{
  "format": "dataset-manifest/1",
  "slug": "manufacturing-virtual-environments",
  "version": 2,
  "seed": "manufacturing-virtual-environments:1",
  "spec": {
    "format": "dataset/1",
    "slug": "manufacturing-virtual-environments",
    "name": "Manufacturing Virtual Environments",
    "tagline": "Optimizing manufacturing through virtual machine lifecycle management.",
    "description": "Manufacturers optimize their production processes by efficiently managing virtual environments that host various applications and workloads. Virtual machines (VMs) play a pivotal role in this operation, enabling companies to test innovations without disrupting real-world manufacturing activities. Each virtual machine in the 'virtual_machines' table represents a digital instance used for different manufacturing applications, storing information such as its ID, status, and associated configurations. These machines generate performance metrics that are crucial for assessing their operational efficiency, contained in the 'performance_metrics' table, which links back to each VM through its unique identifier.\n\nResource allocation is essential for ensuring that each VM performs optimally. The 'resource_allocations' table details how resources like CPU, memory, and storage are distributed among the virtual machines, enabling you to analyze resource utilization patterns. The allocation data can be joined with the performance metrics to question whether certain resource allocations lead to improved performance outcomes.\n\nSnapshots of VMs, documented in the 'vm_snapshots' table, provide critical points in time for recovery or testing. While these are used less frequently, they serve important roles when testing new configurations or recovering from failures. Additionally, the 'vm_extensions' table holds custom extensions that enhance VM capabilities; these are particularly valuable for specialized projects, presenting opportunities for analysis of their impact on performance.\n\nThe environmental configurations in the 'environmental_configs' table reflect the settings that govern the entire virtual environment, allowing comparisons of different configurations against their performance metrics. By querying across these tables, you can answer questions about resource efficiency, VM performance, and the effects of specific configurations or extensions. This synthetic dataset offers a practical playground for data analysts and developers seeking to enhance their skills in managing and analyzing manufacturing-related virtual environments.",
    "domain": "manufacturing",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "virtual machines",
      "manufacturing",
      "performance metrics",
      "resource allocation",
      "snapshots",
      "configurations"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "virtual_machines": {
        "description": "This table contains information about virtual machines, including their unique identifiers, names, statuses, and the dates they were created. Each VM can be linked to performance metrics, snapshots, and resource allocations.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each virtual machine."
          },
          "name": {
            "kind": "fullName",
            "description": "The name of the virtual machine."
          },
          "status": {
            "kind": "enum",
            "values": [
              "running",
              "stopped",
              "paused"
            ],
            "description": "Current status of the virtual machine.",
            "weights": [
              8,
              2,
              1
            ]
          },
          "created_at": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Timestamp when the virtual machine was created.",
            "dayProfile": "business"
          }
        }
      },
      "performance_metrics": {
        "description": "Performance metrics table captures various performance indicators for each virtual machine over time, linked by the VM ID.",
        "rows": 800,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each performance metric entry."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "Reference to the virtual machine."
          },
          "timestamp": {
            "kind": "datetime",
            "after": "vm_id.created_at",
            "withinDays": 365,
            "description": "Time when the performance metric was recorded."
          },
          "cpu_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "CPU usage percentage of the virtual machine.",
            "nullable": true,
            "distribution": "high"
          },
          "memory_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Memory usage percentage of the virtual machine.",
            "nullable": true,
            "distribution": "high"
          },
          "disk_io": {
            "kind": "decimal",
            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Disk I/O operations per second.",
            "nullable": true,
            "distribution": "normal"
          }
        }
      },
      "vm_snapshots": {
        "description": "Snapshots of virtual machines taken at specific points in time for backup and recovery purposes, linked to the corresponding VM.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each snapshot."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "Reference to the virtual machine."
          },
          "snapshot_time": {
            "kind": "datetime",
            "after": "vm_id.created_at",
            "withinDays": 730,
            "description": "Timestamp when the snapshot was taken."
          },
          "description": {
            "kind": "sentence",
            "description": "Description of the snapshot.",
            "nullable": true
          }
        }
      },
      "resource_allocations": {
        "description": "Resource allocations table detailing the allocated resources for each virtual machine, including CPU and memory specifications.",
        "rows": 400,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each resource allocation."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "Reference to the virtual machine."
          },
          "allocation_time": {
            "kind": "datetime",
            "after": "vm_id.created_at",
            "withinDays": 365,
            "description": "Timestamp when the resources were allocated."
          },
          "cpu_allocated": {
            "kind": "int",
            "min": 1,
            "max": 16,
            "description": "Number of CPU cores allocated.",
            "nullable": true,
            "distribution": "normal"
          },
          "memory_allocated": {
            "kind": "decimal",
            "min": 1,
            "max": 128,
            "scale": 2,
            "description": "Amount of memory allocated in GB.",
            "nullable": true,
            "distribution": "normal"
          }
        }
      },
      "vm_extensions": {
        "description": "Extensions or plugins installed on virtual machines, providing additional features or functionalities, linked to the corresponding VM.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each extension."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "Reference to the virtual machine."
