{
  "format": "dataset-manifest/1",
  "slug": "local-business-and-artisan-support-app",
  "version": 1,
  "seed": "local-business-and-artisan-support-app:1",
  "spec": {
    "format": "dataset/1",
    "slug": "local-business-and-artisan-support-app",
    "name": "Artisan Support Transactions",
    "tagline": "Transactions related to orders, workshops, and events from a marketplace for local artisans and their customers.",
    "description": "Artisans showcase their unique, handmade products and engage with customers eager to discover one-of-a-kind items. The bustling marketplace thrives on transactions that encompass orders for these goods, participation in workshops, and attendance at community events designed to spotlight local talent. Customers vary in their engagement levels; some may purchase infrequently while others develop a deeper connection with multiple artisans, often booking several workshops. This dynamic creates a marketplace rich in community involvement and support for local craftspeople.\n\nThe primary table in this dataset, 'transactions', captures the details of each customer interaction, including orders placed, workshops attended, and events joined. Each row within this table represents a specific transaction by a customer, linking them directly to the artisan and the product or service they engaged with. By analyzing the transaction data, developers can explore how different artisans attract their clientele, identify trends in workshop attendance, and evaluate the popularity of certain products or events.\n\nQuestions that can be addressed through this data include: What types of products are most frequently ordered? How does participation in workshops impact future purchases? Are there specific events that drive higher engagement among customers? Such inquiries provide opportunities to practice data manipulation, analysis, and visualization skills. The dataset is synthetic, created to simulate a vibrant marketplace, allowing developers to experiment with real-world scenarios without relying on actual customer data.",
    "domain": "customer-support",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "artisans",
      "transactions",
      "local business",
      "workshops",
      "community events",
      "customer engagement",
      "handmade products"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "transactions": {
        "description": "Each row represents a transaction that includes orders, workshop bookings, and event attendances, linking customers to artisans and capturing details of the transaction.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "customer_name": {
            "kind": "fullName",
            "description": "Full name of the customer involved in the transaction."
          },
          "artisan_name": {
            "kind": "fullName",
            "description": "Full name of the artisan providing the product or service."
          },
          "transaction_type": {
            "kind": "enum",
            "values": [
              "order",
              "workshop",
              "event"
            ],
            "description": "Type of transaction that occurred."
          },
          "transaction_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the transaction occurred.",
            "dayProfile": "waking"
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 500,
            "scale": 2,
            "description": "Monetary amount of the transaction.",
            "nullable": true,
            "when": {
              "column": "loyalty_status",
              "in": [
                "new"
              ]
            }
          },
          "loyalty_status": {
            "kind": "enum",
            "values": [
              "new",
              "loyal",
              "frequent"
            ],
            "description": "Customer loyalty status based on transaction frequency."
          },
          "workshop_attendance": {
            "kind": "bool",
            "trueRate": 0.3,
            "description": "Indicates whether the customer attended a workshop.",
            "nullable": true,
            "when": {
              "column": "transaction_type",
              "in": [
                "workshop"
              ]
            }
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "monetary",
      "timestamped",
      "categorical"
    ],
    "analysisQuestions": [
      "What are the most popular types of transactions among customers?",
      "How does customer loyalty influence transaction amounts?",
      "Which artisans have the highest frequency of repeat customers?",
      "What impact do workshops have on overall sales?",
      "What trends can be identified in transaction types over time?"
    ],
    "suggestedQueries": [
      {
        "title": "List all transactions by artisan",
        "sql": "SELECT artisan_name, COUNT(*) as transaction_count FROM transactions GROUP BY artisan_name;",
        "dialect": "postgres"
      },
      {
        "title": "Calculate total sales per transaction type",
        "sql": "SELECT transaction_type, SUM(amount) as total_sales FROM transactions GROUP BY transaction_type;",
        "dialect": "postgres"
      },
      {
        "title": "Identify loyal customers and their spending",
        "sql": "SELECT customer_name, SUM(amount) as total_spent FROM transactions WHERE loyalty_status = 'loyal' GROUP BY customer_name;",
        "dialect": "postgres"
      },
      {
        "title": "Find transaction trends over the year",
        "sql": "SELECT DATE_TRUNC('month', transaction_date) as month, COUNT(*) as transaction_count FROM transactions GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "E-commerce Analysis",
        "body": "Developers working on e-commerce platforms can use this dataset to simulate customer transactions, allowing them to refine their database schemas and optimize query performance. By practicing with realistic data, they can better understand how transaction types and customer behaviors interact."
      },
      {
        "title": "Data Visualization Projects",
        "body": "Data analysts looking to create visualizations of customer engagement and sales trends can leverage this dataset. By utilizing the transaction details, they can produce insightful graphs and dashboards that highlight artisan popularity and transaction dynamics."
      },
      {
        "title": "Workshop Management Systems",
        "body": "Developers of workshop management systems can employ this dataset to test their applications. By simulating transaction scenarios, they can ensure that their systems accurately track attendance and revenue from various events, enhancing user experience for both artisans and customers."
      }
    ]
  },
  "counts": {
    "transactions": 500
  },
  "stats": {
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      "columns": {
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          "distinct": 500,
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          "nulls": 0,
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          "min": "Alice Ali",
          "max": "Zoe Shah"
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          "nulls": 0,
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          "min": "2022-01-01 09:18:28",
          "max": "2023-12-31 13:37:04"
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        "amount": {
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          "distinct": 168,
          "min": 12.16,
          "max": 498.4
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        "loyalty_status": {
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          "max": "new"
        },
        "workshop_attendance": {
          "nulls": 327,
          "distinct": 2,
          "min": "false",
          "max": "true"
        }
      }
    }
  },
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