{ "format": "dataset-manifest/1", "slug": "energy-insight-transactions", "version": 1, "seed": "energy-insight-transactions:1", "spec": { "format": "dataset/1", "slug": "energy-insight-transactions", "name": "Energy Insight Transactions", "tagline": "Energy consumption metrics and transaction data from multiple organizations for resource management.", "description": "Energy professionals analyze consumption metrics and efficiency snapshots to manage resources and promote sustainability. They focus on transactions that track energy usage across various sectors, leading to insights on performance trends and efficiency improvements. Each row in the energy_transactions table represents a specific transaction that records energy consumption data associated with different organizations. This table allows users to delve into detailed performance metrics, identifying patterns and anomalies in energy use.\n\nThe energy_transactions table serves as the core data source, facilitating analysis on how resources are utilized and highlighting potential areas for improvement. Users can join this table with additional data sources, if available, to correlate specific transactions with organizational performance or external factors influencing energy consumption.\n\nThe data answers critical questions such as: What are the peak consumption periods for different organizations? How does energy efficiency vary across different clusters? Are there noticeable trends in energy usage that could impact resource allocation? Practicing with this dataset can help developers learn to extract valuable insights from transaction data, perform anomaly detection, and conduct comparative assessments of energy performance metrics.\n\nIt's important to note that all the data within this dataset is synthetic, yet it simulates realistic scenarios encountered in the energy sector. This makes it an excellent resource for developers looking to enhance their analytical skills and understand the dynamics of energy transactions in a controlled environment.", "domain": "energy", "shape": "transactional", "useCases": [ "sql-practice", "orm-seeding" ], "keywords": [ "energy consumption", "efficiency metrics", "sustainability", "resource management", "data analysis" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "energy_transactions": { "description": "Each row in this table represents a unique energy transaction, detailing metrics such as consumption, efficiency, and cluster performance. It includes both standardized metrics and outlier data tied to specialized energy reports.", "rows": 500, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each transaction." }, "transaction_date": { "kind": "datetime", "from": "2023-01-01", "to": "2023-12-31", "description": "Date and time of the transaction.", "dayProfile": "business" }, "cluster_id": { "kind": "int", "min": 1, "max": 100, "description": "Identifier for the energy cluster associated with the transaction." }, "consumption_kwh": { "kind": "decimal", "min": 0, "max": 10000, "scale": 2, "description": "Total energy consumption in kilowatt-hours." }, "efficiency_rating": { "kind": "decimal", "min": 0, "max": 1, "scale": 2, "description": "Efficiency rating of the energy usage, ranging from 0 to 1." }, "report_type": { "kind": "enum", "values": [ "standard", "specialized" ], "weights": [ 0.8, 0.2 ], "description": "Type of report associated with the transaction." }, "notes": { "kind": "sentence", "max": 250, "nullable": true, "description": "Additional notes or comments about the transaction." } } } }, "structure": "single-table", "traits": [ "monetary", "timestamped", "nullable" ], "analysisQuestions": [ "What are the peak consumption times across different clusters?", "How does the efficiency rating vary between standard and specialized reports?", "Are there any noticeable outliers in energy consumption for specific clusters?", "What trends can be observed in energy consumption over the year?", "How do different clusters compare in terms of efficiency?" ], "suggestedQueries": [ { "title": "Basic Transaction Overview", "sql": "SELECT * FROM energy_transactions LIMIT 10;", "dialect": "postgres" }, { "title": "Average Consumption by Cluster", "sql": "SELECT cluster_id, AVG(consumption_kwh) as avg_consumption FROM energy_transactions GROUP BY cluster_id;", "dialect": "postgres" }, { "title": "Efficiency Ratings Comparison", "sql": "SELECT report_type, AVG(efficiency_rating) as avg_efficiency FROM energy_transactions GROUP BY report_type;", "dialect": "postgres" }, { "title": "Monthly Consumption Trends", "sql": "SELECT DATE_TRUNC('month', transaction_date) as month, SUM(consumption_kwh) as