Amazon.com, Inc. engages in the retail sale of consumer products, advertising, and subscriptions service through online and physical stores in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It also manufactures and sells electronic devices, including Kindle, Fire tablets, Fire TVs, Echo, Ring, Blink, and eero; and develops and produces media content. In addition, the company offers programs that enable sellers to sell their products in its stores; and programs that allow authors, independent publishers, musicians, filmmakers, Twitch streamers, skill and app developers, and others to publish and sell content. Further, it provides compute, storage, database, analytics, machine learning, and other services, as well as advertising services through programs, such as sponsored ads, display, and video advertising. Additionally, the company offers Amazon Prime, a membership program. The company's products offered through its stores include merchandise and content purchased for resale and products offered by third-party sellers. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

Features 1. Stock Data: Open, High, Low, Close, Volume 2. Date: The specific trading date. 3. Closing Price: The price at which the stock closed on a given day.

Source Free API Provider

Comprehensive dataset covering Amazon's 1.9 million active third-party sellers worldwide, including regional distribution, growth trends, and marketplace dynamics from 2000-2025.

About the dataset

Historical Amazon stock prices in daily frequency, from 14 May, 1997 to 24 Sep, 2020.

Company Profile

Amazon.com, Inc. engages in the retail sale of consumer products and subscriptions in North America and internationally. The company operates through three segments: North America, International, and Amazon Web Services (AWS). It sells merchandise and content purchased for resale from third-party sellers through physical and online stores. The company also manufactures and sells electronic devices, including Kindle, Fire tablets, Fire TVs, Rings, and Echo and other devices; provides Kindle Direct Publishing, an online service that allows independent authors and publishers to make their books available in the Kindle Store; and develops and produces media content. In addition, it offers programs that enable sellers to sell their products on its Websites, as well as its stores; and programs that allow authors, musicians, filmmakers, skill and app developers, and others to publish and sell content. Further, the company provides compute, storage, database, and other AWS services, as well as fulfillment, advertising, publishing, and digital content subscriptions. Additionally, it offers Amazon Prime, a membership program, which provides free shipping of various items; access to streaming of movies and TV episodes; and other services. The company also operates in the food delivery business in Bengaluru, India. It serves consumers, sellers, developers, enterprises, and content creators. The company also has utility-scale solar projects in China, Australia, and the United States. Amazon.com, Inc. was founded in 1994 and is headquartered in Seattle, Washington.

Amazon is one of the most recognisable brands in the world, and the third largest by revenue. It was the fourth tech company to reach a $1 trillion market cap, and a market leader in e-commerce,...

From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost *** billion U.S. dollars, up from *** billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over *** billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately *** billion U.S. dollars was earned in North America compared to only roughly *** billion U.S. dollars internationally.

Analysis of ‘Amazon.com,Inc.(AMZN) Stock Price from May 15 1997’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/devtaz/amazoncom-inc-amzn on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset is stock price of the Amazon Inc till this day.

Content

Whole dataset has total seven columns. Data, Opening price, High price of the day, lowest price of the day, closing price when market close, adjusted price after market close and the volume.

--- Original source retains full ownership of the source dataset ---

Stock Market Analysis of Amazon.com, Inc. (AMZN) from it's Founding / Listing Years which is 1997 to 2022

Data Dictionary

About Amazon.com, Inc. (AMZN)

Amazon.com, Inc. is an American multinational technology company which focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It has been referred to as "one of the most influential economic and cultural forces in the world", and is one of the world's most valuable brands. It is one of the Big Five American information technology companies, alongside Alphabet, Apple, Meta, and Microsoft.

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Those data represent the Brazilian Amazon by 68,629,072 250-m pixels, and 141,032 pixels have LiDAR information and therefore had the AGB estimated, and converted to Mg ha-1 (file trainning_dataset.csv). To generate a wall-to-wall map of the Brazilian Amazon at 250-m resolution, we trained a Random Forest (RF) model 29 using the AGB estimated pixels and remote sensing layers formed by: MODIS vegetation indices, Shuttle Radar Topography Mission (SRTM) data, Tropical Rainfall Measuring Mission (TRMM), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, along with the central coordinates of each 250m pixel, stored in amazon_*.csv files.

Derived from MODIS, we used the Vegetation Indices 16-Day L3 Global 250m temporal series (MOD13Q1) products from 2016, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), from MODIS. From TRMM we used the 3B43 V6 precipitation data, with each pixel value representing the monthly accumulated precipitation from 1998 to 2016 at a resolution of 0.25 degrees. From PALSAR, we used the L-band image in the HH and HV polarizations, acquired in 2015. When necessary, the remote sensing products were resampled by means to a 250 m grid.

