{
  "format": "dataset-manifest/1",
  "slug": "home-furnishings-marketplace-dataset",
  "version": 2,
  "seed": "home-furnishings-marketplace-dataset:1",
  "spec": {
    "format": "dataset/1",
    "slug": "home-furnishings-marketplace-dataset",
    "name": "Home Furnishings Marketplace Dataset",
    "tagline": "Customer profiles, order details, product listings, and supplier information from a home furnishings marketplace.",
    "description": "Customers browse an extensive selection of home furnishings, from chic sofas to elegant lighting fixtures, all designed to transform their living spaces. Orders are placed online, and each transaction records essential details about the purchased items, customer preferences, and the suppliers who provide the products. This marketplace thrives on a diverse inventory, where each product is linked to information about its supplier, including pricing and availability, ensuring that customers receive quality furnishings in a timely manner.\n\nIn this dataset, the 'customers' table includes profiles that capture essential information about shoppers, such as their preferences and purchasing behavior. The 'orders' table lists each transaction, detailing the customer ID associated with the order, the date, total amount spent, and the products included. The 'products' table provides vital information about each item for sale, including categories, descriptions, and supplier IDs. Lastly, the 'suppliers' table contains data on the businesses that manufacture or distribute the products, offering insight into their contributions and reliability. Each row in the orders table connects a specific customer with the products they purchased, while the products relate back to their suppliers, creating a network of relationships essential for understanding the marketplace dynamics.\n\nDevelopers can use this data to answer questions around customer buying patterns, item popularity, and supplier performance. Analyzing how different customers interact with varying product categories can reveal trends and help optimize inventory management. Practicing with this dataset allows for exploration of order fulfillment processes and customer satisfaction metrics. It's important to note that all data in this dataset is synthetic, designed to simulate a real-world marketplace environment without compromising real customer information.",
    "domain": "ecommerce",
    "shape": "hierarchical",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "ecommerce",
      "furniture",
      "marketplace",
      "orders",
      "customers",
      "inventory",
      "suppliers"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains information about customers, including their demographics and location. Each customer is uniquely identified and can have multiple orders associated with them.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "Email address of the customer."
          },
          "city": {
            "kind": "city",
            "description": "City where the customer resides."
          },
          "country": {
            "kind": "country",
            "from": "city",
            "description": "Country where the customer resides."
          }
        }
      },
      "orders": {
        "description": "This table records details of customer orders, including the products purchased. It links to the customers table, allowing users to analyze customer purchasing behavior.",
        "rows": {
          "per": "customers",
          "via": "customer_id",
          "min": 1,
          "max": 5,
          "skew": "heavy-tail"
        },
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each order."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Reference to the customer who placed the order."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "The date when the order was placed.",
            "dayProfile": "waking"
          },
          "total_amount": {
            "kind": "decimal",
            "min": 20,
            "max": 2000,
            "scale": 2,
            "description": "Total amount for the order.",
            "distribution": "long-tail"
          }
        }
      },
      "products": {
        "description": "This table lists products available in the marketplace, detailing their categories and inventory levels. Each product can be linked to multiple orders from the orders table.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "The name of the product."
          },
          "category": {
            "kind": "enum",
            "values": [
              "Furniture",
              "Decor",
              "Accessories"
            ],
            "description": "The category of the product.",
            "weights": [
              6,
              3,
              1
            ]
          },
          "supplier_id": {
            "kind": "fk",
            "ref": "suppliers.id",
            "description": "Reference to the supplier providing the product."
          },
          "inventory_level": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Current inventory level of the product.",
            "distribution": "long-tail"
          }
        }
      },
      "suppliers": {
        "description": "This table contains information about suppliers who provide products to the marketplace. Each supplier can supply multiple products.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each supplier."
          },
          "company_name": {
            "kind": "company",
            "description": "Name of the supplier company."
          },
          "contact_email": {
            "kind": "email",
            "from": "company_name",
            "description": "Email address for contacting the supplier."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the most popular product categories among customers?",
      "How does the average order value vary across different customer demographics?",
      "Which suppliers provide the most products, and how does this affect inventory levels?",
      "What trends can be observed in order placements over time?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Orders and Customers",
        "sql": "SELECT o.id, c.full_name, o.total_amount FROM orders o JOIN customers c ON o.customer_id = c.id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Order Totals by Customer",
        "sql": "SELECT c.full_name, SUM(o.total_amount) as total_spent FROM orders o JOIN customers c ON o.customer_id = c.id GROUP BY c.full_name;",
        "dialect": "postgres"
      },
      {
        "title": "Products Supplied by Each Supplier",
        "sql": "SELECT s.company_name, COUNT(p.id) as product_count FROM suppliers s JOIN products p ON s.id = p.supplier_id GROUP BY s.company_name;",
        "dialect": "postgres"
      },
      {
        "title": "Inventory Levels by Product Category",
        "sql": "SELECT p.category, SUM(p.inventory_level) as total_inventory FROM products p GROUP BY p.category;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Retail Analyst",
        "body": "A retail analyst can utilize this dataset to identify sales trends and customer preferences within the home furnishings market. By analyzing which product categories are most popular among different demographics, they can make informed decisions about inventory management and marketing strategies."
      },
      {
        "title": "Data Science Student",
        "body": "A data science student can use this dataset to practice SQL skills and learn about database design. By constructing queries that join multiple tables, they can gain practical experience in data manipulation and analysis, preparing them for real-world data science roles."
      },
      {
        "title": "Business Intelligence Developer",
        "body": "A business intelligence developer can leverage this data to create interactive dashboards that visualize sales performance and inventory levels. This visualization can help stakeholders quickly assess business health and make strategic decisions based on data insights."
      },
      {
        "title": "Supply Chain Manager",
        "body": "A supply chain manager can analyze supplier performance using this dataset. By examining the relationship between suppliers and product inventory levels, they can optimize sourcing strategies and ensure that inventory meets customer demand."
      }
    ]
  },
  "counts": {
    "customers": 500,
    "orders": 991,
    "suppliers": 100,
    "products": 300
  },
  "stats": {
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          "min": "Alice Hansen",
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        "email": {
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        "city": {
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          "distinct": 63,
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        "country": {
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          "distinct": 43,
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