In 2024, Amazon's net revenue from subscription services segment amounted to 44.37 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 200 million paying members worldwide at the end of 2020. The AWS category generated 107.56 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 638 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

Amazon Sales Dataset Description This dataset contains 250 records of Amazon sales transactions, including details about the products sold, customers, payment methods, and order statuses.

Columns Description: Order ID - Unique identifier for each order (e.g., ORD0001).

Date - Date of the order.

Product - Name of the product purchased.

Category - Product category (Electronics, Clothing, Home Appliances, etc.).

Price - Price of a single unit of the product.

Quantity - Number of units purchased in the order.

Total Sales - Total revenue from the order (Price × Quantity).

Customer Name - Name of the customer.

Customer Location - City where the customer is based.

Payment Method - Mode of payment (Credit Card, Debit Card, PayPal, etc.).

Status - Order status (Completed, Pending, or Cancelled).

This dataset can be used for sales analysis, customer behavior insights, and revenue trends visualization. 🚀

According to forecasts, net sales of electrical products on Amazon are forecast at over *** billion U.S. dollars. With a compound annual growth rate of **** percent, this figure is expected to exceed *** billion dollars by 2026. Yet, the category expected to grow the strongest on the e-commerce platform is health and beauty.

Total-Cash-From-Operating-Activities Time Series for Amazon.com Inc. 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 also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

Change-Receivables Time Series for Amazon.com Inc. 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 also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

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

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

Comprehensive dataset covering Amazon Prime availability across 27 countries, including launch dates, pricing, and regional benefit differences

Context: The "Amazon Books Dataset" is a meticulously curated collection of data related to books available on the Amazon platform, with a primary focus on computer science literature. This dataset holds significant importance for researchers, industry experts, and enthusiasts who are interested in gaining profound insights into the world of computer science literature, publishing trends, and the broader book market. Computer science is at the forefront of technological innovation and is a driving force in shaping the modern world. Understanding the dynamics of the Amazon book market, particularly in the realm of computer science, is essential for various stakeholders in the publishing, technology, and retail industries.

Data Source: The data for this dataset has been meticulously sourced and compiled from Amazon's extensive catalog of books, with a specific emphasis on books related to computer science. The information is current as of the dataset's creation date and may continue to evolve as new books are published and existing ones undergo changes in attributes such as ratings and prices.

Content: The "Amazon Books Catalog Dataset" provides a rich and comprehensive array of data points for each book listed on Amazon, with a primary focus on computer science literature. These data points empower users to conduct in-depth analyses and uncover valuable insights. Here are the key columns included in this dataset:

1. Title: The title of the book, represents its core identity.
2. Description: A concise yet informative overview of the book's content and themes.
3. Author: The name(s) of the author(s) responsible for creating the literary work.
4. ISBN-10: The International Standard Book Number in the 10-digit format, aiding in precise identification.
5. ISBN-13: The International Standard Book Number in the 13-digit format, enhancing global recognition.
6. Publish Date: The date on which the book was officially published, marking its entry into the literary world.
7. Edition: Information about the edition of the book, if applicable, providing insights into its various releases.
8. Best Seller: A binary indicator (1 or 0) signifying whether the book has achieved best-seller status.
9. Top Rated: A binary indicator (1 or 0) highlighting whether the book has received high ratings and acclaim from readers.
10. Rating: The average rating awarded to the book by Amazon customers, offering an assessment of its quality.
11. Review Count: The total number of user reviews posted for the book, reflecting reader engagement and feedback.
12. Price: The current price of the book on the Amazon platform, influences purchase decisions.

Potential Use Cases: This dataset, with its focus on computer science literature, presents a unique opportunity for research and analysis in fields such as:

• Technology Trends: Analyzing emerging trends in computer science literature, including topics, technologies, and subfields.
• Educational Insights: Investigating the popularity of computer science textbooks and learning materials.
• Author Impact: Assessing the influence and contributions of authors within the computer science domain.
• Market Analysis: Understanding pricing dynamics and factors influencing the success of computer science books.

