{
  "format": "dataset-manifest/1",
  "slug": "event-and-activity-bookings-database",
  "version": 1,
  "seed": "event-and-activity-bookings-database:1",
  "spec": {
    "format": "dataset/1",
    "slug": "event-and-activity-bookings-database",
    "name": "Event Booking Insights",
    "tagline": "Bookings, ticket sales, customer feedback, and activities from various events in the hospitality sector.",
    "description": "Event organizers juggle numerous tasks to create unforgettable experiences for guests. They plan diverse activities, manage bookings, and ensure customer satisfaction through feedback. Within this dynamic environment, large concerts might draw in thousands, while intimate workshops cater to smaller audiences, highlighting a varied landscape of customer engagement. This dataset reflects these operations by providing insights into events, activities, bookings, ticket sales, and customer feedback.\n\nThe tables in this dataset interact in several meaningful ways. Each row in the 'events' table represents a unique event, linked to multiple activities detailed in the 'activities' table. Bookings for these events are captured in the 'bookings' table, which connects to both events and ticket sales, allowing for a comprehensive view of attendee participation and revenue generation. Feedback collected from attendees can be tied back to specific bookings, offering valuable insights into customer satisfaction and areas for improvement.\n\nBy exploring this data, developers can answer critical questions such as: How do ticket sales vary across different types of events? What activities generate the most customer engagement? How does feedback correlate with booking volumes? This dataset serves as an excellent resource for practicing data analysis, providing a rich ground for querying and visualizing customer behavior, understanding revenue trends, and assessing overall event performance. Keep in mind that the data is synthetic, crafted to simulate real-world patterns in the hospitality sector.",
    "domain": "hospitality",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "event bookings",
      "hospitality data",
      "customer feedback",
      "ticket sales",
      "activity management"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "events": {
        "description": "This table contains information about various events that are organized, including details such as event type and dates.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each event."
          },
          "name": {
            "kind": "fullName",
            "description": "The name of the event."
          },
          "event_type": {
            "kind": "enum",
            "values": [
              "concert",
              "workshop",
              "seminar",
              "conference",
              "festival"
            ],
            "description": "The type of event."
          },
          "start_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the event starts.",
            "trend": "growing"
          },
          "end_date": {
            "kind": "date",
            "from": "2023-01-02",
            "to": "2024-01-01",
            "description": "Date when the event ends.",
            "trend": "growing"
          }
        }
      },
      "activities": {
        "description": "This table lists various activities associated with each event, detailing the nature of each activity.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each activity."
          },
          "event_id": {
            "kind": "fk",
            "ref": "events.id",
            "description": "Reference to the event the activity belongs to."
          },
          "activity_name": {
            "kind": "fullName",
            "description": "Name of the activity."
          },
          "duration": {
            "kind": "int",
            "min": 30,
            "max": 240,
            "description": "Duration of the activity in minutes."
          }
        }
      },
      "bookings": {
        "description": "This table records information about bookings made for events, including booking dates and customer details.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each booking."
          },
          "event_id": {
            "kind": "fk",
            "ref": "events.id",
            "description": "Reference to the booked event."
          },
          "booking_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the booking was made.",
            "trend": "seasonal"
          },
          "customer_name": {
            "kind": "fullName",
            "description": "Name of the customer who made the booking."
          },
          "ticket_count": {
            "kind": "int",
            "min": 1,
            "max": 10,
            "description": "Number of tickets booked."
          }
        }
      },
      "feedback": {
        "description": "This table captures customer feedback for events, providing insights into customer satisfaction.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each feedback entry."
          },
          "booking_id": {
            "kind": "fk",
            "ref": "bookings.id",
            "description": "Reference to the booking associated with the feedback."
          },
          "rating": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Rating given by the customer."
          },
          "comments": {
            "kind": "words",
            "min": 5,
            "max": 200,
            "description": "Customer comments regarding their experience."
          }
        }
      },
      "ticket_sales": {
        "description": "This table tracks ticket sales for events, providing insights into revenue generation.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each sales record."
          },
          "event_id": {
            "kind": "fk",
            "ref": "events.id",
            "description": "Reference to the event for which tickets were sold."
          },
          "sale_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the ticket sale occurred.",
            "trend": "seasonal"
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 500,
            "scale": 2,
            "description": "Total amount from the ticket sales."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "monetary"
    ],
    "analysisQuestions": [
      "What types of events generate the highest ticket sales?",
      "How does customer feedback vary across different event types?",
      "Which events have the most bookings and what is their average rating?",
      "What is the trend in ticket sales over the year?",
      "How many activities are associated with each event?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Events with Bookings",
        "sql": "SELECT e.name, b.customer_name FROM events e JOIN bookings b ON e.id = b.event_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Rating by Event Type",
        "sql": "SELECT e.event_type, AVG(f.rating) AS average_rating FROM events e JOIN bookings b ON e.id = b.event_id JOIN feedback f ON b.id = f.booking_id GROUP BY e.event_type;",
        "dialect": "postgres"
      },
      {
        "title": "Total Ticket Sales per Event",
        "sql": "SELECT e.name, SUM(ts.amount) AS total_sales FROM events e JOIN ticket_sales ts ON e.id = ts.event_id GROUP BY e.name;",
        "dialect": "postgres"
      },
      {
        "title": "Feedback Trends Over Time",
        "sql": "SELECT DATE(b.booking_date) AS booking_day, AVG(f.rating) AS average_rating FROM bookings b JOIN feedback f ON b.id = f.booking_id GROUP BY booking_day ORDER BY booking_day;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Event Organizer Analysis",
        "body": "Event organizers can utilize this dataset to analyze the performance of their events and activities. By examining ticket sales and customer feedback, they can identify trends and make data-driven decisions to improve future events. For instance, they may discover that certain types of events consistently receive higher ratings, allowing them to tailor their offerings accordingly."
      },
      {
        "title": "Marketing Strategy Development",
        "body": "Marketing teams can leverage insights from this dataset to refine their strategies for promoting events. By understanding which events attract the most bookings and generate the highest revenue, they can develop targeted marketing campaigns that focus on successful event types. This can lead to more effective utilization of marketing budgets and improved return on investment."
      },
      {
        "title": "Customer Experience Improvement",
        "body": "Customer experience teams can analyze feedback data to identify areas for improvement across different events and activities. By understanding customer sentiments and ratings, they can implement changes that enhance the overall experience for attendees, leading to increased satisfaction and repeat bookings."
      },
      {
        "title": "Financial Performance Tracking",
        "body": "Finance teams can use this dataset to monitor the financial performance of events over time. By analyzing ticket sales and revenue generation, they can create forecasts and budgets that help ensure the financial viability of future events. This analysis can also assist in identifying any seasonal trends in sales."
      }
    ]
  },
  "counts": {
    "events": 100,
    "activities": 200,
    "bookings": 500,
    "feedback": 300,
    "ticket_sales": 100
  },
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