{ "format": "dataset-manifest/1", "slug": "logistics-network-insights", "version": 2, "seed": "logistics-network-insights:1", "spec": { "format": "dataset/1", "slug": "logistics-network-insights", "name": "Logistics Network Insights", "tagline": "Shipments, products, categories, customers, and shipping methods in a logistics network.", "description": "Logistics networks optimize the distribution of goods, ensuring that products reach consumers efficiently and effectively. In this operation, products are categorized and promptly shipped to customers using various shipping methods. Each shipment represents a connection between a product, its category, the chosen shipping method, and the customer receiving it. This creates a dynamic environment where the flow of information and goods is critical for success. \n\nThe tables in this dataset include products, categories, shipments, shipping_methods, and customers. Each row in the products table details an individual item available for sale, linking to its respective category in the categories table, which organizes items into relevant groups for better management. The shipments table documents the transactions, recording the specifics of each shipment alongside the shipping method used, highlighting the efficiency of different delivery options. The customers table captures essential data about buyers, allowing for segmenting and targeted marketing strategies. \n\nExploring the relationships among these tables offers valuable insights into customer behavior, shipping efficiency, and product popularity. For example, one can analyze which shipping methods yield the fastest delivery times for specific product categories or identify trends in customer preferences regarding shipping options. Additionally, the dataset can help answer questions like which categories of products are shipped most frequently and how shipping methods impact overall customer satisfaction.\n\nAs a synthetic dataset, it is designed to provide a realistic yet controlled environment for developers seeking to practice data manipulation, analysis, and visualization in the logistics domain. This makes it an excellent resource for honing skills in data-driven decision-making and logistics management.", "domain": "logistics", "shape": "graph-edges", "useCases": [ "sql-practice", "teaching" ], "keywords": [ "logistics", "shipments", "products", "suppliers", "customers", "shipping methods", "data analysis", "synthetic data" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "products": { "description": "This table contains information about various products available for shipment, including their categories and pricing. Each product is linked to its respective category, allowing for analysis of product distribution across different categories.", "rows": 200, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each product." }, "name": { "kind": "words", "min": 1, "max": 3, "description": "The name of the product.", "pool": [ "widget", "gadget", "tool", "device", "item", "product", "appliance", "equipment", "material", "supply", "component", "fixture", "accessory", "unit", "article", "resource", "good", "merchandise", "offering", "stock" ] }, "category_id": { "kind": "fk", "ref": "categories.id", "description": "Foreign key linking to the product category." }, "price": { "kind": "decimal", "min": 10, "max": 500, "scale": 2, "description": "Price of the product in USD.", "nullable": true, "distribution": "long-tail" } } }, "categories": { "description": "This table lists the various categories under which products are classified. It helps in analyzing trends across different types of products.", "rows": 20, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each category." }, "name": { "kind": "words", "min": 1, "max": 3, "description": "The name of the product category.", "pool": [ "electronics", "furniture", "clothing", "accessories", "toys", "appliances", "tools", "supplies", "equipment", "materials", "automotive", "healthcare", "home", "garden", "sports", "outdoors", "food", "beverages", "stationery", "gifts" ] }, "description": { "kind": "sentence", "max": 150, "description": "A brief description of the category.", "nullable": true } } }, "shipments": { "description": "This table records information about product shipments, including their shipping methods and status. It links products to their shipment details, allowing for tracking of shipment efficiency.", "rows": 300, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each shipment." }, "product_id": { "kind": "fk", "ref": "products.id", "description": "Foreign key linking to the shipped product." }, "shipping_method_id": { "kind": "fk", "ref": "shipping_methods.id", "description": "Foreign key linking to the shipping method used.", "nullable": true, "when": { "column": "status", "in": [ "shipped", "delivered" ] } }, "status": { "kind": "enum", "values": [ "pending", "shipped", "delivered", "returned" ], "description": "Current status of the shipment.", "weights": [ 5, 10, 15, 2 ] }, "shipment_date": { "kind": "date", "from": "2023-01-01", "to": "2023-12-31", "description": "Date when the shipment was processed.", "nullable": true, "when": { "column": "status", "in": [ "shipped", "delivered" ] }, "trend": "growing" } } }, "shipping_methods": { "description": "This table outlines different shipping methods available for product delivery. It allows analysis of shipping preferences and effectiveness.", "rows": 10, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each shipping method." }, "name": { "kind": "words", "min": 1, "max": 3, "description": "The name of the shipping method.", "pool": [ "air", "ground", "sea", "express", "standard", "overnight", "freight", "cargo", "delivery", "pickup", "dropoff", "international", "domestic", "same-day", "scheduled", "bulk", "tracked", "untracked", "priority", "economy" ] }, "cost": { "kind": "decimal", "min": 5, "max": 100, "scale": 2, "description": "Cost associated with the shipping method in USD.", "nullable": true, "distribution": "long-tail" } } }, "customers": { "description": "This table captures customer information, including their interactions with products and suppliers. It is crucial for analyzing customer behavior and preferences.", "rows": 150, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each customer." }, "first_name": { "kind": "firstName", "description": "First name of the customer." }, "last_name": { "kind": "lastName", "description": "Last name of the customer." }, "email": { "kind": "email", "from": [ "first_name", "last_name" ], "description": "Email address of the customer." }, "loyalty_points": { "kind": "int", "min": 0, "max": 1000, "description": "Loyalty points accumulated by the customer.", "nullable": true, "distribution": "normal" } } } }, "structure": "multi-table", "traits": [ "categorical", "nullable", "high-cardinality", "timestamped" ], "analysisQuestions": [ "What shipping methods are most frequently used for different product categories?", "How do customer loyalty points correlate with the frequency of product purchases?", "What trends can be observed in shipment statuses over time?", "Which product categories are most popular among customers?", "How does the cost of shipping methods influence customer choices?" ], "suggestedQueries": [ { "title": "Join Products and Categories", "sql": "SELECT p.name, c.name FROM products p JOIN categories c ON p.category_id = c.id;", "dialect": "postgres" }, { "title": "Count Shipments by Status", "sql": "SELECT status, COUNT(*) FROM shipments GROUP BY status;", "dialect": "postgres" }, { "title": "Average Shipping Cost by Method", "sql": "SELECT sm.name, AVG(sm.cost) FROM shipping_methods sm GROUP BY sm.name;", "dialect": "postgres" }, { "title": "Customer Purchase Trends", "sql": "SELECT c.first_name, c.last_name, COUNT(s.id) AS shipment_count FROM customers c LEFT JOIN shipments s ON c.id = s.customer_id GROUP BY c.id;", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Logistics Course", "body": "In a logistics course, instructors can use this dataset to teach students about the complexities of product distribution. By analyzing the relationships between products, shipments, and shipping methods, students can gain a practical understanding of logistics operations." }, { "title": "Data Analysis Workshops", "body": "Data analysts can leverage this dataset during workshops to practice SQL queries. By exploring shipment trends and customer behavior, they can enhance their skills in data manipulation and reporting." }, { "title": "Supply Chain Management Training", "body": "During supply chain management training sessions, participants can utilize this dataset to simulate real-world scenarios. They can analyze how different shipping methods impact delivery times and costs, helping them make informed decisions in their future careers." }, { "title": "Business Intelligence Projects", "body": "Business intelligence students can utilize this dataset to create dashboards and visualizations. By examining customer interactions and shipment data, they can develop insights that inform business strategies and operational improvements." } ] }, "counts": { "categories": 20, "customers": 150, "products": 200, "shipping_methods": 10, "shipments": 300 }, "stats": { "products": { "rows": 200, "columns": { "id": { "nulls": 0, "distinct": 200, "min": 1, "max": 200 }, "name": { "nulls": 0, "distinct": 144 }, "category_id": { "nulls": 0, "distinct": 20, "min": 1, "max": 20 }, "price": { "nulls": 15, "distinct": 183, "min": 10, "max": 497.96 } } }, "categories": { "rows": 20, "columns": { "id": { "nulls": 0, "distinct": 20, "min": 1, "max": 20 }, "name": { "nulls": 0, "distinct": 18 }, "description": { "nulls": 4, "distinct": 16 } } }, "shipments": { "rows": 300, "columns": { "id": { "nulls": 0, "distinct": 300, "min": 1, "max": 300 }, "product_id": { "nulls": 0, "distinct": 152, "min": 1, "max": 200 }, "shipping_method_id": { "nulls": 67, "distinct": 10, "min": 1, "max": 10 }, "status": { "nulls": 0, "distinct": 4, "min": "delivered", "max": "shipped" }, "shipment_date": { "nulls": 67, "distinct": 167, "min": "2023-01-01", "max": "2023-12-31" } } }, "shipping_methods": { "rows": 10, "columns": { "id": { "nulls": 0, "distinct": 10, "min": 1, "max": 10 }, "name": { "nulls": 0, "distinct": 10 }, "cost": { "nulls": 2, "distinct": 8, "min": 5.16, "max": 52.77 } } }, "customers": { "rows": 150, "columns": { "id": { "nulls": 0, "distinct": 150, "min": 1, "max": 150 }, "first_name": { "nulls": 0, "distinct": 64, "min": "Amir", "max": "Zoe" }, "last_name": { "nulls": 0, "distinct": 73, "min": "Adams", "max": "Zhang" }, "email": { "nulls": 0, "distinct": 149 }, "loyalty_points": { "nulls": 13, "distinct": 122, "min": 173, "max": 831 } } } }, "artifacts": [ { "file": "csv/categories.csv", "format": "csv", "table": "categories", "key": "datasets/logistics-network-insights/v2/csv/categories.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/csv/categories.csv", "bytes": 1352, "sha256": "ab55fb70982c484b60601a07f4896fea3e1a07aafb090b2251854b29aa126d00", "rows": 20, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/customers.csv", "format": "csv", "table": "customers", "key": "datasets/logistics-network-insights/v2/csv/customers.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/csv/customers.csv", "bytes": 6679, "sha256": "d6f64e13e3cd5b9c2187eeb15ce8f44538096a5593f9038db96a56395f13ef18", "rows": 150, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/products.csv", "format": "csv", "table": "products", "key": "datasets/logistics-network-insights/v2/csv/products.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/csv/products.csv", "bytes": 5723, "sha256": "207bddd03e05243916e60d2e9859d54c64a8ec9a29fe15df14f0be9ffc8233ba", "rows": 200, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/shipping_methods.csv", "format": "csv", "table": "shipping_methods", "key": "datasets/logistics-network-insights/v2/csv/shipping_methods.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/csv/shipping_methods.csv", "bytes": 253, "sha256": "8d697a0e9e1b52d17584ff59e03d4b556209eef0a381388477eb88683133cbea", "rows": 10, "contentType": "text/csv; charset=utf-8" }, { "file": "csv/shipments.csv", "format": "csv", "table": "shipments", "key": "datasets/logistics-network-insights/v2/csv/shipments.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/csv/shipments.csv", "bytes": 8390, "sha256": "a4139bbeda95b87b8093b3811eba5debf115a9f8cf7e9da666ae9c87221d174e", "rows": 300, "contentType": "text/csv; charset=utf-8" }, { "file": "logistics-network-insights.json", "format": "json", "table": null, "key": "datasets/logistics-network-insights/v2/logistics-network-insights.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/logistics-network-insights.json", "bytes": 93012, "sha256": "fc39ff85a3fcb5961b608f834baa1616439e43da1e36bf0ed2b4a4098a80c1ae", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/logistics-network-insights/v2/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/sql/postgres.sql", "bytes": 34541, "sha256": "096beaf8fe960ea962625bbc0a45c20b22956b185dca9900469519fab4dd541a", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/logistics-network-insights/v2/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/sql/mysql.sql", "bytes": 33970, "sha256": "2af9363604597b64cc82f32532b67fbe057deb487feffe16bea26bbed16b1e26", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/logistics-network-insights/v2/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/sql/mssql.sql", "bytes": 35544, "sha256": "286a083d24b8491a7f3e880c3c68655f6c637faf05b46423abc6799cdb20bc3c", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/logistics-network-insights/v2/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/sql/sqlite.sql", "bytes": 33795, "sha256": "c43c6235ab6888a56dfb7ff75e4faf75eadc2465a76ff0c3a990d2ce68d1f674", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/logistics-network-insights/v2/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/prisma/seed.ts", "bytes": 71962, "sha256": "1539b816ecea9adb5802a9ce24c80470a06948eae54a9bad480e69648e78a69a", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/logistics-network-insights/v2/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/logistics-network-insights/v2/django/fixtures.json", "bytes": 121371, "sha256": "804b64c0ac8498c41040850176f1793ca6c6eba90cf03baf818f06fa125254e8", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788766817355, "updatedAt": 1788784749921 }