{
  "format": "dataset-manifest/1",
  "slug": "human-resources-ledger",
  "version": 1,
  "seed": "human-resources-ledger:1",
  "spec": {
    "format": "dataset/1",
    "slug": "human-resources-ledger",
    "name": "Human Resources Ledger",
    "tagline": "A synthetic dataset for managing HR operations.",
    "description": "Employees drive productivity and innovation within a dynamic organization, ensuring that operations run smoothly. The complexities of payroll management intertwine with attendance tracking, invoice processing, and monitoring sales orders, all vital components for maximizing workforce efficiency. The data captures a snapshot of how these elements interact, revolving mainly around full-time staff with some entries for seasonal or temporary employees, which showcase the fluctuations in workforce dynamics. This structure reflects the organization’s rhythm, characterized by regular payroll cycles and consistent attendance patterns. \n\nThe hr_ledger table serves as the core repository for all the records. Each row represents individual employee records that include critical metrics such as payroll details, attendance logs, and associated sales orders. By linking attendance data with payroll entries, it becomes possible to draw insights into patterns like absenteeism and its impact on overall payroll costs. This allows for analysis of productivity trends over specific periods. Additionally, the data can drive inquiries into sales performance relative to workforce availability, offering a comprehensive view of how staffing levels affect revenue generation. \n\nAnalysts can explore questions such as: How does employee attendance correlate with payroll expenses? What is the breakdown of costs associated with seasonal workers versus full-time staff? By practicing on this data, developers can enhance their skills in relational database management, data analysis, and reporting, all while navigating the intricacies that come with human resource data. Keep in mind, the information within this dataset is synthetic, crafted to mimic real-world HR scenarios while providing a safe environment for experimentation.",
    "domain": "hr",
    "shape": "ledger",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "HR",
      "employee management",
      "payroll",
      "attendance",
      "invoices",
      "sales orders",
      "synthetic data"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "hr_ledger": {
        "description": "This table contains records of employee management activities, linking payroll, attendance, invoices, and sales orders. Each row represents an employee's data across various metrics, capturing the dynamics of workforce management in a ledger format.",
        "rows": 500,
        "columns": {
          "employee_id": {
            "kind": "pk",
            "description": "A unique identifier for each employee."
          },
          "first_name": {
            "kind": "firstName",
            "description": "The first name of the employee."
          },
          "last_name": {
            "kind": "lastName",
            "description": "The last name of the employee."
          },
          "position": {
            "kind": "words",
            "description": "The job title or position held by the employee."
          },
          "salary": {
            "kind": "decimal",
            "min": 30000,
            "max": 120000,
            "scale": 2,
            "description": "The annual salary of the employee."
          },
          "attendance_days": {
            "kind": "int",
            "min": 0,
            "max": 365,
            "description": "The number of days the employee was present at work in a year."
          },
          "invoices_processed": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "The number of invoices processed by the employee."
          },
          "sales_orders": {
            "kind": "int",
            "min": 0,
            "max": 50,
            "description": "The number of sales orders handled by the employee."
          },
          "employment_type": {
            "kind": "enum",
            "values": [
              "full-time",
              "part-time",
              "temporary"
            ],
            "description": "The type of employment of the employee."
          },
          "hire_date": {
            "kind": "date",
            "from": "2010-01-01",
            "to": "2023-12-31",
            "description": "The date when the employee was hired.",
            "trend": "growing"
          },
          "status": {
            "kind": "enum",
            "values": [
              "active",
              "inactive",
              "terminated"
            ],
            "description": "The current employment status of the employee."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "What is the average salary of full-time employees?",
      "How many invoices does each employee process on average?",
      "What is the attendance trend throughout the year?",
      "What percentage of employees are currently active?",
      "How does the number of sales orders relate to employee status?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Employee Data",
        "sql": "SELECT first_name, last_name, position FROM hr_ledger WHERE status = 'active';",
        "dialect": "postgres"
      },
      {
        "title": "Average Salary Calculation",
        "sql": "SELECT AVG(salary) as average_salary FROM hr_ledger WHERE employment_type = 'full-time';",
        "dialect": "postgres"
      },
      {
        "title": "Attendance Grouping",
        "sql": "SELECT hire_date, SUM(attendance_days) as total_attendance FROM hr_ledger GROUP BY hire_date;",
        "dialect": "postgres"
      },
      {
        "title": "Sales Orders by Employment Type",
        "sql": "SELECT employment_type, SUM(sales_orders) as total_sales FROM hr_ledger GROUP BY employment_type;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "HR Data Analysis",
        "body": "HR analysts can use this dataset to practice various SQL queries related to employee management, helping them understand trends in workforce data, such as attendance and payroll analysis."
      },
      {
        "title": "Teaching SQL Concepts",
        "body": "Educators can utilize this dataset to teach students about SQL and database management by providing practical exercises that involve employee data, enhancing their learning experience with real-world scenarios."
      },
      {
        "title": "Performance Tracking",
        "body": "Managers can analyze the data to track employee performance metrics, such as the number of invoices processed and sales orders handled, enabling better decision-making regarding workforce efficiency."
      }
    ]
  },
  "counts": {
    "hr_ledger": 500
  },
  "stats": {
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          "distinct": 79,
          "min": "Alice",
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        "last_name": {
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        "position": {
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        "sales_orders": {
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          "distinct": 3,
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        "status": {
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