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 booming, projected to reach $20B+ by 2033 (CAGR 18%). This in-depth analysis explores market drivers, trends, restraints, key players (Snowflake, Informatica, AWS, etc.), and regional growth. Learn about secure data sharing solutions and the future of data exchange.

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. 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 AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

Datasets

Blockchain dataset - Maintained by - Path:
- Bitcoin - AWS - s3://aws-public-blockchain/v1.0/btc/
- Ethereum - AWS - s3://aws-public-blockchain/v1.0/eth/
- Arbitrum - SonarX - s3://aws-public-blockchain/v1.1/sonarx/arbitrum/
- Aptos - SonarX - s3://aws-public-blockchain/v1.1/sonarx/aptos/
- Base - SonarX - s3://aws-public-blockchain/v1.1/sonarx/base/
- Provenance - SonarX - s3://aws-public-blockchain/v1.1/sonarx/provenance/
- XRP Ledger - SonarX - s3://aws-public-blockchain/v1.1/sonarx/xrp/
- Stellar(XDR files) - Stellar - s3://aws-public-blockchain/v1.1/stellar/
- The Open Network (TON) - TON - s3://aws-public-blockchain/v1.1/ton/
- Cronos - Cronos - s3://aws-public-blockchain/v1.1/cronos/

Become a Data Provider

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

The Data Marketplace Platform market is booming, projected to reach $5 billion by 2025, driven by increased demand for data-driven insights and cloud adoption. Discover key trends, leading companies (AWS, Microsoft, Snowflake), and growth projections in this comprehensive market analysis.

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 Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

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.

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.

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.

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.

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 .

Discover the booming Data Sharing Cluster market analysis, revealing a CAGR-driven expansion fueled by cloud computing, data privacy regulations, and AI. Learn about key players like AWS, Azure, and IBM, and explore regional market trends shaping this dynamic sector through 2033.

The global Amazon AWS Cloud Solutions market size is projected to reach $47330 million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is primarily driven by the increasing adoption of cloud-based solutions by businesses of all sizes to optimize their operations, reduce costs, and enhance flexibility. Additionally, the growing popularity of artificial intelligence (AI), machine learning (ML), and data analytics is further fueling the demand for AWS cloud solutions, as these technologies require robust and scalable cloud infrastructure. The market is segmented by type into cloud infrastructure setup, cloud application development services, cloud platform, and others. The cloud infrastructure setup segment is expected to hold the largest market share during the forecast period due to the high demand for cloud-based infrastructure services, such as compute, storage, and networking. The large corporation segment is anticipated to contribute the majority of the market revenue, as large enterprises are increasingly recognizing the benefits of cloud solutions and investing heavily in their adoption. North America is projected to dominate the market throughout the forecast period, followed by Europe and Asia Pacific. The presence of prominent cloud providers and the growing adoption of cloud-based solutions in these regions are key factors driving the market growth.

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).

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.

This registry exists to help people discover and share datasets that are available via AWS resources. Learn more about sharing data on AWS. See all usage examples for datasets listed in this registry. See datasets from Facebook Data for Good, NASA Space Act Agreement, NIH STRIDES, NOAA Big Data Program, Space Telescope Science Institute, and Amazon Sustainability Data Initiative.

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

Discover the explosive growth of the Data Lake Services market! This comprehensive analysis reveals key trends, drivers, and restraints, along with regional market share data and insights into leading companies like AWS, Azure, and Snowflake. Learn how the increasing adoption of cloud-based solutions and big data analytics is shaping the future of data management.