{
  "format": "dataset-manifest/1",
  "slug": "engagement-metrics-graph",
  "version": 1,
  "seed": "engagement-metrics-graph:1",
  "spec": {
    "format": "dataset/1",
    "slug": "engagement-metrics-graph",
    "name": "Engagement Metrics Graph",
    "tagline": "User engagement data on social media, including interactions with quizzes, listicles, and newsletters.",
    "description": "Engagement Metrics Graph is a synthetic dataset that models user interactions on a social media platform, capturing the dynamics of how users engage with various content formats such as quizzes, listicles, and newsletters. This dataset is designed for developers and data enthusiasts looking to practice SQL queries and explore graph-based analytics in a social context. Each row represents an interaction, detailing the type of engagement, the content involved, and timestamps of these interactions. The relationships between different types of engagements can be analyzed to understand user behavior patterns, such as peak engagement times for specific content formats and the correlation between types of interactions. By working with this dataset, users can develop valuable skills in querying graph-structured data. The dataset is synthetic, ensuring that it provides a safe and flexible environment for experimentation and learning.",
    "domain": "social",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "engagement",
      "social media",
      "user interactions",
      "quizzes",
      "content sharing",
      "analytics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "engagement_metrics": {
        "description": "This table captures user interactions with various content types on the platform. Each row represents a unique engagement, linking users to the content they interacted with and the type of interaction.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each engagement record."
          },
          "user_id": {
            "kind": "uuid",
            "description": "Identifier for the user engaging with the content.",
            "nullable": true
          },
          "content_id": {
            "kind": "uuid",
            "description": "Identifier for the content being engaged with.",
            "nullable": true
          },
          "engagement_type": {
            "kind": "enum",
            "values": [
              "like",
              "follow",
              "quiz_attempt",
              "comment_like"
            ],
            "description": "Type of engagement the user performed."
          },
          "timestamp": {
            "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": [
                "like",
                "comment_like"
              ]
            },
            "dayProfile": "waking"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "timestamped",
      "high-cardinality"
    ],
    "analysisQuestions": [
      "What types of content generate the most likes?",
      "When do users engage with quizzes the most throughout the week?",
      "How does engagement vary between different content formats?",
      "What is the average time between a follow and a quiz attempt by the same user?"
    ],
    "suggestedQueries": [
      {
        "title": "Count Engagements by Type",
        "sql": "SELECT engagement_type, COUNT(*) FROM engagement_metrics GROUP BY engagement_type;",
        "dialect": "postgres"
      },
      {
        "title": "Daily Engagement Trends",
        "sql": "SELECT DATE(timestamp) AS engagement_date, COUNT(*) FROM engagement_metrics GROUP BY engagement_date ORDER BY engagement_date;",
        "dialect": "postgres"
      },
      {
        "title": "User Engagement Patterns",
        "sql": "SELECT user_id, COUNT(*) AS total_engagements FROM engagement_metrics GROUP BY user_id ORDER BY total_engagements DESC LIMIT 10;",
        "dialect": "postgres"
      },
      {
        "title": "Engagements Over Time",
        "sql": "SELECT DATE(timestamp) AS date, COUNT(*) AS engagements FROM engagement_metrics WHERE engagement_type = 'quiz_attempt' GROUP BY date ORDER BY date;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Social Media Analyst",
        "body": "A social media analyst can use this dataset to understand user engagement trends over time, identifying peak periods for specific content types. This analysis can help inform future content strategies and optimize posting schedules."
      },
      {
        "title": "Data Science Student",
        "body": "A data science student might utilize this dataset to practice SQL queries focused on graph-structured data. They can explore user interactions and develop insights into how engagement types correlate with different content formats."
      },
      {
        "title": "Marketing Strategist",
        "body": "A marketing strategist can analyze the engagement metrics to evaluate the effectiveness of various campaigns, identifying which content formats drive the most interactions and tailoring future marketing efforts accordingly."
      }
    ]
  },
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  "stats": {
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        "content_id": {
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          "distinct": 4,
          "min": "comment_like",
          "max": "quiz_attempt"
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          "distinct": 256,
          "min": "2022-01-02 21:23:10",
          "max": "2023-12-31 07:07:32"
        }
      }
    }
  },
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