[Caution]

This dataset is only for study, and personal portfolio. Not reccomended for a research or profitable purpose!

License: CC BY-NC-ND

Dataset Description

This dataset contains transactiononal activity in eccommrce app including product order and also customer behavior in using app. The dataset has several files represent every table

Dataset Columns

Customer Table

Containing the detailed information of registered user in ecommerce application * customer_id = customer unique id * first_name = customer's first name * last_name = customer's last name * username = customer's username * email = customer's email * gender = customer's gender (Male (M) or Female (F)) * device_type = the device type of customer when using the app * device_id = device id of customer when using app * device_version = detailed version of device used by customer * home_location_lat = customer location latitude * home_location_long =customer location longitude * home_location = customer province/region name * home_country = customer country name * first_join_date = customer first join date in app

Product Table

Containing the detailed data of product (fashion product) sold in application * id = product id * gender = target/designate products based on gender * masterCategory = Master category of product * subCategory = sub category of product * articleType = fashion product type * baseColour = base color of fashion product * season = target/designate products based on season * year = the year of production * usage = the usage type of product * productDisplayName = the display name of product in ecommerce app

Transaction Table

contains data for each transaction/product order made by the customer. Each customer can make multiple purchases on multiple products. * created_at = the timestamp when data/transaction created * customer_id = unique id of every customer * booking_id = unique id of transaction * session_id = unique session id of user when visiting the app * product_metadata = the metadata of product purchased * payment_method = the payment method used in transaction * payment_status = the payment status (Success / Failed) * promo_amount = the amount of promo in every transacation * promo_code =promo code * shipment_fee = the shipment fee of transaction (ongkir) * shipment_date_limit = the shipment limit data * shipment_location_lat = the shipment location/target latitude * shipment_location_long = the shipment location/target longitude * total_amount = total amount of money to be paid for every transaction

Click Stream Table

contains data on application usage activities carried out by users in each session or when they make a transaction * session_id = session id * event_name = the name of activity/event * event_time = the time when event occured * event_id = id of event * traffic_source = the activity source by device (mobile/web) * event_metadata = the metadata of activity / detailed activity

Notes * There is a product_metadata feature in the Transaction Table and event_metadata in the Click_Stream Table, which is in the form of a dictionary, you maybe need to extract the contents to form a new feature * subCategory parent to masterCategory * articleType is a specification of the subCategory: masterCategory => subCategory => articleType

Exploring E-commerce Trends: A Guide to Leveraging Dummy Dataset

Introduction: In the world of e-commerce, data is a powerful asset that can be leveraged to understand customer behavior, improve sales strategies, and enhance overall business performance. This guide explores how to effectively utilize a dummy dataset generated to simulate various aspects of an e-commerce platform. By analyzing this dataset, businesses can gain valuable insights into product trends, customer preferences, and market dynamics.

1. Dataset Overview: The dummy dataset contains information on 1000 products across different categories such as electronics, clothing, home & kitchen, books, toys & games, and more. Each product is associated with attributes such as price, rating, number of reviews, stock quantity, discounts, sales, and date added to inventory. This comprehensive dataset provides a rich source of information for analysis and exploration.

2. Data Analysis: Using tools like Pandas, NumPy, and visualization libraries like Matplotlib or Seaborn, businesses can perform in-depth analysis of the dataset. Key insights such as top-selling products, popular product categories, pricing trends, and seasonal variations can be extracted through exploratory data analysis (EDA). Visualization techniques can be employed to create intuitive graphs and charts for better understanding and communication of findings.

3. Machine Learning Applications: The dataset can be used to train machine learning models for various e-commerce tasks such as product recommendation, sales prediction, customer segmentation, and sentiment analysis. By applying algorithms like linear regression, decision trees, or neural networks, businesses can develop predictive models to optimize inventory management, personalize customer experiences, and drive sales growth.

4. Testing and Prototyping: Businesses can utilize the dummy dataset to test new algorithms, prototype new features, or conduct A/B testing experiments without impacting real user data. This enables rapid iteration and experimentation to validate hypotheses and refine strategies before implementation in a live environment.

