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 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 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.

Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data was reported at 0.000 % in 01 May 2024. This stayed constant from the previous number of 0.000 % for 30 Apr 2024. Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data is updated daily, averaging 0.080 % from Apr 2024 (Median) to 01 May 2024, with 9 observations. The data reached an all-time high of 0.080 % in 27 Apr 2024 and a record low of 0.000 % in 01 May 2024. Malawi Internet Usage: Device Vendor Market Share: Mobile: Amazon data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Malawi – Table MW.SC.IU: Internet Usage: Device Vendor Market Share.

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).

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).

Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data was reported at 0.000 % in 22 Aug 2024. This stayed constant from the previous number of 0.000 % for 21 Aug 2024. Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data is updated daily, averaging 0.050 % from Aug 2024 (Median) to 22 Aug 2024, with 9 observations. The data reached an all-time high of 0.050 % in 18 Aug 2024 and a record low of 0.000 % in 22 Aug 2024. Norway Internet Usage: Device Vendor Market Share: Mobile: Amazon data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Norway – Table NO.SC.IU: Internet Usage: Device Vendor Market Share.

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.

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.

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.

The NASA Earth Exchange (NEX) Downscaled Climate Projections (NEX-DCP30) dataset is comprised of downscaled climate scenarios for the conterminous United States 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 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 includes downscaled projections from 33 models, as well as ensemble statistics calculated for each RCP from all model runs available. The purpose of these datasets is to provide a set of 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. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).

Amazon reported $41.49B in Stock for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The global data catalog market is experiencing steady growth, driven by the increasing volume and complexity of enterprise data. As organizations face the challenge of managing multiple data sources and ensuring data quality and governance, the adoption of data catalogs has become increasingly important. According to market research, the total value of the market in 2025 was approximately $2.61 billion, with a projected CAGR of 2.50% from 2025 to 2033. This growth is primarily attributed to the growing need for data-driven decision-making and the proliferation of big data and artificial intelligence (AI) technologies. Key industry trends indicate a growing emphasis on cloud-based data catalog solutions, as well as the integration of AI and machine learning (ML) capabilities. These technologies enhance the automation and efficiency of data cataloging processes, while providing advanced features such as data lineage tracking and data quality monitoring. Furthermore, the convergence of data catalog solutions with other enterprise applications, such as data governance and data analytics platforms, creates opportunities for comprehensive data management and improved data utilization. The competitive landscape is characterized by a mix of established vendors and emerging players, with companies such as Tamr Inc, Collibra NV, TIBCO Software Inc, and IBM Corporation holding significant market share. Ongoing innovations and strategic acquisitions are shaping the market dynamics, as vendors strive to differentiate their offerings and meet evolving customer requirements. The global data catalog market size was valued at USD 2.0 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 24.3% from 2023 to 2030, reaching USD 12.0 billion by 2030. Recent developments include: November 2022 - Amazon EMR customers can now use AWS Glue Data Catalog from their streaming and batch SQL workflows on Flink. The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog. With this release, Companies can directly run Flink SQL queries against the tables stored in the Data Catalog., September 2022 - Syniti, a global leader in enterprise data management, updated new data quality and catalog capabilities available in its industry-leading Syniti Knowledge Platform, building on the enhancements in data migration and data matching added earlier this year. The Syniti Knowledge Platform now includes data quality, catalog, matching, replication, migration, and governance, all available under one login in a single cloud solution. It provides users with a complete and unified data management platform enabling them to deliver faster and better business outcomes with data they can trust., August 2022 - Oracle Cloud Infrastructure collaborated with Anaconda, the world's most recognized data science platform provider. By permitting and integrating the latter company's repository throughout OCI Machine Learning and Artificial Intelligence services, the collaboration aimed to give safe, open-source Python and R tools and packages.. Key drivers for this market are: Growing adoption of Cloud Based Solutions, Solutions Segment is Expected to Hold a Larger Market Size. Potential restraints include: Lack of Standardization and Security Concerns. Notable trends are: Solutions Segment is Expected to Hold a Larger Market Size.

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.

Amazon reported $10.69B in Ordinary Share Capital for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Ordinary Share Capital including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Amazon reported $727.92B in Assets for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Assets including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Amazon reported $50.74B in Selling and Administration Expenses for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Amazon reported $180.2B in Sales Revenues for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Amazon reported $196.87B in Current Assets for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Current Assets including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The global search engine market, valued at $37.39 billion in 2025, is projected to experience robust growth, driven by the increasing adoption of smartphones and internet penetration across emerging economies. A Compound Annual Growth Rate (CAGR) of 14.82% from 2025 to 2033 indicates a significant expansion of this market. Key drivers include the rising demand for enhanced search capabilities, the proliferation of voice search technology, and the growing importance of search engine optimization (SEO) for businesses. The market's segmentation reveals a dynamic landscape, with both online and offline distribution channels contributing significantly. The end-user segment is divided between personal and commercial use, with the latter showing strong growth potential fueled by the increasing reliance on data-driven marketing and advertising strategies. Major players like Google, Amazon, and Baidu are at the forefront of innovation, constantly refining their algorithms and expanding their functionalities to maintain a competitive edge. The competitive landscape is further shaped by the emergence of specialized search engines catering to niche markets, driving innovation and competition. The market's geographical distribution showcases varying growth rates across regions. North America and Europe currently hold substantial market share, driven by high internet penetration and technological advancement. However, Asia-Pacific is poised for rapid growth due to its expanding digital economy and the rising number of internet users. Factors such as data privacy concerns, increasing regulatory scrutiny, and the potential for algorithm bias represent key restraints to market growth. To mitigate these challenges, search engine companies are investing heavily in responsible AI development and data security measures. The forecast period from 2025 to 2033 will likely see a continuous shift towards personalized search experiences, advanced analytics capabilities, and a greater focus on user privacy, ultimately shaping the future of online information retrieval. Recent developments include: February 2023: Microsoft launched "Binging," a cutting-edge search engine driven by AI. This innovative search engine is powered by a state-of-the-art OpenAI model, specifically fine-tuned to optimize search capabilities. The new OpenAI model draws from the expertise of ChatGPT and GPT-3.5, resulting in even faster and more precise search technology., November 2022: Google introduced local search features that were previously showcased earlier in the year. These features include the ability to search your surroundings using your phone's camera. Google has also unveiled an option to search for restaurants based on specific dishes and a new search functionality integrated into Google Maps' Live View., November 2022: Up until this point, search insights were exclusively accessible in English, focusing on users from the US, India, Canada, and the UK. However, YouTube is currently experimenting with expanding the availability of Search Insights on the desktop to more languages, starting with Japanese, Korean, and Hindi, and with plans to include additional languages in the future.. Key drivers for this market are: Increasing Focus to Improve Customer Experience Across Professional Services, Self Service and Personal Segment to Witness the Highest Growth. Potential restraints include: Increasing Focus to Improve Customer Experience Across Professional Services, Self Service and Personal Segment to Witness the Highest Growth. Notable trends are: Self Service and Personal Segment to Witness the Highest Growth.