          },
          "extension_name": {
            "kind": "words",
            "max": 3,
            "description": "Name of the installed extension.",
            "nullable": true,
            "pool": [
              "analytics",
              "monitoring",
              "automation",
              "visualization",
              "reporting",
              "simulation",
              "scheduling",
              "optimization",
              "management",
              "integration",
              "safety",
              "control",
              "design",
              "configuration",
              "analysis",
              "tracking",
              "performance",
              "testing",
              "deployment",
              "planning",
              "resource"
            ],
            "min": 1
          },
          "installed_at": {
            "kind": "datetime",
            "after": "vm_id.created_at",
            "withinDays": 730,
            "description": "Timestamp when the extension was installed."
          }
        }
      },
      "environmental_configs": {
        "description": "Configuration settings for the virtual machines, which can include network settings and other parameters that affect VM performance.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each configuration."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "Reference to the virtual machine."
          },
          "config_time": {
            "kind": "datetime",
            "after": "vm_id.created_at",
            "withinDays": 365,
            "description": "Timestamp when the configuration was applied."
          },
          "network_type": {
            "kind": "enum",
            "values": [
              "NAT",
              "Bridged",
              "Host-only"
            ],
            "description": "Type of network configuration for the VM.",
            "weights": [
              5,
              3,
              2
            ]
          },
          "disk_space": {
            "kind": "decimal",
            "min": 10,
            "max": 2048,
            "scale": 2,
            "description": "Allocated disk space in GB.",
            "nullable": true,
            "when": {
              "column": "network_type",
              "in": [
                "NAT",
                "Bridged",
                "Host-only"
              ]
            },
            "distribution": "long-tail"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "monetary",
      "free-text",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What is the average CPU usage of virtual machines over the past year?",
      "How do resource allocations affect the performance metrics of VMs?",
      "What trends can be identified from the snapshot history of virtual machines?",
      "Which virtual machines have the highest memory usage, and what configurations do they have?",
      "How often are extensions installed on VMs, and what impact do they have on performance?"
    ],
    "suggestedQueries": [
      {
        "title": "Join VMs with Performance Metrics",
        "sql": "SELECT vm.name, pm.cpu_usage FROM virtual_machines vm JOIN performance_metrics pm ON vm.id = pm.vm_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Memory Usage by VM Status",
        "sql": "SELECT vm.status, AVG(pm.memory_usage) FROM virtual_machines vm JOIN performance_metrics pm ON vm.id = pm.vm_id GROUP BY vm.status;",
        "dialect": "postgres"
      },
      {
        "title": "Count of Snapshots per VM",
        "sql": "SELECT vm.name, COUNT(s.id) FROM virtual_machines vm LEFT JOIN vm_snapshots s ON vm.id = s.vm_id GROUP BY vm.name;",
        "dialect": "postgres"
      },
      {
        "title": "Resource Allocation Trends",
        "sql": "SELECT ra.allocation_time, AVG(ra.cpu_allocated) FROM resource_allocations ra GROUP BY ra.allocation_time ORDER BY ra.allocation_time;",
        "dialect": "postgres"
      },
      {
        "title": "Performance Over Time for a Specific VM",
        "sql": "SELECT pm.timestamp, pm.cpu_usage, pm.memory_usage FROM performance_metrics pm JOIN virtual_machines vm ON pm.vm_id = vm.id WHERE vm.name = 'MyVirtualMachine' ORDER BY pm.timestamp;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Data Analysts Exploring VM Performance",
        "body": "Data analysts in a manufacturing company can utilize this dataset to explore the performance of virtual machines over time. By analyzing CPU and memory usage metrics alongside resource allocations, they can identify inefficiencies and recommend improvements to VM configurations to enhance production processes."
      },
      {
        "title": "Developers Testing Virtual Environment Configurations",
        "body": "Developers working with virtual environments can leverage this dataset to test various configurations and their impact on performance. By experimenting with different resource allocations and observing the resulting performance metrics, they can optimize setups for specific manufacturing tasks."
      },
      {
        "title": "IT Managers Assessing VM Lifecycles",
        "body": "IT managers can use this dataset to assess the lifecycle of virtual machines, including how often snapshots are taken and when configurations are applied. This information is crucial for managing virtual environments effectively, ensuring they remain up-to-date and performant throughout their usage."
      },
      {
        "title": "Business Intelligence Professionals Creating Dashboards",
        "body": "Business intelligence professionals can create interactive dashboards using this dataset to visualize trends in virtual machine performance and resource usage. By presenting this data in a digestible format, they can help stakeholders make informed decisions about investments in virtual infrastructure."
      }
    ]
  },
  "counts": {
    "virtual_machines": 200,
    "environmental_configs": 300,
    "performance_metrics": 800,
    "resource_allocations": 400,
    "vm_extensions": 100,
    "vm_snapshots": 150
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}