5. Educational Resources: The dummy dataset serves as an invaluable educational resource for students, researchers, and professionals interested in learning about e-commerce data analysis and machine learning. Tutorials, workshops, and online courses can be developed using the dataset to teach concepts such as data manipulation, statistical analysis, and model training in the context of e-commerce.

6. Decision Support and Strategy Development: Insights derived from the dataset can inform strategic decision-making processes and guide business strategy development. By understanding customer preferences, market trends, and competitor behavior, businesses can make informed decisions regarding product assortment, pricing strategies, marketing campaigns, and resource allocation.

Conclusion: In conclusion, the dummy dataset provides a versatile and valuable resource for exploring e-commerce trends, understanding customer behavior, and driving business growth. By leveraging this dataset effectively, businesses can unlock actionable insights, optimize operations, and stay ahead in today's competitive e-commerce landscape

A dataset consisting of 751,500 English app reviews of 12 online shopping apps. The dataset was scraped from the internet using a python script. This ShoppingAppReviews dataset contains app reviews of the 12 most popular online shopping android apps: Alibaba, Aliexpress, Amazon, Daraz, eBay, Flipcart, Lazada, Meesho, Myntra, Shein, Snapdeal and Walmart. Each review entry contains many metadata like review score, thumbsupcount, review posting time, reply content etc. The dataset is organized in a zip file, under which there are 12 json files and 12 csv files for 12 online shopping apps. This dataset can be used to obtain valuable information about customers' feedback regarding their user experience of these financially important apps.

Categories

e-Commerce, Data Mining, Natural Language Processing, Feedback, User Experience

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

16,000 Zara UK Fashion Products in CSV Format

Unlock fashion retail intelligence with our comprehensive Zara UK products dataset. This premium collection contains 16,000 products from Zara's UK online store, providing detailed insights into one of the world's leading fast-fashion retailers. Perfect for fashion trend analysis, pricing strategies, competitive research, and machine learning applications.

Dataset Overview

• Language: English
• Coverage: Men's, women's, and children's fashion
• File Size: ~30MB
• Data Freshness: Recently collected (2025)

Complete Data Fields Included

Product Information

• name: Complete product titles and descriptions
• brand: Brand identification (Zara)
• category: Product categories (tops, bottoms, dresses, accessories)
• description: Detailed item descriptions and features
• composition: Fabric composition and material details
• breadcrumbs: Navigation path and product hierarchy

Pricing and Promotions

• price: Current prices in GBP
• old_price: Original prices before discounts
• discount: Discount percentages and savings
• promotions: Active promotional campaigns
• currency: GBP for UK market analysis

Product Attributes

• color: Available color variations
• sizes: Size ranges and availability
• images: High-resolution product image URLs
• url: Direct product page links

Technical Fields

• uniq_id: Unique product identifiers
• scraped_at: Data collection timestamps

Key Use Cases

Fashion Trend Analysis

• Track seasonal trends and popular styles
• Analyze color preferences and combinations
• Monitor fashion trend evolution
• Predict upcoming fashion movements

Competitive Intelligence

• Study Zara's pricing strategies
• Analyze product mix and category focus
• Monitor inventory and availability patterns
• Compare market positioning

E-commerce Analytics

• Category performance analysis
• Price optimization strategies
• Inventory planning insights
• Customer preference mapping

Machine Learning Applications

• Fashion recommendation systems
• Price prediction models
• Trend forecasting algorithms
• Image recognition training data

Data Quality Features

• Clean, Validated Data: Pre-processed and error-checked
• Consistent Formatting: Standardized structure across records
• No Duplicates: Unique products only
• Complete Coverage: Entire Zara UK catalog included
• Fresh Collection: Recently scraped for current relevance

Target Industries

Fashion Retailers

• Competitive benchmarking
• Trend adoption strategies
• Pricing optimization
• Product development insights

Technology Companies

• AI training datasets
• Fashion analytics platforms
• E-commerce enhancement
• Style recommendation engines

Market Research

• Industry analysis reports
• Brand performance tracking
• Consumer behavior studies
• Trend forecasting services

Academic Research

• Fashion industry studies
• Business case studies
• Data science applications
• Sustainability research

