{
  "format": "dataset-manifest/1",
  "slug": "adventure-and-extreme-sports-booking-platform",
  "version": 1,
  "seed": "adventure-and-extreme-sports-booking-platform:1",
  "spec": {
    "format": "dataset/1",
    "slug": "adventure-and-extreme-sports-booking-platform",
    "name": "Extreme Adventure Bookings",
    "tagline": "Connect thrill-seekers with adventure providers for unforgettable experiences.",
    "description": "Thrill-seekers embark on adrenaline-pumping adventures through a platform that connects them with various adventure providers. This business facilitates bookings for exciting activities such as rock climbing, bungee jumping, and zip-lining. Customers explore available events, browse schedules, and secure reservations while making payments and taking advantage of discount coupons. Adventure operators handle the logistics and insurance for each event, ensuring that participants have a safe and exhilarating experience. High booking volumes typically occur on weekends during peak seasons, often with a significant number of last-minute reservations, while the uptake of premium insurance options remains relatively low.\n\nThe data consists of several interrelated tables that mirror the operations of this thrilling business. The 'customers' table contains individual details about each thrill-seeker, including their preferences and past bookings. The 'events' table lists all available adventure activities, including specifics like location, scheduled times, and capacity. The 'bookings' table serves as a bridge between customers and events, capturing who booked which activity and when. 'Payments' record the financial transactions associated with each booking, detailing amounts and payment methods used. Lastly, the 'coupons' table provides information on promotional offers that customers can apply to their bookings, influencing their purchasing decisions.\n\nThis dataset answers critical questions about customer behavior, booking patterns, and revenue generation. It is perfect for practice in data analysis, allowing developers to explore trends in peak booking times, the impact of coupons on purchases, and the relationship between event types and insurance selections. By working with this synthetic data, developers can gain insights into operational efficiencies and customer engagement strategies, enhancing their skills in handling real-world datasets.",
    "domain": "hospitality",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "adventure",
      "booking",
      "sports",
      "customer",
      "provider",
      "payments",
      "coupons",
      "insurance",
      "logistics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table holds information about customers who make bookings. Each customer can have multiple bookings associated with them, linked through customer_id.",
        "rows": 200,
        "columns": {
          "customer_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."
          },
          "phone": {
            "kind": "phone",
            "description": "Contact phone number of the customer."
          },
          "preferred_activity": {
            "kind": "enum",
            "values": [
              "rock climbing",
              "bungee jumping",
              "zip-lining"
            ],
            "description": "The customer's preferred type of adventure activity."
          }
        }
      },
      "events": {
        "description": "This table lists various adventure events available for booking. Each event can have multiple bookings made by different customers.",
        "rows": 100,
        "columns": {
          "event_id": {
            "kind": "pk",
            "description": "Unique identifier for each adventure event."
          },
          "activity_type": {
            "kind": "enum",
            "values": [
              "rock climbing",
              "bungee jumping",
              "zip-lining"
            ],
            "description": "Type of adventure activity."
          },
          "schedule_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date when the event is scheduled."
          },
          "max_participants": {
            "kind": "int",
            "min": 1,
            "max": 20,
            "description": "The maximum number of participants allowed for the event."
          },
          "location": {
            "kind": "words",
            "min": 5,
            "max": 100,
            "description": "Location where the event takes place."
          }
        }
      },
      "bookings": {
        "description": "This table captures the bookings made by customers for specific events. Each booking links a customer to an event.",
        "rows": 300,
        "columns": {
          "booking_id": {
            "kind": "pk",
            "description": "Unique identifier for each booking."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.customer_id",
            "description": "Reference to the customer making the booking."
          },
          "event_id": {
            "kind": "fk",
            "ref": "events.event_id",
            "description": "Reference to the booked adventure event."
          },
          "booking_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "dayProfile": "waking",
            "description": "The date and time when the booking was made."
          },
          "payment_status": {
            "kind": "enum",
            "values": [
              "paid",
              "pending",
              "cancelled"
            ],
            "description": "The payment status of the booking."
          }
        }
      },
      "payments": {
        "description": "This table contains payment information for each booking, detailing the amounts paid and any discounts applied.",
        "rows": 300,
        "columns": {
          "payment_id": {
            "kind": "pk",
            "description": "Unique identifier for each payment."
          },
          "booking_id": {
            "kind": "fk",
            "ref": "bookings.booking_id",
            "description": "Reference to the associated booking."
          },
          "amount": {
            "kind": "decimal",
            "min": 50,
            "max": 500,
            "scale": 2,
            "description": "Total amount paid for the booking."
          },
          "coupon_code": {
            "kind": "fk",
            "ref": "coupons.code",
            "nullable": true,
            "description": "Reference to any coupon applied to the payment."
          }
        }
      },
      "coupons": {
        "description": "This table lists promotional coupons that can be applied to bookings. Each coupon has a limited validity and discount value.",
        "rows": 50,
        "columns": {
          "code": {
            "kind": "pk",
            "description": "Unique code for each coupon."
          },
          "discount_percentage": {
            "kind": "decimal",
            "min": 5,
            "max": 50,
            "scale": 2,
            "description": "Percentage discount offered by the coupon."
          },
          "valid_from": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Start date of the coupon validity."
          },
          "valid_to": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "End date of the coupon validity."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "monetary"
    ],
    "analysisQuestions": [
      "What are the peak days for bookings throughout the year?",
      "How effective are promotional coupons in increasing bookings?",
      "Which adventure activities are the most popular among customers?",
      "What is the average amount spent per booking?",
      "How does the payment status vary with booking times?"
    ],
    "suggestedQueries": [
      {
        "title": "List all bookings with customer details",
        "sql": "SELECT b.booking_id, c.full_name, b.booking_date FROM bookings b JOIN customers c ON b.customer_id = c.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count bookings per activity type",
        "sql": "SELECT e.activity_type, COUNT(b.booking_id) AS total_bookings FROM bookings b JOIN events e ON b.event_id = e.event_id GROUP BY e.activity_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find average payment amount per booking",
        "sql": "SELECT AVG(p.amount) AS average_payment FROM payments p;",
        "dialect": "postgres"
      },
      {
        "title": "Get bookings with applied coupon details",
        "sql": "SELECT b.booking_id, c.code, p.amount FROM bookings b JOIN payments p ON b.booking_id = p.booking_id LEFT JOIN coupons c ON p.coupon_code = c.code;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Business Intelligence Analysis",
        "body": "Data analysts can utilize this dataset to identify trends in adventure bookings, analyzing customer preferences and seasonal peaks. By creating visual dashboards, they can present insights to stakeholders, helping to shape marketing strategies and operational decisions."
      },
      {
        "title": "ETL Pipeline Development",
        "body": "Data engineers can leverage this synthetic dataset to practice building ETL pipelines that extract, transform, and load data into a data warehouse. They can implement data validation processes, ensuring that the booking data is accurate and reliable for further analysis."
      },
      {
        "title": "Coupon Effectiveness Testing",
        "body": "Marketing professionals can analyze the effectiveness of various promotional coupons using this dataset. By examining the correlation between coupon usage and booking rates, they can determine which offers resonate most with customers, optimizing future campaigns."
      },
      {
        "title": "Customer Behavior Analysis",
        "body": "Customer experience teams can explore the dataset to understand booking behaviors, such as last-minute reservations and preferred activities. This information can inform enhancements to the booking platform, improving user satisfaction and retention rates."
      }
    ]
  },
  "counts": {
    "coupons": 50,
    "customers": 200,
    "events": 100,
    "bookings": 300,
    "payments": 300
  },
  "stats": {
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