total_consumption FROM energy_transactions GROUP BY month ORDER BY month;", "dialect": "postgres" }, { "title": "Identifying Outliers in Consumption", "sql": "SELECT * FROM energy_transactions WHERE consumption_kwh > (SELECT AVG(consumption_kwh) + 2 * STDDEV(consumption_kwh) FROM energy_transactions);", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Energy Analysts", "body": "Energy analysts can utilize the dataset to monitor and evaluate energy consumption trends across various clusters. By running SQL queries, they can identify peak usage times, assess efficiency ratings, and uncover any anomalies in energy consumption that may indicate inefficiencies." }, { "title": "Sustainability Consultants", "body": "Sustainability consultants can leverage this dataset to provide insights into resource management and energy-saving strategies. By analyzing the efficiency ratings and consumption metrics, they can recommend best practices for organizations aiming to reduce their carbon footprint." }, { "title": "Software Developers", "body": "Software developers can use the Energy Insight Transactions dataset to seed their databases during application development. This will allow them to create realistic scenarios for energy management software and test various functionalities related to energy consumption tracking." }, { "title": "Academic Researchers", "body": "Academic researchers focusing on energy studies can use this synthetic dataset to conduct analyses on energy consumption patterns and efficiency. The data can serve as a basis for experiments, simulations, or comparative studies in the field of energy management." } ] }, "counts": { "energy_transactions": 500 }, "stats": { "energy_transactions": { "rows": 500, "columns": { "id": { "nulls": 0, "distinct": 500, "min": 1, "max": 500 }, "transaction_date": { "nulls": 0, "distinct": 500, "min": "2023-01-01 10:35:38", "max": "2023-12-31 17:09:59" }, "cluster_id": { "nulls": 0, "distinct": 100, "min": 1, "max": 100 }, "consumption_kwh": { "nulls": 0, "distinct": 500, "min": 20.52, "max": 9995.72 }, "efficiency_rating": { "nulls": 0, "distinct": 101, "min": 0, "max": 1 }, "report_type": { "nulls": 0, "distinct": 2, "min": "specialized", "max": "standard" }, "notes": { "nulls": 52, "distinct": 445 } } } }, "artifacts": [ { "file": "csv/energy_transactions.csv", "format": "csv", "table": "energy_transactions", "key": "datasets/energy-insight-transactions/v1/csv/energy_transactions.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/csv/energy_transactions.csv", "bytes": 50422, "sha256": "6c805b896d0d7b4dd76eca6dcc924d4f54cf7f4c46308023de9dded09e0a4180", "rows": 500, "contentType": "text/csv; charset=utf-8" }, { "file": "energy-insight-transactions.json", "format": "json", "table": null, "key": "datasets/energy-insight-transactions/v1/energy-insight-transactions.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/energy-insight-transactions.json", "bytes": 135185, "sha256": "969f21f0357ab378b209157ee5a7d98999dfec315bc102eea18eeced8667f87c", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/energy-insight-transactions/v1/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/sql/postgres.sql", "bytes": 60463, "sha256": "3154a0de35f1c9ed4d0009eed779472ae2d5ace2c62eb5181a8774b26e61552a", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/energy-insight-transactions/v1/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/sql/mysql.sql", "bytes": 60337, "sha256": "e03be01c61ff1ddd45359358b6c64c4e718d4159da3367c4fb69dbc0eb874015", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/energy-insight-transactions/v1/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/sql/mssql.sql", "bytes": 61879, "sha256": "f07677ef036e6d7796ebac8d5c015de245f429eec237b8be44af3a55659cb3f9", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/energy-insight-transactions/v1/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/sql/sqlite.sql", "bytes": 60313, "sha256": "90ef1eba0505228792f4cf15abfd0d516042d5d3d09c62067904c48265649550", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/energy-insight-transactions/v1/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/prisma/seed.ts", "bytes": 117538, "sha256": "752831a46cc5bfb58283377e6bc48f5e1ef06b1feeaad9eff2eb324694a98e6d", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/energy-insight-transactions/v1/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/energy-insight-transactions/v1/django/fixtures.json", "bytes": 164773, "sha256": "2ea2e846b49d5b4b776fc3b1e3b7b264452bb3c0fb6f5ffaa14f57f18772387d", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788766520044, "updatedAt": 1788766520044 }