{
  "format": "dataset-manifest/1",
  "slug": "blockchain-based-secure-e-commerce-transactions",
  "version": 1,
  "seed": "blockchain-based-secure-e-commerce-transactions:1",
  "spec": {
    "format": "dataset/1",
    "slug": "blockchain-based-secure-e-commerce-transactions",
    "name": "Secure E-Commerce Transactions",
    "tagline": "E-commerce transactions including customers, orders, products, shipments, and reviews for secure online shopping.",
    "description": "Consumers browse through an extensive online catalog, carefully selecting items while relying on secure transaction protocols to complete their purchases. This business thrives on ensuring that every customer interaction is smooth, from the moment they click 'buy' to the time the product arrives at their doorstep. The operations are driven by a variety of essential elements, including customer accounts, product listings, order processing, shipment tracking, and user reviews that provide feedback on the shopping experience. Each customer's journey is uniquely recorded, highlighting their preferences and behaviors as they navigate through various products, whether everyday essentials or luxury items.\n\nIn this dataset, the 'customers' table records individual shopper information, detailing their unique IDs, names, and contact details, while the 'products' table stores data on the items available for purchase, including product IDs, descriptions, and prices. The 'orders' table links customers to the products they select, capturing order IDs, the customer ID, product IDs, and transaction dates. This creates a clear relationship where each order is tied directly to a unique customer and one or more products.\n\nThe 'shipments' table tracks the fulfillment of orders, documenting shipment IDs, order IDs, and delivery statuses, allowing insights into logistics and efficiency. Finally, the 'reviews' table connects customers’ feedback to specific products, capturing ratings and comments that reflect customer satisfaction. This interconnected structure enables complex queries that reveal trends, such as which products are most frequently reviewed or which orders experience delays in shipment.\n\nThrough this synthetic dataset, developers can practice answering questions about customer purchasing behavior, order fulfillment efficiency, and product popularity. Analyzing these tables offers valuable insights into the e-commerce landscape, supporting a deeper understanding of consumer interactions and operational workflows.",
    "domain": "ecommerce",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "ecommerce",
      "transactions",
      "orders",
      "customers",
      "products",
      "reviews",
      "shipments",
      "coupons"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "Each row represents a customer in the e-commerce platform, linking to their orders and reviews.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "Full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the customer."
          },
          "registration_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-01-01",
            "description": "Date when the customer registered."
          }
        }
      },
      "orders": {
        "description": "This table contains details of each order placed by customers, linking to customer information.",
        "rows": 600,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each order."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "ID of the customer who placed the order."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "dayProfile": "business",
            "description": "Date and time when the order was placed."
          },
          "total_amount": {
            "kind": "decimal",
            "min": 10,
            "max": 500,
            "scale": 2,
            "description": "Total amount for the order."
          },
          "coupon_code": {
            "kind": "words",
            "max": 10,
            "nullable": true,
            "description": "Optional coupon code used for the order."
          }
        }
      },
      "products": {
        "description": "Each row represents a product available for purchase, linking to orders and reviews.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "Name of the product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "everyday",
              "luxury"
            ],
            "description": "Category of the product."
          },
          "price": {
            "kind": "decimal",
            "min": 5,
            "max": 1000,
            "scale": 2,
            "description": "Price of the product."
          }
        }
      },
      "shipments": {
        "description": "Tracks the shipment details for each order, linking back to the orders table.",
        "rows": 600,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each shipment."
          },
          "order_id": {
            "kind": "fk",
            "ref": "orders.id",
            "description": "ID of the order being shipped."
          },
          "shipped_date": {
            "kind": "datetime",
            "after": "order_id.order_date",
            "withinDays": 10,
            "description": "Date when the order was shipped."
          },
          "delivery_status": {
            "kind": "enum",
            "values": [
              "pending",
              "shipped",
              "delivered",
              "returned"
            ],
            "description": "Current status of the shipment."
          }
        }
      },
      "reviews": {
        "description": "Captures customer reviews for products, linking back to both products and customers.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each review."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "ID of the customer who submitted the review."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "ID of the product being reviewed."
          },
          "rating": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Rating given by the customer."
          },
          "review_text": {
            "kind": "paragraph",
            "max": 500,
            "nullable": true,
            "description": "Optional text review provided by the customer."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "monetary"
    ],
    "analysisQuestions": [
      "What are the most popular product categories among customers?",
      "How does coupon usage affect the total amount of orders?",
      "What is the average shipping time for orders?",
      "How do customer ratings vary by product category?",
      "What delivery status is most commonly reported?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Orders and Customers",
        "sql": "SELECT orders.id, customers.full_name FROM orders JOIN customers ON orders.customer_id = customers.id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Total Amount by Customer",
        "sql": "SELECT customer_id, SUM(total_amount) AS total_spent FROM orders GROUP BY customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Shipping Time",
        "sql": "SELECT AVG(shipped_date - order_id.order_date) AS average_shipping_time FROM shipments JOIN orders ON shipments.order_id = orders.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Reviews by Product",
        "sql": "SELECT product_id, COUNT(*) AS review_count FROM reviews GROUP BY product_id;",
        "dialect": "postgres"
      },
      {
        "title": "Find High-Value Customers",
        "sql": "SELECT customer_id, SUM(total_amount) AS total_spent FROM orders GROUP BY customer_id HAVING total_spent > 1000;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "E-Commerce Analysis Tool",
        "body": "Data analysts at an e-commerce company can use this dataset to analyze customer purchasing patterns, order fulfillment times, and the impact of promotions on sales. By running queries, they can gain insights that help optimize marketing strategies and improve customer satisfaction."
      },
      {
        "title": "ORM Seeding for E-Commerce Applications",
        "body": "Developers working on e-commerce applications can use this synthetic dataset to seed their databases with realistic data. This enables them to test various functionalities such as order processing, customer feedback, and shipment tracking in a controlled environment."
      },
      {
        "title": "Customer Experience Research",
        "body": "Researchers studying consumer behavior can leverage this dataset to investigate how factors like product category and coupon use affect customer satisfaction and purchasing decisions. The insights gained can inform better product offerings and promotional strategies."
      },
      {
        "title": "Logistics and Supply Chain Management",
        "body": "Logistics managers can analyze shipment data to identify bottlenecks in the delivery process. By assessing average shipping times and delivery statuses, they can improve operational efficiency and enhance the overall customer experience."
      }
    ]
  },
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    "customers": 200,
    "orders": 600,
    "products": 300,
    "reviews": 400,
    "shipments": 600
  },
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