Licensing Options

Commercial License

• Full business usage rights
• Team sharing permissions
• Resale of processed insights
• API integration allowed

Academic License

• Non-commercial research use
• Educational institution sharing
• Publication rights included
• Discounted pricing available

Delivery Methods

• Instant

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

📦 Ecommerce Dataset (Products & Sizes Included)

🛍️ Essential Data for Building an Ecommerce Website & Analyzing Online Shopping Trends 📌 Overview This dataset contains 1,000+ ecommerce products, including detailed information on pricing, ratings, product specifications, seller details, and more. It is designed to help data scientists, developers, and analysts build product recommendation systems, price prediction models, and sentiment analysis tools.

🔹 Dataset Features

Column Name Description product_id Unique identifier for the product title Product name/title product_description Detailed product description rating Average customer rating (0-5) ratings_count Number of ratings received initial_price Original product price discount Discount percentage (%) final_price Discounted price currency Currency of the price (e.g., USD, INR) images URL(s) of product images delivery_options Available delivery methods (e.g., standard, express) product_details Additional product attributes breadcrumbs Category path (e.g., Electronics > Smartphones) product_specifications Technical specifications of the product amount_of_stars Distribution of star ratings (1-5 stars) what_customers_said Customer reviews (sentiments) seller_name Name of the product seller sizes Available sizes (for clothing, shoes, etc.) videos Product video links (if available) seller_information Seller details, such as location and rating variations Different variants of the product (e.g., color, size) best_offer Best available deal for the product more_offers Other available deals/offers category Product category

📊 Potential Use Cases

📌 Build an Ecommerce Website: Use this dataset to design a functional online store with product listings, filtering, and sorting. 🔍 Price Prediction Models: Predict product prices based on features like ratings, category, and discount. 🎯 Recommendation Systems: Suggest products based on user preferences, rating trends, and customer feedback. 🗣 Sentiment Analysis: Analyze what_customers_said to understand customer satisfaction and product popularity. 📈 Market & Competitor Analysis: Track pricing trends, popular categories, and seller performance. 🔍 Why Use This Dataset? ✅ Rich Feature Set: Includes all necessary ecommerce attributes. ✅ Realistic Pricing & Rating Data: Useful for price analysis and recommendations. ✅ Multi-Purpose: Suitable for machine learning, web development, and data visualization. ✅ Structured Format: Easy-to-use CSV format for quick integration.

📂 Dataset Format CSV file (ecommerce_dataset.csv) 1000+ samples Multi-category coverage 🔗 How to Use? Download the dataset from Kaggle. Load it in Python using Pandas: python Copy Edit import pandas as pd
df = pd.read_csv("ecommerce_dataset.csv")
df.head() Explore trends & patterns using visualization tools (Seaborn, Matplotlib). Build models & applications based on the dataset!

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

We'll customize a Wildberries dataset to align with your unique requirements, incorporating data on product categories, customer reviews, pricing trends, popular items, demographic insights, sales figures, and other relevant metrics. Leverage our Wildberries datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and online shopping trends, facilitating refined product offerings and marketing campaigns. Tailor your access to the complete dataset or specific subsets according to your business needs. Popular use cases include conducting competitor analysis to understand market positioning, monitoring brand reputation through consumer feedback, and performing consumer market analysis to identify and predict emerging trends in e-commerce and online retail.

This dataset was collected from a large survey and several public datasets focusing on rural areas in Bavaria, Germany. The research was conducted in Bavaria due to its advanced technology infrastructure, diverse economy, and ongoing rural e-commerce development initiatives. The data sources include government statistics, rural development projects, and local e-commerce and infrastructural organizations. The dataset is designed to assess and analyze key variables influencing rural e-commerce growth.

The dataset spans five years of trends and indicators, providing a rich foundation for machine learning studies. Its multidimensional nature allows for the application of sophisticated models to uncover the primary drivers of e-commerce success in rural areas. The dataset has been validated using multiple independent sources to ensure its reliability and accuracy.

