{
  "format": "dataset-manifest/1",
  "slug": "ecommerce-transactions-sample-data",
  "version": 2,
  "seed": "ecommerce-transactions-sample-data:1",
  "spec": {
    "format": "dataset/1",
    "slug": "ecommerce-transactions-sample-data",
    "name": "Ecommerce Transactions Sample Data",
    "tagline": "Customer orders, product variants, and sales channels from an ecommerce platform.",
    "description": "Customers navigate a vibrant online marketplace, selecting products, adding them to their carts, and finalizing purchases through diverse shipping options. This dataset centers on customer orders, illustrating how transactions occur within the ecommerce environment. Each order represents a customer's choice, reflecting their preferences, the products they select, and the channels they utilize to make their purchases.\n\nThe primary table in this dataset is the 'orders' table, which captures essential details about each transaction. Each row corresponds to a unique order, recording information such as order IDs, customer details, product variants, quantities, and shipping methods. This structure allows developers to understand individual purchases and the varying factors influencing each transaction. By analyzing this data, one can observe patterns in purchasing behavior, such as popular product variants, common combinations of items in orders, and the impact of shipping methods on customer satisfaction.\n\nThis dataset is particularly useful for practicing data manipulation, analysis, and visualization. Developers can explore questions like: What are the trends in order volume over time? Which product variants are frequently purchased together? How do shipping methods affect order fulfillment times? Such inquiries help in honing skills related to data analysis, as well as in gaining insights into customer behavior within an ecommerce context. Keep in mind that this data is synthetic, generated to provide a realistic environment for practice without the constraints of real-world data privacy issues.",
    "domain": "ecommerce",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "ecommerce",
      "transactions",
      "orders",
      "products",
      "sales",
      "customers",
      "shipping"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "orders": {
        "description": "Each row in this table represents a customer order, detailing the items purchased and their associated attributes. Orders are linked to various products and shipping methods, capturing the overall purchasing experience.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each order."
          },
          "customer_id": {
            "kind": "int",
            "min": 1,
            "max": 1000,
            "description": "Identifier for the customer placing the order."
          },
          "product_variant_id": {
            "kind": "int",
            "min": 1,
            "max": 500,
            "description": "Identifier for the specific product variant ordered."
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Number of items ordered, usually between 1 and 5.",
            "distribution": "long-tail"
          },
          "total_price": {
            "kind": "decimal",
            "min": 5,
            "max": 500,
            "scale": 2,
            "description": "Total price for the order, including all items.",
            "distribution": "long-tail"
          },
          "shipping_method": {
            "kind": "enum",
            "values": [
              "standard",
              "express",
              "overnight"
            ],
            "weights": [
              0.7,
              0.2,
              0.1
            ],
            "description": "Shipping method selected for the order."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the order was placed.",
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "monetary",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average order value across all transactions?",
      "How do shipping methods affect order totals?",
      "What is the distribution of order quantities among customers?",
      "Which products have the highest sales volume?",
      "What trends can be observed in order volumes over time?"
    ],
    "suggestedQueries": [
      {
        "title": "Retrieve all orders placed by a specific customer",
        "sql": "SELECT * FROM orders WHERE customer_id = 123;",
        "dialect": "postgres"
      },
      {
        "title": "Calculate the total revenue generated from all orders",
        "sql": "SELECT SUM(total_price) AS total_revenue FROM orders;",
        "dialect": "postgres"
      },
      {
        "title": "Find the average quantity of items per order",
        "sql": "SELECT AVG(quantity) AS average_quantity FROM orders;",
        "dialect": "postgres"
      },
      {
        "title": "Group orders by shipping method and calculate total revenue",
        "sql": "SELECT shipping_method, SUM(total_price) AS revenue FROM orders GROUP BY shipping_method;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Ecommerce Analysts",
        "body": "Data analysts working in ecommerce can use this dataset to create dashboards that visualize sales performance, identify trends in customer purchasing behaviors, and assess the effectiveness of different shipping methods."
      },
      {
        "title": "Developers for ORM Seeding",
        "body": "Software developers building ecommerce platforms can utilize this dataset to seed their databases with realistic transaction data, which helps in testing features and ensuring that their applications handle various order scenarios effectively."
      },
      {
        "title": "Marketing Teams",
        "body": "Marketing professionals can analyze this dataset to understand customer preferences, identify popular products, and strategize marketing campaigns based on purchasing trends and seasonal spikes in order volumes."
      }
    ]
  },
  "counts": {
    "orders": 500
  },
  "stats": {
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        "customer_id": {
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          "distinct": 402,
          "min": 6,
          "max": 997
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        "product_variant_id": {
          "nulls": 0,
          "distinct": 313,
          "min": 3,
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        "quantity": {
          "nulls": 0,
          "distinct": 5,
          "min": 1,
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        "total_price": {
          "nulls": 0,
          "distinct": 493,
          "min": 5,
          "max": 494.42
        },
        "shipping_method": {
          "nulls": 0,
          "distinct": 3,
          "min": "express",
          "max": "standard"
        },
        "order_date": {
          "nulls": 0,
          "distinct": 500,
          "min": "2022-01-01 09:19:16",
          "max": "2023-12-27 17:39:04"
        }
      }
    }
  },
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