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

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.

AWS Data

## Overview

AWS Data is a dataset for instance segmentation tasks - it contains Object annotations for 2,886 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

About

From website:

Public Data Sets on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community, and like all AWS services, users pay only for the compute and storage they use for their own applications. An initial list of data sets is already available, and more will be added soon.

Previously, large data sets such as the mapping of the Human Genome and the US Census data required hours or days to locate, download, customize, and analyze. Now, 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. For example, users can produce or use prebuilt server images with tools and applications to analyze the data sets. By hosting this important and useful data with cost-efficient services such as Amazon EC2, AWS hopes to provide researchers across a variety of disciplines and industries with tools to enable more innovation, more quickly.

As of April 2025, Amazon Wed Services (AWS) cloud data centers operated in ** markets in the Asia-Pacific region, with ** availability zones in total. An availability zone (AZs) is one or more separate data centers located within specific regions within which cloud services originate and operate. Each AZ has independent power, cooling, and physical security.

featured here: https://medium.com/@george.fekkas/quick-and-dirty-cloudtrail-threat-hunting-log-analysis-b64af10ef923

https://summitroute.com/blog/2020/10/09/public_dataset_of_cloudtrail_logs_from_flaws_cloud/

The columns should be flattened. Some columns dropped because they are not good features for NVIDIA Morpheus digital fingerprinting autoencoders.

Amazon Web Services (AWS) global cloud data centers operate in ** geographic regions, each containing several availability zones (AZs). As of 2024, Europe/Middle East/Africa and Asia Pacific and China had ** zones combined, which is over ** percent of all AWS' AZs.

Test Private AWS S3 data. This is for TEST PURPOSES ONLY

This dataset contains transaction data from a fictitious SaaS company selling sales and marketing software to other companies (B2B). In the dataset, each row represents a single transaction/order (9,994 transactions), and the columns include:

Here is the Original Dataset: https://ee-assets-prod-us-east-1.s3.amazonaws.com/modules/337d5d05acc64a6fa37bcba6b921071c/v1/SaaS-Sales.csv

Features

| # | Name of the attribute | Description | | -- | --------------------- | -------------------------------------------------------- | | 1 | Row ID | A unique identifier for each transaction. | | 2 | Order ID | A unique identifier for each order. | | 3 | Order Date | The date when the order was placed. | | 4 | Date Key | A numerical representation of the order date (YYYYMMDD). | | 5 | Contact Name | The name of the person who placed the order. | | 6 | Country | The country where the order was placed. | | 7 | City | The city where the order was placed. | | 8 | Region | The region where the order was placed. | | 9 | Subregion | The subregion where the order was placed. | | 10 | Customer | The name of the company that placed the order. | | 11 | Customer ID | A unique identifier for each customer. | | 13 | Industry | The industry the customer belongs to. | | 14 | Segment | The customer segment (SMB, Strategic, Enterprise, etc.). | | 15 | Product | The product was ordered. | | 16 | License | The license key for the product. | | 17 | Sales | The total sales amount for the transaction. | | 18 | Quantity | The total number of items in the transaction. | | 19 | Discount | The discount applied to the transaction. | | 20 | Profit | The profit from the transaction. |

Inspiration: The CRoss Industry Standard Process for Data Mining (CRISP-DM) CRISP-DM methodology

• [ ] Understanding the business
• [ ] Understanding the data
• [x] Preparing the data
• [ ] Modelling
• [ ] Evaluating
• [ ] Implementing the analysis.

This data set represents the automatic weather station (AWS) data from the 16 stations of the Desert Research Institute network for the period 00 PST March 1 to 00 PST May 1, 2006 during the Terrain-induced Rotor Experiment (T-REX) field campaign. The data have a temporal resolution of 30 seconds, and are in netCDF format files.

In the past, the U.S. Geological Survey (USGS) and NASA collaborated on the creation of four global land data sets from Landsat images: one from the 1970s, and one each from circa 1990, 2000, and 2005. Each of these global data sets was created from the primary Landsat sensor in use at the time: the Multispectral Scanner (MSS) in the 1970s, the Thematic Mapper (TM) in 1990, Enhanced Thematic Mapper Plus (ETM+) in 2000, and a combination of TM and ETM+ in 2005.

A centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of the novel corona virus (SARS-CoV-2) and its associated illness, COVID-19. Globally, there are several efforts underway to gather this data, and we are working with partners to make this crucial data freely available and keep it up-to-date. Hosted on the AWS cloud, we have seeded our curated data lake with COVID-19 case tracking data from Johns Hopkins and The New York Times, hospital bed availability from Definitive Healthcare, and over 45,000 research articles about COVID-19 and related coronaviruses from the Allen Institute for AI.

The following data is pulled from AWS official pricing API. Contains all pricing data across AWS services

Source: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-price-list-query-api.html

Radar and weather station data collected in 2014 as part of the Subglacial Access and Fast Ice Research Experiment (SAFIRE) and used to quantify englacial water storage in the paper, Surface Meltwater Impounded by Seasonal Englacial Storage in West Greenland. See the following link to the manuscript: https://doi.org/10.1029/2018GL079787For additional data from the same field campaign see:https://doi.org/10.6084/m9.figshare.5745294And items 112029 and 112009 located athttp://www.bgs.ac.uk/services/ngdc/accessions/index.html

Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.

Update

This data file lists approximate locations of Amazon Web Services (AWS) data centers around the world. Some of this was collected manually by searching local news articles on real estate purchases by Amazon in each region, and other information was obtained from https://www.datacenterdynamics.com/. Note that in most regions AWS has multiple data centers, and so the selected location may only reflect one of them in that region.

This data is helpful for AWS users to quickly view where their assets are housed across the world and help them ensure that they are meeting information privacy guidelines.

This data set contains 1-minute resolution surface meteorological data from the Atmospheric Boundary Layer Experiments (ABLE) operated by the Argonne National Laboratory in the Walnut River Watershed in Butler County Kansas (east of Wichita). The ABLE Automated Weather Station (AWS) Network consists of five stations. Data cover the period from 20 May to 7 July 2003 The data are in columnar ASCII format.

tan-0909/data-science-on-aws dataset hosted on Hugging Face and contributed by the HF Datasets community

Meteorological data and images collected on the Mt. Hunter plateau, Denali National Park, Alaska. Data were collected with an automatic weather station using instrumentation from Campbell Scientific. Large-scale atmospheric circulation systems affect the geographic distribution of precipitation in western North America, yet little is known about how these systems may have varied before the instrumental period of the last 150 years. The main goal of this project is to reconstruct the history of precipitation in Alaska during the last thousand years using ice core records of snow accumulation. The researchers plan to collect several new ice cores from the Mt. Hunter Plateau in the Alaska Range of Denali National Park and the new ice cores will be combined with an existing spatial array of ice cores in the region to map changes in the spatial patterns of precipitation. Because changes in atmospheric circulation patterns caused by ENSO and the Pacific Decadal Oscillation (PDO) affect where the precipitation falls, this spatial array of ice cores will provide a record of how these larger scale climate systems have varied during the last thousand years. The project will focus on determining the differences in the precipitation patterns at the Little Ice Age (approximately 200 to 600 years ago) and Medieval Climate Anomaly (approximately 800 to 1,200 years ago).

Gain extensive insights with our Amazon datasets, encompassing detailed product information including pricing, reviews, ratings, brand names, product categories, sellers, ASINs, images, and much more. Ideal for market researchers, data analysts, and eCommerce professionals looking to excel in the competitive online marketplace. Over 425M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more