{
  "format": "dataset-manifest/1",
  "slug": "interest-engagement-metrics",
  "version": 2,
  "seed": "interest-engagement-metrics:1",
  "spec": {
    "format": "dataset/1",
    "slug": "interest-engagement-metrics",
    "name": "Interest Engagement Metrics",
    "tagline": "Metrics on user engagement, interests, events, and interactions in the telecom industry.",
    "description": "Users create profiles, join groups, and engage in events related to their telecom interests, driving interactions and discussions within the community. As users participate in these events, they contribute insights, comments, and likes, fostering connections with others who share similar passions. The dynamic nature of these interactions highlights how users gravitate towards popular topics while also allowing for specialized interests to emerge, albeit with fewer participants.\n\nThe data model includes several key tables: 'users' captures individual user profiles and their unique identifiers; 'interest_categories' categorizes various telecom topics that users can engage with; 'events' tracks organized activities where users can gather; and 'engagements' records user interactions with content—such as comments and likes. Each user in the 'users' table can be linked to multiple entries in the 'engagements' table, showcasing their interactions over time. The 'events' table links to 'users' to illustrate attendance and participation, while 'interest_categories' connects with both 'users' and 'events' to frame discussions around particular topics.\n\nDevelopers can leverage this synthetic data to answer pertinent questions about user behavior: Which interest categories generate the most engagement? What types of events attract the highest attendance? How do user interactions vary across different telecom topics? By practicing with this dataset, developers can explore relationships between user demographics, interests, and engagement metrics, enabling them to build insights that could enhance user experiences in similar applications.",
    "domain": "telecom",
    "shape": "event-log",
    "useCases": [
      "analytics-demo",
      "etl-pipelines"
    ],
    "keywords": [
      "telecom",
      "networking",
      "user engagement",
      "events",
      "interests",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "Each row represents a user profile in the networking app, linking to their engagement activities and interests.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the user.",
            "nullable": true
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The user's email address, derived from their full name.",
            "nullable": true
          },
          "joined_at": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the user joined the app.",
            "dayProfile": "waking"
          }
        }
      },
      "interest_categories": {
        "description": "This table categorizes different telecom interests that users can engage with, linking to related events and user interactions.",
        "rows": 30,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interest category."
          },
          "name": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the telecom interest category.",
            "pool": [
              "mobile",
              "broadband",
              "internet",
              "gaming",
              "streaming",
              "social",
              "music",
              "news",
              "sports",
              "technology",
              "apps",
              "ecommerce",
              "education",
              "travel",
              "health",
              "finance",
              "lifestyle",
              "entertainment",
              "advertising",
              "communication",
              "community"
            ]
          },
          "description": {
            "kind": "sentence",
            "description": "A brief description of the interest category.",
            "nullable": true
          }
        }
      },
      "events": {
        "description": "Each row records an event organized around a specific interest category, linking users who attended.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each event."
          },
          "name": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the event.",
            "nullable": true,
            "pool": [
              "launch",
              "promotion",
              "webinar",
              "conference",
              "workshop",
              "seminar",
              "contest",
              "survey",
              "networking",
              "meetup",
              "festival",
              "exhibition",
              "presentation",
              "demo",
              "panel",
              "discussion",
              "forum",
              "training",
              "class",
              "event",
              "gathering"
            ]
          },
          "date": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the event takes place.",
            "trend": "seasonal"
          },
          "interest_category_id": {
            "kind": "fk",
            "ref": "interest_categories.id",
            "description": "Foreign key linking to the interest category for the event."
          }
        }
      },
      "engagements": {
        "description": "This table tracks user interactions with events, including comments and likes, linking back to the user and the event.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each engagement."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Foreign key linking to the user who engaged."
          },
          "event_id": {
            "kind": "fk",
            "ref": "events.id",
            "description": "Foreign key linking to the event being engaged with."
          },
          "engagement_type": {
            "kind": "enum",
            "values": [
              "like",
              "comment"
            ],
            "description": "Type of engagement the user had with the event.",
            "weights": [
              7,
              3
            ]
          },
          "created_at": {
            "kind": "datetime",
            "from": "2022-01-01",
            "to": "2023-12-31",
            "description": "Date and time when the engagement occurred.",
            "nullable": true,
            "when": {
              "column": "engagement_type",
              "in": [
                "comment"
              ]
            },
            "dayProfile": "business"
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "Which interest categories have the highest user engagement?",
      "What time of day do users engage with events the most?",
      "How does engagement vary between different events?",
      "Are there specific users who frequently engage across multiple interest categories?"
    ],
    "suggestedQueries": [
      {
        "title": "Get all users and their engagement types",
        "sql": "SELECT u.full_name, e.name AS event_name, en.engagement_type FROM users u JOIN engagements en ON u.id = en.user_id JOIN events e ON en.event_id = e.id;",
        "dialect": "postgres"
      },
      {
        "title": "Count engagement types per event",
        "sql": "SELECT e.name, COUNT(en.id) AS engagement_count FROM events e LEFT JOIN engagements en ON e.id = en.event_id GROUP BY e.name;",
        "dialect": "postgres"
      },
      {
        "title": "Find users with the most likes",
        "sql": "SELECT u.full_name, COUNT(en.id) AS like_count FROM users u JOIN engagements en ON u.id = en.user_id WHERE en.engagement_type = 'like' GROUP BY u.full_name ORDER BY like_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Analyze engagement trends over time",
        "sql": "SELECT DATE_TRUNC('month', en.created_at) AS month, COUNT(en.id) AS total_engagements FROM engagements en GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Telecom Event Analysis",
        "body": "Telecom analysts can utilize this dataset to examine user engagement metrics across various events and interest categories. By analyzing the interactions, they can determine which topics resonate most with users and adjust future events accordingly."
      },
      {
        "title": "User Behavior Insights",
        "body": "Data scientists can leverage this dataset to explore user behavior patterns in the telecom networking app. Understanding engagement trends can help refine marketing strategies and enhance user retention efforts, ensuring that the platform remains engaging."
      },
      {
        "title": "Performance Tracking for Events",
        "body": "Event organizers can use this dataset to track the performance of different events based on user engagement metrics. By identifying high-performing events, organizers can replicate successful elements in future events to boost participation."
      },
      {
        "title": "Interest Category Development",
        "body": "Product managers can analyze this data to identify gaps in interest categories and develop new topics that cater to user interests. This insight can drive content creation and enhance user satisfaction within the app."
      }
    ]
  },
  "counts": {
    "interest_categories": 30,
    "events": 150,
    "users": 200,
    "engagements": 500
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