{
  "format": "dataset-manifest/1",
  "slug": "connected-voices",
  "version": 1,
  "seed": "connected-voices:1",
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    "format": "dataset/1",
    "slug": "connected-voices",
    "name": "Connected Voices",
    "tagline": "User profiles, voice messages, playlists, and interactions within a social platform's community.",
    "description": "Users engage through voice-activated interactions on a vibrant social platform, creating a dynamic space for connection and sharing. The dataset models user profiles, voice messages, playlists, and the intricate network of follows and likes that shape relationships within this community. Users typically maintain close connections with a small circle of friends, while some rise to prominence as influencers with larger followings. This dataset captures the interactions that occur, highlighting how posts cluster around trending tags and categories, reflecting the current interests of the user base. By practicing with this synthetic data, users can explore social network analysis, query relationships, and examine user engagement patterns effectively.",
    "domain": "social",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "social network",
      "voice interactions",
      "user profiles",
      "engagement",
      "influencers",
      "followers",
      "likes",
      "playlists",
      "trending tags"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "user_interactions": {
        "description": "This table captures user interactions such as follows, likes, and voice message exchanges. Each row represents a unique interaction between users, linking their profiles through the user_id and connected_user_id columns.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each interaction record."
          },
          "user_id": {
            "kind": "fk",
            "ref": "user_interactions.id",
            "nullable": true,
            "description": "The ID of the user initiating the interaction."
          },
          "connected_user_id": {
            "kind": "fk",
            "ref": "user_interactions.id",
            "nullable": true,
            "description": "The ID of the user receiving the interaction."
          },
          "interaction_type": {
            "kind": "enum",
            "values": [
              "follow",
              "like",
              "voice_message"
            ],
            "description": "Type of interaction, indicating whether it's a follow, a like, or a voice message."
          },
          "created_at": {
            "kind": "datetime",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "Timestamp indicating when the interaction occurred.",
            "dayProfile": "waking"
          },
          "tags": {
            "kind": "words",
            "max": 10,
            "description": "Tags associated with the interaction, highlighting themes or subjects."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What are the most common types of interactions among users?",
      "How do follows and likes correlate with user engagement?",
      "Which tags are most frequently associated with voice messages?",
      "What patterns can be observed in interactions over time?",
      "How do influencer interactions differ from regular users?"
    ],
    "suggestedQueries": [
      {
        "title": "Get all interactions by a specific user",
        "sql": "SELECT * FROM user_interactions WHERE user_id = ?;",
        "dialect": "postgres"
      },
      {
        "title": "Count interactions by type",
        "sql": "SELECT interaction_type, COUNT(*) FROM user_interactions GROUP BY interaction_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find trending tags over time",
        "sql": "SELECT tags, COUNT(*) FROM user_interactions WHERE created_at >= '2023-01-01' GROUP BY tags ORDER BY COUNT(*) DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Analyze user engagement patterns",
        "sql": "SELECT user_id, COUNT(*) AS engagement_count FROM user_interactions GROUP BY user_id ORDER BY engagement_count DESC;",
        "dialect": "postgres"
      },
      {
        "title": "Explore influencer interactions",
        "sql": "SELECT connected_user_id, COUNT(*) FROM user_interactions WHERE interaction_type = 'follow' GROUP BY connected_user_id HAVING COUNT(*) > 100;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Social Network Analysis",
        "body": "Data analysts can utilize this dataset to study the relationships and interactions within a voice-activated social platform. By querying user connections, follows, and likes, they can gain insights into the structure of user engagement and identify key influencers in the community."
      },
      {
        "title": "Teaching SQL Queries",
        "body": "Educators can employ this dataset in classroom settings to teach students about SQL querying techniques. The diverse range of interactions allows students to practice writing queries for various use cases, such as aggregations and joins, enhancing their understanding of database management."
      },
      {
        "title": "User Engagement Studies",
        "body": "Marketing professionals can analyze this data to understand user behavior and engagement patterns. By examining the types of interactions and associated tags, they can tailor their campaigns to align with trending topics and enhance user retention strategies."
      }
    ]
  },
  "counts": {
    "user_interactions": 500
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  "stats": {
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          "distinct": 235,
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        "connected_user_id": {
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          "distinct": 237,
          "min": 1,
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        "interaction_type": {
          "nulls": 0,
          "distinct": 3,
          "min": "follow",
          "max": "voice_message"
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          "nulls": 0,
          "distinct": 500,
          "min": "2023-01-01 14:57:01",
          "max": "2023-12-31 17:53:05"
        },
        "tags": {
          "nulls": 0,
          "distinct": 500
        }
      }
    }
  },
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