{
  "format": "dataset-manifest/1",
  "slug": "network-monitoring-database",
  "version": 1,
  "seed": "network-monitoring-database:1",
  "spec": {
    "format": "dataset/1",
    "slug": "network-monitoring-database",
    "name": "Network Performance Insights",
    "tagline": "Network performance metrics, device statistics, interface statuses, alerts, and usage patterns from telecom operations.",
    "description": "Technicians monitor network performance in real-time, ensuring that all systems run smoothly and that customers experience reliable service. They utilize data from various devices connected to the network, analyze interface statistics, and respond to alerts that indicate potential issues. Regular maintenance schedules keep the network optimized, allowing for seamless operation and swift recovery from any disruptions. Patterns of usage can be closely examined to identify peak activity times, helping to allocate resources efficiently and improve service delivery.\n\nThe dataset contains several interrelated tables that provide a comprehensive view of the telecom operations. The 'devices' table holds information on the various hardware units connected to the network, detailing their specifications and operational status. The 'interfaces' table captures metrics and configurations for the network connections, illustrating their performance and reliability. The 'alerts' table logs notifications triggered by anomalous events, while the 'maintenance' table tracks scheduled activities aimed at preserving network integrity. Finally, the 'usage_patterns' table analyzes how and when devices are utilized, highlighting trends that can influence network adjustments.\n\nBy exploring the relationships among these tables, developers can uncover answers to critical operational questions, such as which devices are most prone to failure, how interface performance fluctuates during peak times, and what maintenance activities most effectively reduce alert occurrences. This synthetic dataset is particularly valuable for those looking to practice their data manipulation and analysis skills, providing an opportunity to engage with realistic scenarios in network performance management.",
    "domain": "telecom",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "network monitoring",
      "telecom",
      "performance metrics",
      "alert management",
      "device health"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "devices": {
        "description": "This table records performance metrics for various network devices, linking to their respective interfaces and alerts. Each row represents a device's performance over a specific time period.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the device."
          },
          "device_name": {
            "kind": "fullName",
            "description": "Name of the network device."
          },
          "device_type": {
            "kind": "enum",
            "values": [
              "Router",
              "Switch",
              "Access Point"
            ],
            "description": "Type of the network device."
          },
          "cpu_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "CPU usage percentage.",
            "nullable": true,
            "when": {
              "column": "device_type",
              "in": [
                "Router",
                "Switch"
              ]
            }
          },
          "memory_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Memory usage percentage.",
            "nullable": true,
            "when": {
              "column": "device_type",
              "in": [
                "Router",
                "Switch"
              ]
            }
          },
          "timestamp": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Time of the recorded metrics.",
            "dayProfile": "waking"
          }
        }
      },
      "interfaces": {
        "description": "This table captures the status of network interfaces associated with devices, linking to the devices table. Each row indicates the operational status of an interface at a specific time.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the interface."
          },
          "device_id": {
            "kind": "fk",
            "ref": "devices.id",
            "description": "Reference to the device the interface belongs to."
          },
          "interface_name": {
            "kind": "fullName",
            "description": "Name of the network interface."
          },
          "status": {
            "kind": "enum",
            "values": [
              "Up",
              "Down",
              "Maintenance"
            ],
            "description": "Operational status of the interface."
          },
          "last_checked": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Last time the status was checked.",
            "nullable": true,
            "when": {
              "column": "status",
              "in": [
                "Down",
                "Maintenance"
              ]
            },
            "dayProfile": "business"
          }
        }
      },
      "alerts": {
        "description": "This table logs alert occurrences related to device and interface issues, linking to both devices and interfaces. Each row represents an alert generated by the monitoring system.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the alert."
          },
          "device_id": {
            "kind": "fk",
            "ref": "devices.id",
            "description": "Reference to the device associated with the alert."
          },
          "interface_id": {
            "kind": "fk",
            "ref": "interfaces.id",
            "description": "Reference to the interface associated with the alert."
          },
          "alert_type": {
            "kind": "enum",
            "values": [
              "Critical",
              "Warning",
              "Info"
            ],
            "description": "Type of the alert."
