{
  "format": "dataset-manifest/1",
  "slug": "literary-marketplace",
  "version": 2,
  "seed": "literary-marketplace:1",
  "spec": {
    "format": "dataset/1",
    "slug": "literary-marketplace",
    "name": "Literary Marketplace",
    "tagline": "Connect readers with a diverse range of literary works.",
    "description": "A vibrant bookstore connects readers with a diverse range of literary works, facilitating the purchase and discovery of books. The operation revolves around popular titles from various genres, the authors behind them, customer orders, and payment transactions. Inventory management is crucial, ensuring that bestselling books are readily available while also tracking award-winning titles that pique customer interest. While most orders consist of a mix of fiction and non-fiction, award-winning books tend to be less common in individual sales, creating a unique demand among discerning readers.\n\nWithin the literary marketplace, a row in the literary_marketplace table represents individual transactions, capturing essential details like the book title, author, genre, customer information, and payment status. Each transaction is linked to specific customer profiles, allowing for analysis of buying habits and preferences. Additionally, the table can be used to explore patterns in order history, which can reveal trends in which genres or authors are gaining popularity or which books are consistently in demand.\n\nThis dataset allows developers to practice a range of queries, such as analyzing sales trends over time, identifying the most popular genres or authors, and examining the impact of promotional campaigns on sales. By exploring customer order patterns, users can also delve into the demographics of their clientele, understanding how different segments engage with various types of literature. Since the data is synthetic, it offers a flexible and realistic environment to hone data manipulation and analysis skills without any privacy concerns.",
    "domain": "ecommerce",
    "shape": "star-schema",
    "useCases": [
      "bi-dashboards",
      "analytics-demo"
    ],
    "keywords": [
      "bookstore",
      "books",
      "authors",
      "orders",
      "inventory",
      "sales",
      "genres",
      "award-winning",
      "literature"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "literary_marketplace": {
        "description": "Each row represents a unique book transaction, linking details about the book, the author, customer orders, and payment information. This table allows for comprehensive analysis of sales trends and inventory management within the bookstore.",
        "rows": 1000,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction"
          },
          "book_title": {
            "kind": "fullName",
            "description": "Title of the book being purchased"
          },
          "author_name": {
            "kind": "fullName",
            "description": "Name of the author of the book"
          },
          "genre": {
            "kind": "enum",
            "values": [
              "Fiction",
              "Non-Fiction",
              "Mystery",
              "Romance",
              "Science Fiction",
              "Fantasy",
              "Biography",
              "History"
            ],
            "description": "Genre of the book",
            "weights": [
              5,
              3,
              2,
              4,
              3,
              2,
              1,
              1
            ]
          },
          "order_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date when the order was placed",
            "nullable": true,
            "when": {
              "column": "payment_status",
              "in": [
                "Paid"
              ]
            },
            "dayProfile": "waking"
          },
          "quantity": {
            "kind": "int",
            "min": 1,
            "max": 5,
            "description": "Number of copies of the book purchased in the transaction",
            "nullable": true,
            "when": {
              "column": "payment_status",
              "in": [
                "Paid",
                "Refunded"
              ]
            }
          },
          "price": {
            "kind": "decimal",
            "min": 5,
            "max": 50,
            "scale": 2,
            "description": "Price of the book at the time of sale",
            "distribution": "long-tail"
          },
          "payment_status": {
            "kind": "enum",
            "values": [
              "Paid",
              "Pending",
              "Refunded"
            ],
            "description": "Current status of the payment for the order",
            "weights": [
              7,
              2,
              1
            ]
          },
          "is_award_winning": {
            "kind": "bool",
            "trueRate": 0.2,
            "description": "Indicates whether the book is an award-winning title",
            "nullable": true
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "monetary",
      "timestamped"
    ],
    "analysisQuestions": [
      "What are the top-selling genres in the bookstore?",
      "How do sales of award-winning books compare to regular titles?",
      "What are the peak sales periods throughout the year?",
      "Which authors have the highest sales volume?",
      "What is the average price of books sold in each genre?"
    ],
    "suggestedQueries": [
      {
        "title": "Fetch all transactions for a specific genre",
        "sql": "SELECT * FROM literary_marketplace WHERE genre = 'Fiction';",
        "dialect": "postgres"
      },
      {
        "title": "Count total sales by author",
        "sql": "SELECT author_name, SUM(price * quantity) AS total_sales FROM literary_marketplace GROUP BY author_name;",
        "dialect": "postgres"
      },
      {
        "title": "Find average transaction size by payment status",
        "sql": "SELECT payment_status, AVG(quantity) AS average_quantity FROM literary_marketplace GROUP BY payment_status;",
        "dialect": "postgres"
      },
      {
        "title": "Identify sales trends over time",
        "sql": "SELECT DATE_TRUNC('month', order_date) AS month, SUM(price * quantity) AS total_sales FROM literary_marketplace GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Business Analysts",
        "body": "Business analysts can utilize the Literary Marketplace dataset to create comprehensive reports on sales trends, customer preferences, and inventory management. By analyzing the data, they can provide insights that drive marketing strategies and inventory decisions, helping the bookstore to better cater to its customers."
      },
      {
        "title": "Data Scientists",
        "body": "Data scientists can explore the dataset to build predictive models that forecast book sales based on past trends. By understanding which genres and authors attract the most customers, they can help the bookstore optimize its inventory and marketing efforts, ensuring that popular titles are always available."
      },
      {
        "title": "Marketing Teams",
        "body": "Marketing teams can leverage the dataset to identify target demographics for specific genres or award-winning titles. By analyzing customer orders and payment statuses, they can tailor promotional campaigns to boost sales and increase customer engagement within their literary marketplace."
      }
    ]
  },
  "counts": {
    "literary_marketplace": 1000
  },
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