The Registry of Open Data on AWS contains publicly available datasets that are available for access from AWS resources. Note that datasets in this registry are available via AWS resources, but they are not provided by AWS; these datasets are owned and maintained by a variety of government organizations, researchers, businesses, and individuals. This dataset contains derived forms of the data in https://github.com/awslabs/open-data-registry that have been transformed for ease of use with machine interfaces. Currently, only the ndjson form of the registry is populated here.

The Data Exchange Tool market is experiencing robust growth, driven by the increasing need for secure and efficient data sharing across organizations. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions, the growing demand for real-time data analytics, and the increasing regulatory pressure around data privacy and compliance. Companies are actively seeking solutions to streamline data exchange processes, improve data quality, and reduce the risks associated with manual data transfer. This trend is particularly pronounced in sectors such as finance, healthcare, and government, where secure and reliable data sharing is critical for operational efficiency and regulatory compliance. The market is characterized by a diverse range of players, encompassing established enterprise software vendors alongside innovative startups offering specialized solutions. While consolidation is a possibility, the market is expected to remain fragmented due to the diverse needs of different industries and data types. We estimate the current market size (2025) to be around $5 billion, projecting a Compound Annual Growth Rate (CAGR) of 18% through 2033, leading to a market value exceeding $20 billion by the end of the forecast period. This growth trajectory is supported by the continued investment in data infrastructure, the proliferation of data-driven business models, and the increasing adoption of advanced data governance frameworks. The competitive landscape is dynamic, with companies like Snowflake, Informatica, and AWS playing significant roles alongside specialized data exchange tool providers. The success of these companies hinges on factors such as ease of integration, security features, scalability, and the ability to support diverse data formats and protocols. Future growth will depend on advancements in areas such as AI-powered data matching, automated data quality checks, and blockchain-based data security solutions. The market will also see further consolidation as larger players acquire smaller firms to expand their product portfolios and market share. Regional variations in adoption rates are expected, with North America and Europe leading the market initially, followed by a gradual expansion into Asia-Pacific and other regions as digital transformation initiatives accelerate globally. Challenges include maintaining data security and privacy, ensuring interoperability between different systems, and addressing the complexity of managing data exchange across geographically dispersed locations.

The Data Exchange Platform Services Marketsize was valued at USD 1.3 billion in 2023 and is projected to reach USD 7.5 billion by 2032, exhibiting a CAGR of 19.15 % during the forecast period. The market of data exchange platform services includes services that are required to provide strict capability of exchanging data between various organizations, systems, and/or applications. They include an integration hub used for accepting, transforming, and routing data in real time according to its source and format. Besides, there are several use cases associated with Data Exchange platforms, including Inter-Organization Data Sharing, Cloud-Application Integration, and From Cloud to On-Prem, Off-Prem Applications Data Transfer. These include financing, medical, sales and industrial production, statistic and analytical, provision of goods and services, as well as meeting clients’ requirements. These trends include the need for cloud as a scale-out solution, data management, and security issues, and some disciplines have different need-states for platforms. To utilize data as a competitive tool, the market of the data exchange platform services is gradually developing at this moment and a new market is being formed. Recent developments include: November 2019: Amazon Web Services (AWS) partnered with Deloitte to help customers securely find, subscribe to, and use third-party data in the cloud using AWS Data Exchange, a new service that can help address unique healthcare issues. March 2020: Google Cloud Platform (GCP) launched Anthos, a new platform that enables businesses to run their applications across multiple clouds and on-premises environments. Anthos includes a data exchange feature that allows businesses to share data between their applications, regardless of where they are deployed.. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Misuse of Virtual Currency and Security Attacks Confines the Adoption of Cryptocurrencies. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The Data Exchange Platform Services market is experiencing robust growth, driven by the increasing need for secure and efficient data sharing across organizations. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rise of cloud computing and the increasing adoption of APIs are simplifying data exchange processes and reducing costs, making data exchange platforms more accessible to businesses of all sizes. Furthermore, stringent data privacy regulations, such as GDPR and CCPA, are driving the demand for secure and compliant data exchange solutions. The growing importance of data analytics and the need for real-time data insights across different systems also contribute significantly to market growth. Major players like Amazon Web Services, Microsoft, and IBM are actively investing in and expanding their data exchange platform offerings, further fueling market competition and innovation. The market is segmented by deployment (cloud, on-premises), organization size (small, medium, large), and industry vertical (healthcare, finance, retail, etc.). The cloud deployment segment is expected to dominate due to its scalability, flexibility, and cost-effectiveness. The increasing adoption of data exchange platforms in the healthcare and finance sectors, driven by the need for secure patient data sharing and financial transactions, is also significantly contributing to market growth. However, challenges remain, including concerns over data security and integration complexities, which can act as restraints to market expansion. Overcoming these challenges through robust security protocols and user-friendly interfaces will be crucial for sustained market growth. The competitive landscape is characterized by the presence of established technology giants and specialized data exchange platform providers. This intense competition fosters innovation and drives down prices, ultimately benefiting end-users.

