{
  "format": "dataset-manifest/1",
  "slug": "real-estate-data-connections",
  "version": 2,
  "seed": "real-estate-data-connections:1",
  "spec": {
    "format": "dataset/1",
    "slug": "real-estate-data-connections",
    "name": "Real-Estate Data Connections",
    "tagline": "Connections between properties, users, and databases in the real estate sector.",
    "description": "Real estate professionals analyze properties and transactions using diverse data sources, establishing connections that inform their decisions. This business thrives on the interplay between properties, users, and databases, with a focus on understanding how these entities interact through various metrics and alerts. The real estate connections captured in this dataset illustrate these dynamics, revealing patterns of user engagement across a landscape where properties range from actively listed to historical holdings.\n\nEach row in the real_estate_connections table represents a unique interaction between a property and a user, showcasing the types of connections that exist. This includes direct engagements with listings, inquiries made, and interactions with data systems. Users are mapped to various properties they are interested in or have accessed, giving a clear view of engagement levels and frequency. By analyzing this information, developers can understand trends in user behavior and property interest, providing valuable insights into the real estate market's ebb and flow.\n\nPracticing with this data allows developers to explore various questions such as: How do user engagement levels vary among different property types? What patterns emerge in user interactions over time? Which properties experience the highest engagement rates, and how does that correlate with market trends? This dataset serves as a practical tool for honing skills in data manipulation, querying, and analysis.\n\nIt’s important to note that the data within this dataset is synthetic, designed to mimic real-world interactions in the real estate industry. The goal is to offer a rich resource for developers looking to practice their data skills in a context that reflects genuine business operations.",
    "domain": "real-estate",
    "shape": "graph-edges",
    "useCases": [
      "sql-practice",
      "teaching"
    ],
    "keywords": [
      "real estate",
      "data connections",
      "user engagement",
      "properties",
      "market trends",
      "metrics"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "real_estate_connections": {
        "description": "Each row links a property to a user, detailing their interaction metrics and alerts. This table captures how users engage with various properties over time.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each connection."
          },
          "property_id": {
            "kind": "uuid",
            "description": "Unique identifier for the property involved in the connection."
          },
          "user_id": {
            "kind": "uuid",
            "description": "Unique identifier for the user interacting with the property."
          },
          "views": {
            "kind": "int",
            "min": 0,
            "max": 1000,
            "description": "Number of views the property received from the user.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "inquiries": {
            "kind": "int",
            "min": 0,
            "max": 100,
            "description": "Number of inquiries made by the user about the property.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "alerts_triggered": {
            "kind": "int",
            "min": 0,
            "max": 10,
            "description": "Number of alerts triggered by user activity on the property.",
            "nullable": true,
            "distribution": "long-tail"
          },
          "last_accessed": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the user last accessed the property.",
            "nullable": true,
            "trend": "growing"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "high-cardinality",
      "nullable",
      "timestamped"
    ],
    "analysisQuestions": [
      "How many views do properties receive on average from users?",
      "What is the relationship between user inquiries and the number of views?",
      "Which properties have the highest user engagement based on alerts triggered?",
      "How does user engagement vary between active and historical listings?"
    ],
    "suggestedQueries": [
      {
        "title": "Basic User Engagement Overview",
        "sql": "SELECT property_id, SUM(views) AS total_views FROM real_estate_connections GROUP BY property_id;",
        "dialect": "postgres"
      },
      {
        "title": "Inquiries per Property",
        "sql": "SELECT property_id, AVG(inquiries) AS average_inquiries FROM real_estate_connections GROUP BY property_id;",
        "dialect": "postgres"
      },
      {
        "title": "Alerts Analysis",
        "sql": "SELECT property_id, COUNT(alerts_triggered) AS alerts_count FROM real_estate_connections WHERE last_accessed >= '2023-01-01' GROUP BY property_id;",
        "dialect": "postgres"
      },
      {
        "title": "Trends Over Time",
        "sql": "SELECT DATE_TRUNC('month', last_accessed) AS month, COUNT(*) AS total_connections FROM real_estate_connections GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Real Estate Analyst",
        "body": "A real estate analyst can use this dataset to evaluate user engagement metrics across different property listings. By querying the dataset, they can identify which properties attract the most views and inquiries, helping them to provide insights into market trends and user preferences."
      },
      {
        "title": "Data Science Instructor",
        "body": "An instructor teaching data science can utilize this dataset in a classroom setting to demonstrate key SQL concepts such as joins, aggregates, and window functions. Students can practice writing queries to extract meaningful insights about user interactions and engagement in the real estate sector."
      },
      {
        "title": "Property Manager",
        "body": "A property manager can leverage the data to understand user behavior towards their listings. By analyzing the alerts triggered and inquiries made, they can tailor their marketing strategies to improve engagement and conversion rates for their properties."
      }
    ]
  },
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    "real_estate_connections": 500
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        "inquiries": {
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          "distinct": 98,
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        "alerts_triggered": {
          "nulls": 54,
          "distinct": 11,
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        "last_accessed": {
          "nulls": 47,
          "distinct": 384,
          "min": "2020-01-05",
          "max": "2023-12-30"
        }
      }
    }
  },
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