Features: The dataset includes the following features:

Economic Factors:

Household_Income: Annual household income in the rural region. Employment_Rate: Proportion of the rural population employed. Agricultural_Productivity: Measure of agricultural output in the region. Tech_Expenditure: Spending on technology-related products and services. Technological Factors:

Internet_Penetration: Percentage of the population with access to the internet. Smartphone_Usage: Percentage of the population owning and using smartphones. Ecommerce_Awareness: Awareness of e-commerce platforms within the rural community. Infrastructure Factors:

Road_Connectivity: Quality and availability of road infrastructure. Warehouse_Proximity: Average distance to major warehouses. Electricity_Availability: Hours of electricity availability per day. Logistics_Performance: Efficiency of logistics and supply chains. Social and Cultural Factors:

Literacy_Rate: Proportion of the population that is literate. Gender_Equality_Index: Index representing gender parity in the region. Trust_in_Online_Transactions: Level of trust in online transactions and e-commerce. E-Commerce Adoption Metrics:

Ecommerce_Growth: Growth rate of e-commerce activity in the region. Average_Order_Value: Average order value of e-commerce transactions. Repeat_Customer_Rate: Percentage of repeat customers for e-commerce platforms. Policy and Support Indicators:

Subsidy_Accessibility: Availability of government subsidies to support e-commerce. Skill_Program_Availability: Availability of training programs related to e-commerce. Labels: Priority_Score: A regression label calculated from various factors including household income, internet penetration, and logistics performance. Priority_Level: A categorical label indicating the priority level for e-commerce development in the region, categorized into Low, Medium, and High. This dataset is suitable for applications in e-commerce prediction, rural development studies, and machine learning models targeting the optimization of e-commerce readiness in rural areas.

Massive eBay Marketplace Data Collection for E-commerce Intelligence

Unlock the power of online marketplace analytics with our comprehensive eBay products dataset. This premium collection contains 1.29 million products from eBay's global marketplace, providing extensive insights into one of the world's largest e-commerce platforms. Perfect for competitive analysis, pricing strategies, market research, and machine learning applications in e-commerce.

Dataset Overview

• Total Products: 1,290,000+ marketplace listings
• Source: eBay Global Marketplace
• Format: CSV, ZIP compressed
• File Size: Optimized compressed format
• Coverage: Multi-category product listings across eBay

Complete Data Fields Included

Product Identification

• id: Unique eBay product identifiers
• name: Complete product titles and names
• url: Direct eBay listing page links
• epid: eBay Product ID for catalog matching
• source: Data source identification

Product Details

• raw_product_description: Original unprocessed product descriptions
• product_description: Cleaned and formatted product descriptions
• brand: Brand names and manufacturer information
• mpn: Manufacturer Part Numbers
• gtin13: Global Trade Item Numbers (barcodes)

Pricing and Availability

• price: Current listing prices
• currency: Currency information for international listings
• in_stock: Stock availability status
• breadcrumbs: Category navigation paths

Visual and Technical Data

• images: Product image URLs and references
• crawled_at: Data collection timestamps

Key Use Cases

E-commerce Market Research

• Analyze eBay marketplace trends and patterns
• Study product category performance
• Monitor pricing strategies across sellers
• Identify high-demand product categories

Competitive Intelligence

• Benchmark pricing against eBay marketplace
• Analyze product positioning strategies
• Study seller competition and market share
• Monitor inventory levels and availability

Price Optimization

• Develop dynamic pricing algorithms
• Analyze price elasticity across categories
• Compare marketplace pricing trends
• Optimize listing prices for maximum visibility

Machine Learning Applications

• Train recommendation systems
• Develop price prediction models
• Create product categorization algorithms
• Build inventory forecasting systems

Target Industries

E-commerce Retailers

• Marketplace Strategy: Optimize eBay selling strategies
• Pricing Intelligence: Competitive price monitoring
• Product Research: Identify profitable product opportunities
• Inventory Planning: Demand forecasting and stock optimization

Technology Companies

• AI Training Data: Machine learning model development
• Analytics Platforms: E-commerce intelligence tools
• Price Comparison: Marketplace comparison services
• Search Enhancement: Product discovery optimization

Market Research Firms

• Industry Reports: E-commerce marketplace analysis
• Consumer Behavior: Online shopping pattern studies
• Brand Monitoring: Brand performance tracking
• Trend Analysis: Market trend identification

