{
  "format": "dataset-manifest/1",
  "slug": "bitcoin-altcoin-exchange",
  "version": 2,
  "seed": "bitcoin-altcoin-exchange:1",
  "spec": {
    "format": "dataset/1",
    "slug": "bitcoin-altcoin-exchange",
    "name": "Cryptocurrency Exchange Activity",
    "tagline": "User transactions, referrals, fees, and trades in a cryptocurrency exchange platform.",
    "description": "Users actively trade digital assets, navigating the complexities of account security and compliance in the fast-paced cryptocurrency market. This exchange specializes in facilitating transactions between altcoins and fiat currencies, handling everything from deposits and withdrawals to referral engagements and user fee structures. The platform accommodates a diverse user base, where most individuals engage in regular trading with small to moderate transaction sizes, while a smaller segment may involve themselves in larger withdrawals or intricate referral schemes, illustrating the spectrum of financial behaviors present within the exchange.\n\nThe dataset comprises several interconnected tables that provide a holistic view of user activity. The 'users' table contains individual user profiles, capturing essential information such as account creation dates and user roles. It links to the 'transactions' table, where each row represents a specific transaction, detailing amounts, timestamps, and types (deposits or withdrawals). Additionally, the 'referrals' table tracks user referral activities, connecting users who bring in new participants, and highlights incentives earned through these referrals. The 'fees' table outlines the fee structures associated with different transaction types, giving insights into the cost dynamics of trading. Finally, the 'trades' table records individual trading actions, including pairs traded, volumes, and prices at transaction timestamps, linking back to both users and transactions.\n\nThis dataset provides valuable opportunities for practice in data analysis, allowing developers to explore crucial questions such as user engagement patterns, transaction volumes, fee impacts on trading behavior, and the effectiveness of referral programs. Synthetic in nature, the data allows for safe experimentation without concerns over real-user privacy or compliance issues, making it ideal for those looking to sharpen their analytical skills.",
    "domain": "banking",
    "shape": "slowly-changing",
    "useCases": [
      "etl-pipelines",
      "teaching"
    ],
    "keywords": [
      "cryptocurrency",
      "exchange",
      "transactions",
      "users",
      "fees",
      "referrals",
      "altcoins",
      "fiat",
      "trading"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "This table contains information about users registered on the cryptocurrency exchange. Each user has a unique account and can perform multiple transactions.",
        "rows": 200,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "username": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Unique username chosen by the user.",
            "pool": [
              "crypto",
              "block",
              "chain",
              "trade",
              "coin",
              "token",
              "miner",
              "wallet",
              "hash",
              "ledger",
              "stake",
              "decentral",
              "node",
              "peer",
              "hashrate",
              "altcoin",
              "dapp",
              "satoshi",
              "fomo",
              "hodl",
              "pump",
              "dump",
              "whale",
              "fork",
              "scam",
              "defi",
              "yield",
              "liquidity",
              "swap",
              "gas",
              "fee",
              "market",
              "bull",
              "bear",
              "signal",
              "trend",
              "analysis",
              "portfolio",
              "exchange",
              "arbitrage",
              "risk",
              "investment",
              "capital",
              "strategy",
              "growth",
              "dividend",
              "security",
              "protocol",
              "utility",
              "reward",
              "claim",
              "stakeholder",
              "community",
              "innovate",
              "venture"
            ]
          },
          "email": {
            "kind": "email",
            "description": "Email address of the user."
          },
          "account_created_at": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "nullable": true,
            "description": "Date when the user created their account."
          }
        }
      },
      "transactions": {
        "description": "This table records all transactions made by users, including deposits and withdrawals. Each transaction is linked to a user.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Identifier linking to the user who made the transaction."
          },
          "transaction_type": {
            "kind": "enum",
            "values": [
              "deposit",
              "withdrawal"
            ],
            "description": "Type of transaction performed by the user."
          },
          "amount": {
            "kind": "decimal",
            "min": 10,
            "max": 10000,
            "scale": 2,
            "description": "Amount of money involved in the transaction.",
            "distribution": "long-tail"
          },
          "transaction_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date when the transaction took place."
          }
        }
      },
      "referrals": {
        "description": "This table captures referral activities, where users can refer others to join the exchange. Each referral is linked to a user.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each referral."
          },
          "referrer_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Identifier linking to the user who made the referral."
          },
          "referred_email": {
            "kind": "email",
            "description": "Email address of the referred user."
          },
          "referral_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date when the referral was made."
          }
        }
      },
      "fees": {
        "description": "This table tracks the fee structures related to transactions on the exchange, linked to user activities.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each fee entry."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Identifier linking to the user associated with the fee."
          },
          "fee_amount": {
            "kind": "decimal",
            "min": 0.01,
            "max": 500,
            "scale": 2,
            "description": "Amount charged as a fee for transactions.",
            "distribution": "long-tail"
          },
          "fee_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date when the fee was applied."
          }
        }
      },
      "trades": {
        "description": "This table documents the trades executed on the exchange, detailing the exchange of altcoins and fiat currencies.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each trade."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Identifier linking to the user who executed the trade."
          },
          "trade_amount": {
            "kind": "decimal",
            "min": 1,
            "max": 10000,
            "scale": 2,
            "description": "Amount of altcoin traded.",
            "distribution": "long-tail"
          },
          "trade_date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2024-12-31",
            "description": "Date when the trade was executed."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "nullable",
      "categorical",
      "timestamped"
    ],
    "analysisQuestions": [
      "What is the average transaction amount per user over the specified period?",
      "How many users have referred others, and what is the conversion rate of referrals?",
      "What is the distribution of transaction types among users?",
      "Which users incur the highest fees over time?",
      "How do trade volumes fluctuate over different months?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users with Transactions",
        "sql": "SELECT users.username, transactions.amount FROM users JOIN transactions ON users.id = transactions.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Aggregate Transaction Amounts by User",
        "sql": "SELECT user_id, SUM(amount) AS total_transacted FROM transactions GROUP BY user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Count Referrals per User",
        "sql": "SELECT referrer_id, COUNT(*) AS referral_count FROM referrals GROUP BY referrer_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Fees by User",
        "sql": "SELECT user_id, AVG(fee_amount) AS average_fee FROM fees GROUP BY user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Trade Volume Over Time",
        "sql": "SELECT DATE(trade_date) AS trade_day, SUM(trade_amount) AS total_traded FROM trades GROUP BY trade_day ORDER BY trade_day;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Financial Analysts",
        "body": "Financial analysts can utilize this dataset to study transaction patterns and user engagement over time. By analyzing transaction amounts and types, they can identify trends in user behavior and assess the overall health of the cryptocurrency exchange."
      },
      {
        "title": "Educators in Finance",
        "body": "Educators can leverage this dataset to teach concepts related to cryptocurrency trading and financial compliance. The structured tables allow students to explore real-world scenarios, enhancing their understanding of digital asset management and trading strategies."
      },
      {
        "title": "Data Engineers",
        "body": "Data engineers can use this synthetic dataset to practice building ETL pipelines. They can extract, transform, and load the data into different formats, ensuring that they understand how to handle financial data effectively while maintaining data integrity."
      },
      {
        "title": "Compliance Officers",
        "body": "Compliance officers can analyze fees and referral activities to ensure adherence to regulatory standards. By investigating user behaviors and transaction histories, they can identify any suspicious activities or patterns that may require further scrutiny."
      }
    ]
  },
  "counts": {
    "users": 200,
    "fees": 100,
    "referrals": 150,
    "trades": 300,
    "transactions": 500
  },
  "stats": {
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