The data exchange tool market is projected to reach USD 14.2 billion by 2033, exhibiting a CAGR of 12.6% during the forecast period. Growing demand for data-driven decision-making, increasing adoption of cloud-based services, and the need for efficient data sharing are key factors driving the market growth. The market is segmented by deployment type (cloud-based, on-premises), application (SMEs, large enterprises), and region (North America, Europe, Asia Pacific, Middle East & Africa, South America). Among the regions, North America held the largest market share in 2025, and Asia Pacific is expected to exhibit the highest CAGR during the forecast period. Key players in the market include Harbr, Eight Wire, Cleo, iGrant.io, Unisys, X-ROAD, Eureka, Dataswift.io, Narrative.io, Explorium.ai, Snowflake, Informatica, OptInsight, Spring Labs, Weld, Lotame, Safe Software, PartnerLinQ, Digi.me, AWS, The Data Exchange, Dawex, and others. This report provides a comprehensive overview of the global data exchange tool market, with a focus on its concentration, product insights, regional trends, drivers, challenges, and emerging technologies.

A multidisciplinary repository of public data sets such as the Human Genome and US Census data that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. If you have a public domain or non-proprietary data set that you think is useful and interesting to the AWS community, please submit a request and the AWS team will review your submission and get back to you. Typically the data sets in the repository are between 1 GB to 1 TB in size (based on the Amazon EBS volume limit), but they can work with you to host larger data sets as well. You must have the right to make the data freely available.

The Data Marketplace Platform market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across various industries. The market's expansion is fueled by several key factors, including the proliferation of big data, the rising adoption of cloud-based solutions, and the growing need for data monetization strategies. Businesses are increasingly seeking efficient and secure ways to access, share, and analyze diverse datasets, leading to a surge in demand for platforms that facilitate these processes. Furthermore, the development of advanced analytics and AI/ML capabilities further enhances the value proposition of these platforms, attracting both data buyers and sellers. Competition is fierce, with established tech giants like Microsoft, Oracle, and AWS alongside specialized data marketplace providers vying for market share. The market is segmented by data type (structured, unstructured), deployment model (cloud, on-premise), and industry vertical (finance, healthcare, retail, etc.), each exhibiting unique growth trajectories. A conservative estimate suggests a market size of approximately $5 billion in 2025, growing at a CAGR of 25% over the forecast period (2025-2033). This growth is expected to be driven by increasing cloud adoption, improved data security measures, and the emergence of innovative business models within the data marketplace ecosystem. The competitive landscape is characterized by both large established players and nimble startups. Successful players are those that offer comprehensive solutions encompassing data discovery, secure access control, data governance, and advanced analytics capabilities. Geographic expansion and strategic partnerships are crucial for achieving sustainable growth. While the market enjoys significant growth potential, challenges remain including data privacy concerns, data quality issues, and the need to establish trust and transparency within the marketplace ecosystem. Addressing these challenges effectively will be critical for the continued success and expansion of the Data Marketplace Platform market. The robust growth forecast suggests significant opportunities for both established players and new entrants to capitalize on this expanding market.

OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.