Academic Research

• E-commerce Studies: Online marketplace research
• Business Intelligence: Retail analytics case studies
• Data Science Projects: Large-scale dataset analysis
• Economic Research: Digital marketplace economics

Data Quality Features

• Comprehensive Coverage: 1.29 million unique products
• Rich Metadata: Complete product information included
• Validated Data: Quality-checked and processed
• Structured Format: Ready-to-use CSV format
• Global Scope: International marketplace coverage

Here are a few use cases for this project:

1. E-commerce Catalog Categorization: Online retail platforms can use the 'al items' model to automatically classify and tag images of products within the decor category such as stockings, ribbons, tree skirts, etc. This will aid in enhancing product searching and filtering, improving overall user experience.

2. Interior Design Planning: The model could be used in apps or software that help its users visualize and plan their interior design. By identifying different decor items in real or virtual spaces, it can provide suggestions for improvement or create a shopping list.

3. Automated Retail Inventory Management: Retail stores can utilize this model to scan their inventory, keeping track of decor items. This would automate the process of inventory management and decrease human errors.

4. Augmented Reality Shopping Apps: AR shopping apps can use this model to recognize decor items at the user's home and suggest similar or matching products from their inventory. It could help to personalize the shopping experience.

5. Social Media Advertising: Businesses could use this model to monitor user-uploaded images on social media, identify their product's usage or preference and accordingly run targeted advertising campaigns.

E-Commerce Dataset: Products, Customers, and Trends

Description

This dataset provides a comprehensive view of an e-commerce platform, featuring detailed information about products, customers, pricing, and sales trends. It is designed for data analysis, machine learning, and insights into online retail operations. The dataset is structured to help researchers and analysts explore various aspects of e-commerce, such as product popularity, customer preferences, and shipping performance.

Columns and Their Descriptions

• Product ID: Unique identifier for each product.
• Product Name: The name or title of the product listed in the catalog.
• Category: The category or type of the product (e.g., Electronics, Clothing, Home Decor).
• Price: The price of the product in USD.
• Discount: The discount applied to the product as a percentage of the original price.
• Tax Rate: The applicable tax rate for the product as a percentage.
• Stock Level: The number of units currently available in inventory.
• Supplier ID: A unique identifier for the supplier of the product.
• Customer Age Group: The age group of customers who frequently purchase this product (e.g., Teens, Adults, Seniors).
• Customer Location: The geographical location of customers (e.g., Country, State, or City).
• Customer Gender: The gender(s) of customers most likely to purchase this product (e.g., Male, Female, Both).
• Shipping Cost: The cost of shipping the product in USD.
• Shipping Method: The method of shipping used (e.g., Standard, Express, Overnight).
• Return Rate: The percentage of orders for this product that are returned by customers.
• Seasonality: The season(s) during which the product is most popular (e.g., Winter, Summer, All-Year).
• Popularity Index: A score indicating the product's popularity on a scale of 0 to 100.

Use Cases

This dataset is ideal for: - Exploratory Data Analysis (EDA): Analyze sales trends, product popularity, and customer preferences. - Visualization: Create insightful charts to visualize product performance, regional sales, and shipping trends. - Customer Insights: Understand customer segmentation based on demographics, preferences, and location. - Machine Learning Applications: - Regression: Predict product popularity based on price, discount, and stock level. - Clustering: Identify similar product categories for targeted marketing. - Classification: Predict whether a product will be returned based on its features.

Sample Data

NoSQL Database Market was valued at $9.38 Billion in 2023, and is projected to reach $USD 86.48 Billion by 2032, at a CAGR of 28% from 2023 to 2032.

Explore the POS transactions dataset in Saudi Arabia, including data on mobile transactions, sales using cards, e-commerce transactions, and more. Analyze the No. of Transactions, Total POS, and Sales in Thousand Saudi Riyals to gain insights into the country's payment trends.