Researchers, analysts, and computer science enthusiasts can leverage this dataset to explore the intricate world of computer science literature, uncover market trends, and contribute to a deeper understanding of the evolving landscape of technology and knowledge dissemination on one of the world's largest online marketplaces.

Enterprise-Value-To-Sales-Ratio Time Series for Amazon.com Inc. 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 also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

This dataset provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

Comprehensive dataset analyzing Amazon's daily website visits, traffic patterns, seasonal trends, and comparative analysis with other ecommerce platforms based on May 2025 data.

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.

MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs.

To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review.

The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues.

MuMu dataset (mapping, metadata, annotations and text reviews)

Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments

These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus.

NOTE: This version provides simplified files with metadata and splits.

Scientific References

Please cite the following papers if using MuMu dataset or Tartarus library.

Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).

Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Blue, red, and near-infrared reflectances, centered at 469-nanometers, 645-nanometers, and 858-nanometers, respectively, are used to determine the MODIS daily vegetation indices. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes a new Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. Global MOD13Q1 data are provided every 16 days at 250-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Lacking a 250m blue band, the EVI algorithm uses the 500m blue band to correct for residual atmospheric effects, with negligible spatial artifacts. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.

The Amazon Ecommerce dataset is available to be purchased alongside competing ecommerce players in each country. We license our data in granular or aggregate form (cheaper) on a weekly or monthly delivery frequency. We can go back in time several years and the panel is synced/ normalised as of today.

We can offer geolocation data as well as panel level demographics. Contact us for data sample or to speak.

All data comes from our proprietary consumer panel which we have access to our users email accounts (with consent). Unlike credit card data, email receipt data is granular so we can track what items are bought, at what frequency and amounts spent, geolocation data, and work out user overlap amongst different platforms.

Amazon e-receipt data comes in itemized format. Contact us for data dictionary and to find out our volume in each country.

Ebitda Time Series for Amazon.com Inc. 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 also provides AgentCore services, such as AgentCore Runtime, AgentCore Memory, AgentCore Observability, AgentCore Identity, AgentCore Gateway, AgentCore Browser, and AgentCore Code Interpreter. It serves consumers, sellers, developers, enterprises, content creators, advertisers, and employees. Amazon.com, Inc. was incorporated in 1994 and is headquartered in Seattle, Washington.

Amazon ML and DS books sales:

Overview

Introducing the "Amazon ML and DS books sales" dataset, a comprehensive collection of book-related data from various countries, providing a panoramic view of the world of literature and e-commerce. This dataset combines information on book titles, authors, prices, ratings, reviews, publishing dates, country origins, and even currency details. Whether you're a book enthusiast, data analyst, or researcher, this dataset offers a treasure trove of opportunities for analysis and exploration.

Columns

This dataset encompasses seven vital columns:

1. Title: The title of the book (object).
2. Authors: The authors of the book (object).
3. Price: The listed price of the book (float64).
4. Stars: The average rating given to the book (float64).
5. Number of Reviews: The count of reviews for the book (float64).
6. Published Date: The date when the book was published (datetime64).
7. Country: The specific country where the book is listed (object).
8. Currency: The currency in which the book's price is listed (object).

Data Details

With 8,429 records, this dataset is a substantial resource for research and analysis. It offers extensive insights into books from diverse genres and origins. Some columns may contain missing data, as indicated by the "Non-Null Count" for each column, allowing for valuable opportunities for data cleaning and enhancement.

Potential Use Cases

The "Global Bookstore Insights" dataset opens doors to a wide range of applications, including:

• Market Analysis: Explore book pricing and availability trends across countries and analyze the impact of currency.
• Author Profiling: Identify popular authors and their international reach.
• Rating Predictions: Build models to predict book ratings and recommend reads to users.
• Cultural Trends: Analyze publishing trends and literary preferences in various regions.

Data Sources

This dataset is the result of meticulous data collection efforts, involving book data from Amazon websites of multiple countries. It's intended to serve the purpose of research, education, and analysis.

We invite you to dive into this dataset, conduct your analyses, and share your discoveries with the Kaggle community. Your insights can help expand our understanding of the worldwide literary and e-commerce landscape.