{
  "format": "dataset-manifest/1",
  "slug": "financial-operations-ledger",
  "version": 2,
  "seed": "financial-operations-ledger:1",
  "spec": {
    "format": "dataset/1",
    "slug": "financial-operations-ledger",
    "name": "Financial Operations Ledger",
    "tagline": "Customers, products, transactions, orders, and vendors data from ongoing financial exchanges in fintech.",
    "description": "Customers engage with various financial products, purchasing services that result in a series of transactions. Every transaction records essential details such as the time, amount, and related product, allowing the business to analyze customer behavior and product performance. Orders reflect the requests made by customers for specific products, detailing the quantities and associated costs, while vendors provide the necessary products and services that fulfill these orders. This interconnectedness highlights the flow of financial operations and helps maintain a seamless service experience for customers.\n\nThe 'customers' table captures individual client information, including their engagement level and transaction history. Each entry can be linked to multiple 'transactions' that reflect the financial exchanges made, providing insight into customer spending patterns. The 'products' table lists various offerings available in the marketplace, showcasing descriptions and pricing. This table connects to transactions to reveal which products are most popular and how customers interact with different offerings. 'Orders' detail the specific requests from customers, while 'vendors' supply the products sold, linking the offerings back to the source.\n\nAnalyzing this dataset allows developers to explore numerous questions: How do customer behaviors vary across different financial products? What are the trends in transactions over time? Which products generate the most revenue, and how do vendor relationships influence inventory management? Practicing with this synthetic dataset provides a rich environment to develop skills in relational database management, data analysis, and financial reporting, simulating real-world scenarios faced by fintech companies.",
    "domain": "fintech",
    "shape": "ledger",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "financial transactions",
      "customer engagement",
      "products",
      "orders",
      "vendors",
      "ledger",
      "fintech"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "customers": {
        "description": "This table contains unique customer records, detailing their engagement with financial products. Each customer is linked to their transactions and orders.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each customer."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the customer.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The email address of the customer.",
            "nullable": true
          },
          "signup_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-01-01",
            "description": "The date when the customer signed up."
          }
        }
      },
      "products": {
        "description": "This table captures the financial products offered, linking them to the transactions made by customers. Each entry details the nature and category of the product.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each product."
          },
          "product_name": {
            "kind": "productName",
            "description": "The name of the financial product.",
            "nullable": true
          },
          "category": {
            "kind": "enum",
            "values": [
              "loans",
              "investments",
              "savings",
              "insurance"
            ],
            "description": "The category of the financial product."
          },
          "price": {
            "kind": "decimal",
            "min": 100,
            "max": 10000,
            "scale": 2,
            "description": "The price of the product.",
            "nullable": true,
            "distribution": "long-tail"
          }
        }
      },
      "transactions": {
        "description": "This table records each transaction made by customers, linking them to specific products. It reflects the dynamic nature of customer engagement.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "customer_id": {
            "kind": "fk",
            "ref": "customers.id",
            "description": "The ID of the customer making the transaction."
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The ID of the product being purchased."
          },
          "transaction_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-01-01",
            "dayProfile": "business",
            "description": "The date and time of the transaction."
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 5000,
            "scale": 2,
            "description": "The amount of money involved in the transaction.",
            "nullable": true,
            "distribution": "long-tail"
          }
        }
      },
      "orders": {
        "description": "This table outlines the orders placed by customers, including their statuses and timestamps. It links back to the transactions and customers.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each order."
          },
          "transaction_id": {
            "kind": "fk",
            "ref": "transactions.id",
            "description": "The ID of the transaction linked to the order."
          },
          "order_status": {
            "kind": "enum",
            "values": [
              "pending",
              "completed",
              "cancelled"
            ],
            "description": "The current status of the order."
          },
          "order_date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-01-01",
            "dayProfile": "business",
            "description": "The date and time when the order was placed.",
            "nullable": true,
            "when": {
              "column": "order_status",
              "in": [
                "completed",
                "cancelled"
              ]
            }
          }
        }
      },
      "vendors": {
        "description": "This table tracks vendors supplying products, linking them to the products offered. Each vendor's performance can be assessed through this connection.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each vendor."
          },
          "vendor_name": {
            "kind": "company",
            "description": "The name of the vendor.",
            "nullable": true
          },
          "contact_email": {
            "kind": "email",
            "from": "vendor_name",
            "description": "The email address for contacting the vendor.",
            "nullable": true
          },
          "product_id": {
            "kind": "fk",
            "ref": "products.id",
            "description": "The ID of the product supplied by the vendor."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "timestamped",
      "monetary",
      "categorical"
    ],
    "analysisQuestions": [
      "What products are most frequently purchased by customers?",
      "How do transaction amounts vary across different product categories?",
      "What percentage of orders are cancelled each month?",
      "When do most customers sign up for new financial products?",
      "How does vendor performance correlate with product sales?"
    ],
    "suggestedQueries": [
      {
        "title": "Fetch all transactions for a specific customer",
        "sql": "SELECT * FROM transactions WHERE customer_id = 1;",
        "dialect": "postgres"
      },
      {
        "title": "Count of transactions by product category",
        "sql": "SELECT p.category, COUNT(t.id) FROM transactions t JOIN products p ON t.product_id = p.id GROUP BY p.category;",
        "dialect": "postgres"
      },
      {
        "title": "Monthly order status summary",
        "sql": "SELECT DATE_TRUNC('month', o.order_date) AS month, o.order_status, COUNT(*) FROM orders o GROUP BY month, o.order_status;",
        "dialect": "postgres"
      },
      {
        "title": "Average transaction amount by product",
        "sql": "SELECT p.product_name, AVG(t.amount) FROM transactions t JOIN products p ON t.product_id = p.id GROUP BY p.product_name;",
        "dialect": "postgres"
      },
      {
        "title": "Yearly customer sign-up trends",
        "sql": "SELECT EXTRACT(YEAR FROM c.signup_date) AS year, COUNT(*) FROM customers c GROUP BY year;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Financial Analyst",
        "body": "A financial analyst can utilize this dataset to evaluate customer spending patterns and product popularity over time. By querying transaction amounts and analyzing order statuses, they can identify trends and make data-driven decisions to optimize product offerings."
      },
      {
        "title": "Database Instructor",
        "body": "An instructor teaching SQL can use this dataset to demonstrate complex joins and aggregations. Students can practice writing queries that span multiple tables, helping them understand real-world applications of relational databases in financial contexts."
      },
      {
        "title": "Product Manager",
        "body": "Product managers can leverage the insights from this dataset to assess how different financial products are performing in the market. They can analyze vendor performance and customer feedback, guiding future product development and marketing strategies."
      },
      {
        "title": "Data Scientist",
        "body": "A data scientist can use this dataset to build predictive models based on customer behavior. By analyzing transaction trends and order fulfillment rates, they can develop algorithms to forecast sales and improve inventory management."
      }
    ]
  },
  "counts": {
    "customers": 200,
    "products": 100,
    "transactions": 500,
    "orders": 300,
    "vendors": 50
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