POS Using Near Field Communication Technology, No. of Mobile Transactions, Total POS, No. of Transactions, Sales Using Cards in Thousand Saudi Riyals, Sales in Thousand Saudi Riyals, Sales Using Mobile in Thousand Saudi Riyals, E-Commerce Transactions Using Mada Cards, No. of Cards Transactions, No. of Points of Sale Terminals, E-Commerce Transactions Using Mada Cards, Sales, Transactions, POS, Money, Bank, SAMA Monthly

Saudi Arabia Follow data.kapsarc.org for timely data to advance energy economics research..- Sales In Thousand Riyals- End of Period-Including transactions of mada cards through online shopping sites and in-app purchases. It does not include transactions by Visa, MasterCard and other credit cards.

SQL In-Memory Database Market size was valued at USD 9.26 Billion in 2024 and is projected to reach USD 35.7 Billion by 2032, growing at a CAGR of 20.27% from 2026 to 2032.

SQL In-Memory Database Market Drivers

Demand for Real-Time Analytics and Processing: Businesses increasingly require real-time insights from their data to make faster and more informed decisions. SQL In-Memory databases excel at processing data much faster than traditional disk-based databases, enabling real-time analytics and operational dashboards.

Growth of Big Data and IoT Applications: The rise of Big Data and the Internet of Things (IoT) generates massive amounts of data that needs to be processed quickly. SQL In-Memory databases can handle these high-velocity data streams efficiently due to their in-memory architecture.

Improved Performance for Transaction Processing Systems (TPS): In-memory databases offer significantly faster query processing times compared to traditional databases. This translates to improved performance for transaction-intensive applications like online banking, e-commerce platforms, and stock trading systems.

Reduced Hardware Costs (in some cases): While implementing an in-memory database might require an initial investment in additional RAM, it can potentially reduce reliance on expensive high-performance storage solutions in specific scenarios.

Focus on User Experience and Application Responsiveness: In today's digital landscape, fast and responsive applications are crucial. SQL In-Memory databases contribute to a smoother user experience by enabling quicker data retrieval and transaction processing.

However, it's important to consider some factors that might influence market dynamics:

Limited Data Capacity: In-memory databases are typically limited by the amount of available RAM, making them less suitable for storing massive datasets compared to traditional disk-based solutions.

Higher Implementation Costs: Setting up and maintaining an in-memory database can be more expensive due to the additional RAM requirements compared to traditional databases.

Hybrid Solutions: Many organizations opt for hybrid database solutions that combine in-memory and disk-based storage, leveraging the strengths of both for different data sets and applications.

Cart abandonment rates have been climbing steadily since 2014, after reaching an all-time high in 2013. In 2023, the share of online shopping carts that is being abandoned reached 70 percent for the first time since 2013. This is an increase of more than 10 percentage points compared to the start of the time period considered here. Mobiles vs. desktops When global consumers shop online, they spend considerably more when doing so on desktop computers. In December 2023, the average value of e-commerce purchases made through desktops was approximately 159 U.S. dollars. Purchases completed on mobiles and tablets were of comparable values, ranging between 100 and 105 U.S. dollars. Even though consumers spent more when conducting their shopping on computers, they were more inclined to add products to their shopping carts when using mobile devices. Ultimately, mobile devices provide a convenient and more accessible way to shop, but desktop computers remain the preferred choice for more expensive purchases. Where do consumers shop online? Across the globe, digital marketplaces are shoppers’ number-one online shopping destination. As of April 2024, some 29 percent of consumers voted marketplaces as their favorite e-commerce channel, followed by physical stores and retailer sites. Looking at which retailers’ global shoppers prefer to shop at, amazon.com emerged as the world's most popular online marketplace, based on share of visits. The U.S. portal accounted for around one-fifth of the global online marketplace's traffic in December 2023. Amazon's German and Japanese portal sites ranked third and fifth among the leading online marketplaces, further demonstrating Amazon's dominance over the market.

Get access to the Walmart Basic Product Details Dataset, which includes essential information on a wide range of products available at Walmart.

This comprehensive dataset features product names, categories, descriptions, prices, and more. Ideal for market analysis, competitive research, and e-commerce applications.

Download now to enhance your data-driven strategies and insights with detailed Walmart product information.

The dataset having basic details of a dataset like title, id, image, price and descripton.

Records count: 2.5 million +

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.