{
  "format": "dataset-manifest/1",
  "slug": "manufacturing-resource-management",
  "version": 1,
  "seed": "manufacturing-resource-management:1",
  "spec": {
    "format": "dataset/1",
    "slug": "manufacturing-resource-management",
    "name": "Manufacturing Resource Management",
    "tagline": "Virtual machines, clusters, storage volumes, and performance metrics from a manufacturing resource management system.",
    "description": "Virtual machines manage production workflows, track inventory, and monitor equipment performance in a manufacturing environment. This operation relies heavily on these virtual machines to ensure efficiency and productivity across various stages of manufacturing. Each virtual machine is linked to performance snapshots that provide insights into the operational health of the system, while backup tasks ensure data integrity and security. Additionally, clusters serve as the backbone of virtual machine organization, allowing for scalability and resource allocation, with some clusters housing a significant number of virtual machines and others containing fewer due to specific storage volume constraints.\n\nIn this dataset, each row in the virtual_machines table represents an individual virtual machine, complete with unique identifiers and performance metrics. The clusters table contains details of the clusters, with each row signifying a distinct cluster and its relationship to multiple virtual machines. The storage_volumes table allocates storage specifically for these virtual machines, detailing individual volumes that can be linked back to their respective virtual machines. The backup_tasks table tracks the backup activities associated with each virtual machine, ensuring operational resilience. Lastly, performance_snapshots provide a historical view of the virtual machines' performance over time, enabling trend analysis and system optimization.\n\nThis dataset answers critical questions around virtual machine performance, backup efficiency, and resource allocation within a manufacturing setting. Practicing with this data allows developers to enhance their analytical skills, dive into performance trends, and explore how virtual machines interact within clusters and manage storage effectively. The data is synthetic, crafted to simulate real-world scenarios while providing a rich environment for hands-on learning.",
    "domain": "manufacturing",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "virtual machines",
      "performance metrics",
      "storage volumes",
      "backup tasks",
      "clusters",
      "manufacturing",
      "efficiency",
      "resource management"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "virtual_machines": {
        "description": "Each row represents a virtual machine, detailing its specifications, performance metrics, and the cluster it belongs to.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the virtual machine."
          },
          "name": {
            "kind": "fullName",
            "description": "The name of the virtual machine."
          },
          "cluster_id": {
            "kind": "fk",
            "ref": "clusters.id",
            "description": "The cluster that the virtual machine is a part of."
          },
          "cpu_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "CPU usage percentage of the virtual machine.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "paused"
              ]
            }
          },
          "memory_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 128,
            "scale": 2,
            "description": "Memory usage in GB.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "paused"
              ]
            }
          },
          "disk_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 2000,
            "scale": 2,
            "description": "Disk usage in GB.",
            "nullable": true
          },
          "status": {
            "kind": "enum",
            "values": [
              "running",
              "stopped",
              "paused"
            ],
            "description": "Current operational status of the virtual machine."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date the virtual machine was created.",
            "dayProfile": "business"
          }
        }
      },
      "clusters": {
        "description": "Each row represents a cluster that contains multiple virtual machines, including its capacity and performance metrics.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the cluster."
          },
          "name": {
            "kind": "fullName",
            "description": "The name of the cluster."
          },
          "total_vms": {
            "kind": "int",
            "min": 1,
            "max": 60,
            "description": "Total number of virtual machines in the cluster."
          },
          "cpu_capacity": {
            "kind": "decimal",
            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Total CPU capacity of the cluster in GHz."
          },
          "memory_capacity": {
            "kind": "decimal",
            "min": 0,
            "max": 512,
            "scale": 2,
            "description": "Total memory capacity in GB."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date the cluster was created.",
            "dayProfile": "business"
          }
        }
      },
      "storage_volumes": {
        "description": "Each row represents a storage volume associated with a virtual machine, detailing its capacity and usage.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the storage volume."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "The virtual machine that the storage volume is associated with."
          },
          "capacity": {
            "kind": "decimal",
            "min": 0,
            "max": 2000,
            "scale": 2,
            "description": "Total capacity of the storage volume in GB."