Automated Weather Station and AWS-like networks are the primary source of surface-level meteorological data in remote polar regions. These networks have developed organically and independently, and deliver data to researchers in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. Moreover, station tilt causes significant biases in polar AWS measurements of radiation and wind direction. Researchers, network operators, and data centers would benefit from AWS-like data in a common format, amenable to automated analysis, and adjusted for known biases. This project addresses these needs by developing a scientific software workflow called "Justified AWS" (JAWS) to ingest Level 2 (L2) data in the multiple formats now distributed, harmonize it into a common format, and deliver value-added Level 3 (L3) output suitable for distribution by the network operator, analysis by the researcher, and curation by the data center. Polar climate researchers currently face daunting problems including how to easily: 1. Automate analysis (subsetting, statistics, unit conversion) of AWS-like L2 ASCII data. 2. Combine or intercompare data and data quality from among unharmonized L2 datasets. 3. Adjust L2 data for biases such as AWS tilt angle and direction. JAWS addresses these common issues by harmonizing AWS L2 data into a common format, and applying accepted methods to quantify quality and estimate biases. Specifically, JAWS enables users and network operators to 1. Convert L2 data (usually ASCII tables) into a netCDF-based L3 format compliant with metadata conventions (Climate-Forecast and ACDD) that promote automated discovery and analysis. 2. Include value-added L3 features like the Retrospective, Iterative, Geometry-Based (RIGB) tilt angle and direction corrections, solar angles, and standardized quality flags. 3. Provide a scriptable API to extend the initial L2-to-L3 conversion to newer AWS-like networks and instruments. Polar AWS network experts and NSIDC DAAC personnel, each with decades of experience, will help guide and deliberate the L3 conventions implemented in Stages 2-3. The project will start on July 1, 2017 at entry Technology Readiness Level 3 and will exit on June 30, 2019 at TRL 6. JAWS is now a heterogeneous collection of scripts and methods developed and validated at UCI over the past 15 years. At exit, JAWS will comprise three modular stages written in or wrapped by Python, installable by Conda: Stage 1 ingests and translates L2 data into netCDF. Stage 2 annotates the netCDF with CF and ACDD metadata. Stage 3 derives value-added scientific and quality information. The labor-intensive tasks include turning our heterogeneous workflow into a robust, standards-compliant, extensible workflow with an API based on best practices of modern scientific information systems and services. Implementation of Stages 1-2 may be straightforward though tedious due to the menagerie of L2 formats, instruments, and assumptions. The RIGB component of Stage 3 requires ongoing assimilation of ancillary NASA data (CERES, AIRS) and use of automated data transfer protocols (DAP, THREDDS). The immediate target recipient elements are polar AWS network managers, users, and data distributors. L2 borehole data suffers from similar interoperability issues, as does non-polar AWS data. Hence our L3 format will be extensible to global AWS and permafrost networks. JAWS will increase in situ data accessibility and utility, and enable new derived products (both are AIST goals). The PI is a long-standing researcher, open source software developer, and educator who understands obstacles to harmonizing disparate datasets with NASA interoperability recommendations. Our team participates in relevant geoscience communities, including ESDS working groups, ESIP, AGU, and EarthCube.

Prices are current as of 7/05/11.1CPUs are in terms of a 1-GHz Opteron 2007 processor, unless otherwise noted. For example, a machine with four 1-GHz processors would be listed as 4×1.1aCPU is a quad-core Xeon X5570, i.e., two quad-core CPUs, where each core is 4.19 GHz.1bCPU is a quad-core Xeon X5570, and instance includes two NVIDIA Tesla "Fermi" M2050 GPUs.2Costs reflect standard EC2 use with Linux OS. Costs increase when using Windows and decrease when using Reserved Instances (up-front payment) or Spot Instances (user-specified price on unused EC2 capacity).3Within same AWS availability region (e.g., AWS US-East).4Request costs are more difficult to estimate, and are usually more pertinent when databases and other similar services are involved. Programs like IOSTAT can be used to estimate EBS requests.

Global, aggregated physical air quality data from public data sources provided by government, research-grade and other sources. These awesome groups do the hard work of measuring these data and publicly sharing them, and our community makes them more universally-accessible to both humans and machines.

The EEG-DaSh data archive will establish a data-sharing resource for MEEG (EEG, MEG) data, enabling large-scale computational advancements to preserve and share scientific data from publicly funded research for machine learning and deep learning applications.

A collection of downscaled climate change projections, derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth Exchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and LOCA (CMIP5).

This data contains in-phase and quadrature waveforms of AWS-1 LTE uplink emissions collected at NASA Langley Research Center's Langley Research Antenna System. The collection campaign is part of the NASCTN effort on AWS-3 LTE impacts on Aeronautical Mobile Telemetry https://www.nist.gov/programs-projects/aws-3-lte-impacts-amt. The work is detailed in the NTIA Technical Report TR-21-553 In-Situ Captures of AWS-1 LTE for Aeronautical Mobile Telemetry System Evaluation available at https://www.its.bldrdoc.gov/publications/details.aspx?pub=3262 . Additional pertinent details on recording parameter settings are outlined in the format provided in NTIA Technical Memo TM-21-553 available at https://www.its.bldrdoc.gov/publications/details.aspx?pub=3261 .