          },
          "alert_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Time when the alert was generated.",
            "dayProfile": "waking",
            "trend": "growing"
          },
          "resolved": {
            "kind": "bool",
            "trueRate": 0.6,
            "description": "Indicates if the alert has been resolved.",
            "nullable": true,
            "when": {
              "column": "alert_type",
              "in": [
                "Critical",
                "Warning"
              ]
            }
          }
        }
      },
      "maintenance": {
        "description": "This table tracks scheduled maintenance activities for devices and interfaces, linking to both the devices and interfaces tables. Each row shows a maintenance event's details.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the maintenance event."
          },
          "device_id": {
            "kind": "fk",
            "ref": "devices.id",
            "description": "Reference to the device undergoing maintenance."
          },
          "interface_id": {
            "kind": "fk",
            "ref": "interfaces.id",
            "description": "Reference to the interface undergoing maintenance."
          },
          "maintenance_date": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Scheduled date for the maintenance.",
            "trend": "seasonal"
          },
          "duration": {
            "kind": "int",
            "min": 1,
            "max": 24,
            "description": "Duration of the maintenance in hours.",
            "nullable": true
          }
        }
      },
      "usage_patterns": {
        "description": "This table captures usage patterns for devices over time, linking to the devices table. Each row indicates usage statistics during peak hours.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for the usage pattern record."
          },
          "device_id": {
            "kind": "fk",
            "ref": "devices.id",
            "description": "Reference to the device being monitored."
          },
          "peak_usage_time": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Time when peak usage was observed.",
            "nullable": true,
            "dayProfile": "waking"
          },
          "data_usage": {
            "kind": "decimal",
            "min": 0,
            "max": 1000,
            "scale": 2,
            "description": "Data usage in GB during peak hours.",
            "nullable": true
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "timestamped",
      "monetary",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the common peak usage times for different devices?",
      "How often do alerts occur, and what types are most prevalent?",
      "What is the average duration of maintenance activities across devices?",
      "Which devices show the highest CPU and memory usage under load?",
      "How do interface statuses correlate with alert occurrences?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Devices and Interfaces",
        "sql": "SELECT d.device_name, i.interface_name, i.status FROM devices d JOIN interfaces i ON d.id = i.device_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Alerts by Type",
        "sql": "SELECT alert_type, COUNT(*) FROM alerts GROUP BY alert_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average Maintenance Duration Per Device",
        "sql": "SELECT d.device_name, AVG(m.duration) FROM maintenance m JOIN devices d ON m.device_id = d.id GROUP BY d.device_name;",
        "dialect": "postgres"
      },
      {
        "title": "Peak Usage Times Analysis",
        "sql": "SELECT device_id, COUNT(*) AS peak_count FROM usage_patterns GROUP BY device_id ORDER BY peak_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Alerts over Time",
        "sql": "SELECT DATE(alert_time) AS alert_date, COUNT(*) FROM alerts GROUP BY alert_date ORDER BY alert_date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Network Monitoring Dashboard",
        "body": "Network administrators can utilize this dataset to create dashboards that visualize real-time performance metrics of various devices within the telecom infrastructure. By analyzing device metrics and alert occurrences, they can quickly identify and address potential issues before they escalate."
      },
      {
        "title": "Performance Reporting",
        "body": "Analysts can generate detailed reports on network performance over specific periods, using historical data to spot trends and anomalies. This information can be shared with management to inform decisions regarding infrastructure investments and upgrades."
      },
      {
        "title": "Predictive Maintenance",
        "body": "Using the maintenance data along with performance metrics, telecom companies can develop predictive maintenance strategies. By analyzing usage patterns and alert occurrences, they can schedule maintenance during off-peak times to minimize service disruption."
      },
      {
        "title": "Capacity Planning",
        "body": "Service planners can leverage the usage patterns and device performance data to forecast future capacity needs. By understanding peak usage times and device health trends, they can allocate resources more effectively and plan for future expansions."
      }
    ]
  },
  "counts": {
    "devices": 200,
    "interfaces": 300,
    "alerts": 150,
    "maintenance": 100,
    "usage_patterns": 200
  },
  "stats": {
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