updated, replaced retired AHPS with NWPSJan 22, 2024: Reinstated Somali Water and Land Management source after they successfully migrated to HTTPS ProtocolJan 3, 2024: Somali Water and Land Management source deactivated until Web Site issues are resolved!Mar 20, 2023: Nebraska DNR has been updated to leverage new source and now honors Stage Status!Feb 16, 2023: Nebraska DNR source update temporarily disabled due to source repository change!Aug 10, 2021: Added missing source for Nova Scotia CanadaJul 3, 2021: Added Somali Water and Land Information Management dataJun 30, 2021: Added Volusia County dataFeb 9, 2021: Refinements and Fixes:Corrections to Flow conversion for 'Environment Agency - UK'Corrections to Flow conversion for 'Horizons Regional Council - New Zealand'Added display of Metric Stage Height and Flow to PopupJan 27, 2021: Official release of Feature Service offering. Upgrades include:Automatic addition of new source stationsRemoval of stations with data older than 180 daysAddition of 'Governing Location' field that provides geographic State or Province (optional) plus Country NameAddition of 'Hours Since Last Update' field that maintains the age since gauge data was last updated

This dataset contains application and network measurements of 6,982 individual Twitch.tv streaming sessions of 222 different streamers summing up to more than 1,000h live streaming. The data are aggregated to uplink requests.

The global market for Event Stream Processing (ESP) tools is experiencing robust growth, driven by the increasing adoption of real-time data analytics across diverse industries. The proliferation of IoT devices, the need for immediate insights from streaming data, and the rise of cloud-based solutions are key factors fueling this expansion. While precise figures for market size and CAGR are unavailable, industry reports suggest a multi-billion dollar market with a healthy Compound Annual Growth Rate (CAGR) in the range of 15-20% between 2025 and 2033. This growth is fueled by enterprises seeking to leverage real-time data for improved decision-making, enhanced customer experience, and optimized operational efficiency. The market is segmented by deployment (cloud-based and on-premises) and user type (large enterprises and SMEs), with cloud-based solutions demonstrating faster adoption due to scalability and cost-effectiveness. Competitive pressures are high, with established players like IBM, Oracle, and Amazon competing alongside specialized vendors such as Confluent and StreamSets. The increasing complexity of data streams and the need for advanced analytics capabilities are driving innovation in the ESP tools space, with a focus on enhanced security, better integration with other data processing tools, and improved scalability to handle massive volumes of data. Geographic distribution shows strong presence in North America and Europe, driven by early adoption of digital transformation initiatives and a robust IT infrastructure. However, significant growth opportunities exist in the Asia-Pacific region, particularly in countries like China and India, due to rapid digitalization and increasing investments in data infrastructure. Market restraints include the complexity of implementing and managing ESP systems, the need for specialized skills, and the potential for data security and privacy concerns. Despite these challenges, the overall market outlook remains positive, with sustained growth anticipated in the coming years. The evolution towards serverless architectures and the integration of AI/ML capabilities into ESP platforms are expected to further shape market dynamics in the future, driving both innovation and consolidation within the vendor landscape.

The statistic presents data on the share of YouTubers who have streamed live TV in the last month in the United States as of the fourth quarter of 2016, by device. During the survey, 25 percent of respondents stated that they had streamed live TV on a mobile device in the past month.

According to a survey of users in the United States carried out in March 2024, feeling updated and informed, as well as feeling part of an event were the leading reasons among audiences of live streaming content to engage with this type of online video content. Approximately 52 percent of users aged between 35 and 54 years reported watching livestreamed content because it made them feel updated and informed. Feeling more connected to friends, an organization, or a brand, was also an important reason for users aged between 18 and 54 to watch live streaming content.