The NASA Earth Exchange (NEX) Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011] developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The dataset is an ensemble of projections from 21 different models and two RCPs (RCP 4.5 and RCP 8.5), and provides daily estimates of maximum and minimum temperatures and precipitation using a daily Bias-Correction - Spatial Disaggregation (BCSD) method (Thrasher, et al., 2012). The data spans the entire globe with a 0.25 degree (~25-kilometer) spatial resolution for the periods from 1950 through 2005 (Historical) and from 2006 to 2100 (Climate Projections).

This data is provided as a supplement to NIST Technical Note 2140, AWS-3 LTE Impacts on AMT, which presents test results assessing the impact of adjacent-band long term evolution (LTE) emissions on aeronautical mobile telemetry (AMT) systems. The data provided here is sufficient to reproduce all of the plots and analyses in Chapters 5 and 6, and Appendices B, C, D, and E, which present the data analysis results. All files are spreadsheets in comma-separated values (CSV) format, with labeled columns for various key performance indicators (KPIs) and test configurations. See the data description document provided in the zip file for an outline of the contents and the technical note referenced above for details on the data collection.

This datasets contains the coverage of automatic Weather Stations (AWS). Eight AWS are strategically located in the northern parts of the country including Hargeysa, Borama, Aburin, Dacarbudhug, Xumbaweyne, Ceerigaabo, Garowe and Gaalckacyo . The nineth AWS is located in the south at the border of Kenya, Somalia and Ethiopia in Mandera town. It is hoped that when the situation allows more automatic weather stations will be installed in the southern regions. The stations record a variety of weather elements including; rainfall, temperature, relative humidity, atmospheric pressure, wind speed, wind direction and solar radiation. Data from these automatic stations is received in SWALIM Nairobi office daily in near-real-time through satellite at a frequency of every four hours. The data is then transmitted to the public though a client service platform on the SWALIM website.

In the fourth quarter of 2024, the most popular vendor in the cloud infrastructure services market, Amazon Web Services (AWS), controlled ** percent of the entire market. Microsoft Azure takes second place with ** percent market share, followed by Google Cloud with ** percent market share. Together, these three cloud vendors account for ** percent of total spend in the fourth quarter of 2024. Organizations use cloud services from these vendors for machine learning, data analytics, cloud native development, application migration, and other services. AWS Services Amazon Web Services is used by many organizations because it offers a wide variety of services and products to its customers that improve business agility while being secure and reliable. One of AWS’s most used services is Amazon EC2, which lets customers create virtual machines for their strategic projects while spending less time on maintaining servers. Another important service is Amazon Simple Storage Service (S3), which offers a secure file storage service. In addition, Amazon also offers security, website infrastructure management, and identity and access management solutions. Cloud infrastructure services Vendors offering cloud services to a global customer base do so through different types of cloud computing, which include infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Further, there are different cloud computing deployment models available for customers, namely private cloud and public cloud, as well as community cloud and hybrid cloud. A cloud deployment model is defined based on the location where the deployment resides, and who has access to and control over the infrastructure.

Market Analysis for Amazon AWS Cloud Solutions The market for Amazon AWS (Amazon Web Services) Cloud Solutions is projected to reach a staggering $473.30 million by 2033, growing at a remarkable CAGR due to the increasing adoption of cloud computing and the expansion of data-intensive applications. Key drivers fueling this growth include the cost efficiency, flexibility, and scalability offered by cloud platforms. The market is segmented by type (Infrastructure Setup, Application Development, Platform, Other) and application (Large Corporations, SMEs). The AWS cloud solutions market is dominated by established players such as Netguru SA, Logicalis, and Rackspace. These companies offer a comprehensive suite of services, ranging from consulting and design to implementation and support. The market is expected to witness increased competition from emerging cloud providers in the coming years. Geographical expansion is another key trend, with the market witnessing significant growth in emerging regions such as Asia-Pacific and the Middle East & Africa.

These datasets contain peer-to-peer trades from various recommendation platforms.

Metadata includes

• peer-to-peer trades

• have and want lists

• image data (tradesy)