Random Forest models were tested using the H2O_Flow platform and produced the best model based on RMSE and R², containing NDVI q3, PALSAR HV, TRMM mean, X, SRTM, Y, PALSAR HH, EVI q1, EVI mean, NDVI mean, NDVI q1, EVI q3.

The objective of CAMREX (Carbon in the Amazon River Experiment) project which was conducted from 1982 through 1991, was been to define by mass balances and direct measurements those processes which control the distribution of bioactive elements (C, N, P and O) in the mainstem of the Amazon River in Brazil. The CAMREX dataset represents a time series unique in its length and detail for very large river systems. The central sampling strategy has been to obtain representative flux-weighted water samples for comprehensive chemical analysis and to make rate measurements over 18 different sites within a 2000 km reach of the Brazilian Amazon mainstem, including major intervening tributaries. Samples have now been collected on 13 different cruises (1982-1991) during contrasting hydrographic stages. Data or images are provided for (1) water chemistry, (2) daily river discharge, (3) monthly estimates for 1989 of some model drivers and structure including NPP, Evapotranspiration, Precipitation, Temperature, and AVHRR data, (4) daily precipitation, and (5) air temperature anomalies.The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.

Amazon Review 2023 is an updated version of the Amazon Review 2018 dataset. This dataset mainly includes reviews (ratings, text) and item metadata (desc- riptions, category information, price, brand, and images). Compared to the pre- vious versions, the 2023 version features larger size, newer reviews (up to Sep 2023), richer and cleaner meta data, and finer-grained timestamps (from day to milli-second).

Analysis of ‘Historical Stock Price of (FAANG + 5) companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/suddharshan/historical-stock-price-of-10-popular-companies on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

--- Original source retains full ownership of the source dataset ---

This dataset is a compilation of carbon and energy eddy covariance flux, meteorology, radiation, canopy temperature, humidity, CO2 profiles and soil moisture and temperature profile data that were collected at nine towers across the Brazilian Amazon. Independent investigators provided the data from a variety of flux tower projects over the period 1999 thru 2006. This is Version 2 of the tower data compilation, where the data have been harmonized across projects, additional quality control checks were performed, and have been aggregated to hourly, daily, 16-day, and monthly timesteps. This integrated dataset is intended to facilitate integrative studies and data-model synthesis from a common reference point.

This repository includes HRLDAS Noah-MP model output generated as part of Bieri et al. (2025) - Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): Impact on Amazon dry-season transpiration. These data are distributed in two different formats: Raw model output files and subsetted files that include data for a specific variable. All files are .nc format (NetCDF) and aggregated into .tar files to facilitate download. Given the size of these datasets, Globus transfer is the best way to download them. Raw model output for four model experiments is available: FD (control), GW, SOIL, and ROOT. See the associated publication for information on the different experiments. These data span an approximately 20 year period from 01 Jun 2000 to 31 Dec 2019. The data have a spatial resolution of 4 km and a temporal frequency of 3 hours. These data are for a domain in the southern Amazon basin (see Figure 1 in the associated publication). Data for each experiment is available as a .tar file which includes 3-hourly NetCDF files. All default Noah-MP output variables are included in each file. As a result, the .tar files are quite large and may take many hours or even days to transfer depending on your network speed and local configurations. These files are named 'noahmp_output_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT). Subsetted model output at a daily temporal resolution for all four model experiments is also available. These .tar files include the following variables: water table depth (ZWT), latent heat flux (LH), sensible heat flux (HFX), soil moisture (SOIL_M), canopy evaporation (ECAN), ground evaporation (EDIR), transpiration (ETRAN), rainfall rate at the surface (QRAIN), and two variables that are specific to the ROOT experiment: ROOTACTIVITY (root activity function) and GWRD (active root water uptake depth). There is one file for each variable within the tarred files. These files are named 'noahmp_output_subset_2000_2019_EXP.tar', where EXP is the name of the experiment (FD, GW, SOIL, or ROOT). Finally, there is a sample dataset with raw 3-hourly output from the ROOT experiment for one day. The purpose of this sample dataset is to allow users to confirm if these data meet their needs before initiating a full transfer via Globus. This file is named 'noahmp_output_sample_ROOT.tar'. The README.txt file provides information on the Noah-MP output variables in these datasets, among other specifications. Information on HRLDAS Noah-MP and names/definitions of model output variables that are useful in working with these data are available here: http://dx.doi.org/10.5065/ew8g-yr95. Note that some output variables may be listed in this document under a different variable name, so searching for the long name (e.g. 'baseflow' instead of 'QRF') is recommended. Information on additional output variables that were added to the model as part of this study is available here: https://github.com/bieri2/bieri-et-al-2025-EGU-GMD/tree/DynaRoot. Model code, configuration files, and forcing data used to carry out the model simulations are linked in the related resources section.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Amazon.com Inc. (AMZN): The Future of Retail?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The US National Center for Atmospheric Research partnered with the IBS Center for Climate Physics in South Korea to generate the CESM2 Large Ensemble which consists of 100 ensemble members ... at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble were made downloadable via the Climate Data Gateway on June 14, 2021. NCAR has copied a subset (currently ~500 TB) of CESM2 LENS data to Amazon S3 as part of the AWS Public Datasets Program. To optimize for large-scale analytics we have represented the data as ~275 Zarr stores format accessible through the Python Xarray library. Each Zarr store contains a single physical variable for a given model run type and temporal frequency (monthly, daily).

