{
  "format": "dataset-manifest/1",
  "slug": "auto-showroom",
  "version": 3,
  "seed": "auto-showroom:1",
  "spec": {
    "format": "dataset/1",
    "slug": "auto-showroom",
    "name": "Auto Dealership Transactions",
    "tagline": "Transactions, customers, vehicles, services, loans, and suppliers from an auto dealership.",
    "description": "An auto showroom connects customers with the vehicles and services they need, facilitating everything from new car purchases to maintenance requests. This operation thrives on tracking customer interactions, managing vehicle inventories, processing loan applications, and overseeing various services, while also coordinating with suppliers who provide essential parts and components. Each day, staff members assist customers in selecting from a wide range of car models, ensuring they find the perfect fit for their needs, whether it be a popular sedan or a specialized vehicle. Service requests often revolve around routine maintenance, showcasing the regularity of these interactions and the importance of customer satisfaction in fostering long-term relationships.\n\nThe dataset consists of several interconnected tables: 'customers' holds information about the individuals purchasing or servicing vehicles; 'vehicles' lists the different car models available; 'transactions' captures the details of purchases and service appointments; 'services' outlines the types of maintenance or repairs offered; 'loans' documents the financing options available to customers; and 'suppliers' provides insights into the various companies supplying parts and accessories. A single row in the 'customers' table represents one individual, while a row in the 'vehicles' table reflects a specific model. These can be joined through the 'transactions' table, which relates customers to their chosen vehicles and the services they have utilized.\n\nThis dataset answers a variety of questions relevant to the auto dealership's operations, such as identifying customer preferences, analyzing transaction trends, and evaluating service performance. Practitioners can explore how customer demographics correlate with vehicle choices or how service frequency affects customer loyalty. This dataset is synthetic, providing a safe environment for developers and analysts to sharpen their skills without the complexities and privacy concerns associated with real-world data.",
    "domain": "hospitality",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "auto dealership",
      "transactions",
      "customers",
      "vehicles",
      "services",
      "loans",
      "suppliers"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains customer information, linking each customer to their transactions and requests. Each row represents an individual customer.",
        "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."
          },
          "phone": {
            "kind": "phone",
            "description": "Contact phone number of the customer."
          },
          "city": {
            "kind": "city",
            "description": "City where the customer resides.",
            "nullable": true,
            "when": {
              "column": "loyalty_status",
              "in": [
                "silver",
                "gold",
                "platinum"
              ]
            }
          },
          "loyalty_status": {
            "kind": "enum",
            "values": [
              "bronze",
              "silver",
              "gold",
              "platinum"
            ],
            "description": "Loyalty status of the customer.",
            "nullable": true,
            "weights": [
              8,
              5,
              2,
              1
            ]
          }
        }
      },
      "vehicles": {
        "description": "This table captures the vehicle models available at the dealership, linking to transactions and services. Each row represents a distinct vehicle model.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each vehicle model."
          },
          "model_name": {
            "kind": "productName",
            "description": "Name of the vehicle model."
          },
          "manufacturer": {
            "kind": "company",
            "description": "Manufacturer of the vehicle."
          },
          "price": {
            "kind": "decimal",
            "min": 20000,
            "max": 100000,
            "scale": 2,
            "description": "Price of the vehicle model.",
            "nullable": true,
            "when": {
              "column": "category",
              "in": [
                "SUV",
                "truck",
                "specialty"
              ]
            },
            "distribution": "long-tail"
          },
          "category": {
            "kind": "enum",
            "values": [
              "sedan",
              "SUV",
              "truck",
              "specialty"
            ],
            "description": "Category of the vehicle.",
            "nullable": true,
            "weights": [
              6,
              4,
              3,
              1
            ]
          }
        }
      },
      "transactions": {
        "description": "This table records each sale transaction, linking customers to the vehicles they purchase. Each row represents a sale.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Customer who made the purchase."
          },
          "vehicle_id": {
            "kind": "fk",
            "ref": "vehicles.id",
            "description": "Vehicle purchased in the transaction."
          },
          "transaction_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date of the transaction.",
            "trend": "seasonal"
          },
          "amount": {
            "kind": "decimal",
            "min": 20000,
            "max": 100000,
            "scale": 2,
            "description": "Total amount of the transaction.",
            "distribution": "long-tail"
          }
        }
      },
      "services": {
        "description": "This table details the services provided to customers, linking back to their transactions. Each row represents a service request.",
        "rows": 250,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each service record."