          },
          "used_space": {
            "kind": "decimal",
            "min": 0,
            "max": 2000,
            "scale": 2,
            "description": "Used space on the storage volume in GB."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date the storage volume was created.",
            "dayProfile": "business"
          }
        }
      },
      "backup_tasks": {
        "description": "Each row represents a backup task for a virtual machine, including its status and scheduled time.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the backup task."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "The virtual machine that the backup task is associated with."
          },
          "backup_time": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Scheduled time for the backup task.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "failed"
              ]
            },
            "dayProfile": "business"
          },
          "status": {
            "kind": "enum",
            "values": [
              "completed",
              "failed",
              "in_progress"
            ],
            "description": "Current status of the backup task."
          }
        }
      },
      "performance_snapshots": {
        "description": "Each row represents a snapshot of performance metrics for a virtual machine at a specific time.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the performance snapshot."
          },
          "vm_id": {
            "kind": "fk",
            "ref": "virtual_machines.id",
            "description": "The virtual machine for which the snapshot is taken."
          },
          "snapshot_time": {
            "kind": "datetime",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The time at which the performance metrics were recorded."
          },
          "cpu_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "CPU usage percentage at the snapshot time."
          },
          "memory_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 128,
            "scale": 2,
            "description": "Memory usage in GB at the snapshot time."
          },
          "disk_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 2000,
            "scale": 2,
            "description": "Disk usage in GB at the snapshot time."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "categorical",
      "monetary"
    ],
    "analysisQuestions": [
      "What is the average CPU usage of virtual machines over time?",
      "How do storage volumes impact the performance of virtual machines?",
      "Which clusters have the highest number of virtual machines, and how does that affect overall performance?",
      "What percentage of backup tasks are successful versus those that fail?",
      "How does memory usage correlate with the number of virtual machines in a cluster?"
    ],
    "suggestedQueries": [
      {
        "title": "Join virtual machines and clusters",
        "sql": "SELECT vm.name, c.name FROM virtual_machines vm JOIN clusters c ON vm.cluster_id = c.id;",
        "dialect": "postgres"
      },
      {
        "title": "Average CPU usage by cluster",
        "sql": "SELECT c.name, AVG(vm.cpu_usage) FROM virtual_machines vm JOIN clusters c ON vm.cluster_id = c.id GROUP BY c.name;",
        "dialect": "postgres"
      },
      {
        "title": "Count of backup task statuses",
        "sql": "SELECT status, COUNT(*) FROM backup_tasks GROUP BY status;",
        "dialect": "postgres"
      },
      {
        "title": "Performance metrics over time",
        "sql": "SELECT ps.snapshot_time, AVG(ps.cpu_usage) AS avg_cpu_usage FROM performance_snapshots ps GROUP BY ps.snapshot_time ORDER BY ps.snapshot_time;",
        "dialect": "postgres"
      },
      {
        "title": "Storage usage and performance correlation",
        "sql": "SELECT vm.id, vm.name, sv.used_space, ps.cpu_usage FROM virtual_machines vm JOIN storage_volumes sv ON vm.id = sv.vm_id JOIN performance_snapshots ps ON vm.id = ps.vm_id;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Operational Efficiency Analysis",
        "body": "Manufacturing analysts can utilize this dataset to evaluate the operational efficiency of virtual machines and clusters in their facility. By examining CPU and memory usage, they can identify underperforming resources and optimize workflows, leading to improved production output."
      },
      {
        "title": "Backup Task Monitoring",
        "body": "IT managers can leverage this dataset to monitor the success rates of backup tasks across various virtual machines. By analyzing the status of backup tasks, they can implement strategies to reduce failures and ensure data integrity, which is critical in manufacturing environments."
      },
      {
        "title": "Resource Allocation Planning",
        "body": "Resource planners can use this dataset to strategize virtual machine allocation based on performance metrics and storage requirements. Understanding how many virtual machines can effectively operate within a cluster helps avoid over-provisioning and maximizes resource utilization."
      },
      {
        "title": "Trend Analysis for Capacity Planning",
        "body": "Capacity planners can analyze historical performance snapshots to forecast future resource needs. By identifying trends in CPU and memory usage, they can make informed decisions on scaling resources to meet the demands of upcoming production cycles."
      }
    ]
  },
  "counts": {
    "clusters": 50,
    "virtual_machines": 300,
    "backup_tasks": 100,
    "performance_snapshots": 500,
    "storage_volumes": 200
  },
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