This dataset is a compilation of carbon and energy eddy covariance flux, meteorology, radiation, canopy temperature, humidity, CO2 profiles and soil moisture and temperature profile data that were collected at nine towers across the Brazilian Amazon. Independent investigators provided the data from a variety of flux tower projects over the period 1999 thru 2006. This is Version 2 of the tower data compilation, where the data have been harmonized across projects, additional quality control checks were performed, and have been aggregated to hourly, daily, 16-day, and monthly timesteps. This integrated dataset is intended to facilitate integrative studies and data-model synthesis from a common reference point.

Important Notice: Update of JRA-55 data will terminate at the end of January 2024. Please use Japanese Reanalysis for Three Quarters of a Century (JRA-3Q) [https://rda.ucar.edu/datasets/d640000/] at that time. The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25. Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and...

Weekly archive of some State of Pennsylvania datasets found in this list: https://data.pa.gov/browse?q=vaccinations

For most of these datasets, the "date_saved" field is the date that the WPRDC pulled the data from the state data portal and the archive combines all the saved records into one table. The exception to this is the "COVID-19 Vaccinations by Day by County of Residence Current Health (archive)" which is already published by the state as an entire history.

The "date_updated" field is based on the date that the "updatedAt" field from the corresponding data.pa.gov dataset. Changes to this field have turned out to not be a good indicator of whether records have updated, which is why we are archiving this data on a weekly basis without regard to the "updatedAt" value. The "date_saved" field is the one you should sort on to see the variation in vaccinations over time.

Most of the source tables have gone through schema changes or expansions. In some cases, we've kept the old archives under a separate resource with something like "[Orphaned Schema]" added to the resource name. In other cases, we've adjusted our schema to accommodate new column names, but there will be a date range during which the new columns have null values because we did not start pulling them until we became aware of them.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The MOD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD14A1 Version 6.1 data product.The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MOD14A1) Version 6 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MOD14A1 contains eight consecutive days of fire data conveniently packaged into a single file.The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire radiative power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues Known issues are described on the MODIS Land Quality Assessment website and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua OutageImprovements/Changes from Previous Versions Refinements to internal cloud mask, which sometimes flags heavy smoke as clouds. Fix for frequent false alarms occurring in the Amazon that are caused by small (~1 km²) clearings within forests. * Fix to correct a bug that causes incorrect assessment of cloud and water pixels adjacent to fire pixels near the scan edge. Detect small fires using dynamic thresholding. Process ocean and coastline pixels to detect fire from oil rigs. The Version 6 fire mask has the potential to detect fire over water pixels. Therefore, class 3 pixel values have been changed to be classified as “non-fire water pixels”.

This dataset was initiated in 2019 to introduce one of the first apps for Amazon Alexa in Luxembourg. This project aimed to release a real use-case of local services on a voice assistant platform, and we developed a waste pickup calendar.

The first challenge was accessing the raw data; at that time, the only choice was to scrap it from official websites. So we developed a nodejs modular scraping tool that connects to multiple sources, which are to this day: 1. HTML from sidec.lu using cheerio library 2. json from valorlux.lu 3. ICS files from vdl.lu using node-ical library

When scraping is complete, the tool unifies all results into a single format, Normalises pickup types, matches against the CACLR address database and writes 1 json file per postal code in a simple format:

“’ [ { ‘uid’ means: ‘5e8a5f0732fc6’, “event_date”: ‘1608073200000’, “city”: ‘Luxembourg’, ‘rental’ means: “Eich Coast”, “streetNumbers”: “1-25, 2-24”, ‘postal code’: 1450, “Summary”: “BULKY” }, { ‘uid’ means: ‘5e8a5f074f2c3’, “event_date”: ‘1608505200000’, “city”: ‘Luxembourg’, ‘rental’ means: “Eich Coast”, “streetNumbers”: “1-25, 2-24”, ‘postal code’: 1450, “Summary”: “PAPER” } ] “’ Note: The dataset does not cover the entire country (yet). Several other websites/sources should be crawled and consolidated to have a complete picture.