          },
          "transaction_id": {
            "kind": "fk",
            "ref": "transactions.id",
            "description": "Transaction associated with the service."
          },
          "service_type": {
            "kind": "enum",
            "values": [
              "maintenance",
              "repair",
              "cleaning"
            ],
            "description": "Type of service provided.",
            "weights": [
              7,
              2,
              1
            ]
          },
          "service_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date when the service was rendered.",
            "nullable": true,
            "trend": "seasonal"
          },
          "cost": {
            "kind": "decimal",
            "min": 50,
            "max": 2000,
            "scale": 2,
            "description": "Cost of the service provided.",
            "nullable": true,
            "when": {
              "column": "service_type",
              "in": [
                "repair",
                "cleaning"
              ]
            },
            "distribution": "normal"
          }
        }
      },
      "loans": {
        "description": "This table tracks loan applications made by customers for vehicle purchases. Each row corresponds to a loan application.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each loan application."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "Customer applying for the loan."
          },
          "amount": {
            "kind": "decimal",
            "min": 5000,
            "max": 50000,
            "scale": 2,
            "description": "Loan amount requested.",
            "distribution": "long-tail"
          },
          "application_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Date of loan application.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "approved"
              ]
            },
            "trend": "growing"
          },
          "status": {
            "kind": "enum",
            "values": [
              "pending",
              "approved",
              "rejected"
            ],
            "description": "Current status of the loan application.",
            "nullable": true,
            "weights": [
              5,
              3,
              1
            ]
          }
        }
      },
      "suppliers": {
        "description": "This table contains information about suppliers that provide parts and services to the dealership. Each row represents a supplier.",
        "rows": 80,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each supplier."
          },
          "name": {
            "kind": "company",
            "description": "Name of the supplier."
          },
          "contact": {
            "kind": "fullName",
            "description": "Contact person at the supplier."
          },
          "phone": {
            "kind": "phone",
            "description": "Contact phone number for the supplier."
          },
          "email": {
            "kind": "email",
            "from": "contact",
            "description": "Email address of the supplier's contact."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "monetary",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What are the most popular vehicle models sold this year?",
      "How often do customers return for maintenance services?",
      "What is the average loan amount requested by customers?",
      "Which supplier provides the most parts for our vehicles?",
      "What percentage of transactions involve specialty vehicles?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Customers with Transactions",
        "sql": "SELECT customers.full_name, transactions.amount FROM customers JOIN transactions ON customers.id = transactions.customer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Services by Type",
        "sql": "SELECT service_type, COUNT(*) FROM services GROUP BY service_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average Transaction Amount by Vehicle Category",
        "sql": "SELECT vehicles.category, AVG(transactions.amount) FROM transactions JOIN vehicles ON transactions.vehicle_id = vehicles.id GROUP BY vehicles.category;",
        "dialect": "postgres"
      },
      {
        "title": "Loans Status Summary",
        "sql": "SELECT status, COUNT(*) FROM loans GROUP BY status;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Sales Analysis",
        "body": "A business analyst at an auto dealership can use this dataset to analyze sales trends over the year, identifying which vehicle models are selling the best and how customer loyalty impacts purchasing decisions."
      },
      {
        "title": "Service Optimization",
        "body": "Service managers can leverage this data to optimize service schedules, ensuring that maintenance requests are handled efficiently, and identifying peak service times to allocate resources effectively."
      },
      {
        "title": "Loan Processing Efficiency",
        "body": "Loan officers can utilize the dataset to assess the volume of loan applications, monitor approval rates, and identify any bottlenecks in the loan processing system, ultimately aiming to enhance customer satisfaction."
      },
      {
        "title": "Supplier Relationship Management",
        "body": "Procurement specialists can analyze supplier data to evaluate performance, track the frequency of parts ordered, and establish stronger relationships with suppliers based on reliability and service quality."
      }
    ]
  },
  "counts": {
    "customers": 200,
    "loans": 150,
    "suppliers": 80,
    "vehicles": 100,
    "transactions": 300,
    "services": 250
  },
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