{ "format": "dataset-manifest/1", "slug": "culinary-customization-dataset", "version": 1, "seed": "culinary-customization-dataset:1", "spec": { "format": "dataset/1", "slug": "culinary-customization-dataset", "name": "Culinary Customization Dataset", "tagline": "Customer profiles, meal recipes, dietary restrictions, and ingredient preferences for personalized dining experiences.", "description": "Restaurants craft customized dining experiences, tailoring recipes and meal plans to meet the unique tastes and dietary needs of their customers. Each meal is not just a dish; it reflects individual health goals and ingredient preferences, allowing diners to enjoy meals that resonate with their personal lifestyle choices. The culinary experience is enriched by the diverse array of dietary restrictions and ingredient preferences that guests may have, resulting in an intricate web of possibilities for chefs and food service providers.\n\nThe primary table in this dataset, culinary_customization, holds detailed records of customer profiles, meal recipes, dietary restrictions, and ingredient preferences. Each row represents a unique culinary customization request, linking customer data with specific meals tailored to their requirements. This table enables a deep understanding of how different factors, such as weight loss goals or allergen avoidance, influence meal choices and recipe development. By analyzing these relationships, developers can gain insights into customer behavior, dietary trends, and the effectiveness of various meal plans.\n\nThis dataset is particularly suited for those looking to practice data analysis in the culinary domain, providing opportunities to explore questions like: How do dietary restrictions affect meal selections? What are the most common ingredient combinations for specific health goals? Which customer profiles are more likely to request certain types of meal customizations? Since the data is synthetic, it allows for extensive experimentation without concerns about privacy or real-world implications, making it an excellent resource for honing analytical skills in the restaurant industry.", "domain": "restaurants", "shape": "transactional", "useCases": [ "sql-practice", "orm-seeding" ], "keywords": [ "personalized dining", "meal plans", "dietary preferences", "recipe customization", "customer profiles" ], "locale": "en", "license": "CC0-1.0", "version": 1, "tables": { "culinary_customization": { "description": "This table holds information about personalized recipes and meal plans, linking customer profiles to their dietary needs and preferences. Each row represents a unique culinary customization tailored to an individual customer.", "rows": 500, "columns": { "id": { "kind": "pk", "description": "Unique identifier for each recipe customization." }, "customer_id": { "kind": "int", "min": 1, "max": 1000, "description": "Identifier for the customer requesting the meal plan." }, "recipe_name": { "kind": "words", "min": 3, "max": 50, "description": "The name of the personalized recipe." }, "dietary_restriction": { "kind": "enum", "values": [ "vegan", "vegetarian", "gluten-free", "nut-free", "none" ], "description": "Dietary restrictions applicable to the recipe." }, "ingredient_preferences": { "kind": "sentence", "min": 10, "max": 200, "description": "A description of preferred ingredients for the recipe.", "nullable": true, "when": { "column": "target_goal", "in": [ "weight loss", "muscle gain", "maintenance", "general health" ] } }, "target_goal": { "kind": "enum", "values": [ "weight loss", "muscle gain", "maintenance", "general health" ], "description": "The dietary goal for which the meal plan is designed." }, "creation_date": { "kind": "datetime", "from": "2020-01-01", "to": "2023-10-01", "description": "The date when the recipe customization was created.", "nullable": true, "dayProfile": "waking" } } } }, "structure": "single-table", "traits": [ "categorical", "nullable", "timestamped" ], "analysisQuestions": [ "What dietary restrictions are most common among customers?", "How do ingredient preferences vary across different dietary goals?", "Which recipes are the most popular for weight loss?", "How often are meal plans created for muscle gain compared to other goals?" ], "suggestedQueries": [ { "title": "Fetch all recipes for a customer", "sql": "SELECT * FROM culinary_customization WHERE customer_id = 1;", "dialect": "postgres" }, { "title": "Count recipes by dietary restriction", "sql": "SELECT dietary_restriction, COUNT(*) FROM culinary_customization GROUP BY dietary_restriction;", "dialect": "postgres" }, { "title": "Get recipes created within the last month", "sql": "SELECT * FROM culinary_customization WHERE creation_date >= NOW() - INTERVAL '1 month';", "dialect": "postgres" }, { "title": "Find all recipes for weight loss goals", "sql": "SELECT * FROM culinary_customization WHERE target_goal = 'weight loss';", "dialect": "postgres" } ], "usageScenarios": [ { "title": "Recipe Customization Tool", "body": "Developers can use this dataset to build a recipe customization tool for restaurants. They can simulate how different dietary restrictions and ingredient preferences affect the recipes that are suggested to customers, allowing them to create a more personalized dining experience." }, { "title": "Analytics Dashboard", "body": "Data analysts can leverage this dataset to create an analytics dashboard that visualizes trends in dietary restrictions and ingredient preferences over time. This can help restaurants understand their customers better and tailor their menus accordingly." }, { "title": "Nutritional Analysis Application", "body": "Nutritionists can utilize the dataset to develop an application that analyzes the nutritional content of personalized meal plans. By understanding the common ingredients and recipes requested by customers, they can provide valuable feedback on health and wellness." } ] }, "counts": { "culinary_customization": 500 }, "stats": { "culinary_customization": { "rows": 500, "columns": { "id": { "nulls": 0, "distinct": 500, "min": 1, "max": 500 }, "customer_id": { "nulls": 0, "distinct": 388, "min": 3, "max": 999 }, "recipe_name": { "nulls": 0, "distinct": 500 }, "dietary_restriction": { "nulls": 0, "distinct": 5, "min": "gluten-free", "max": "vegetarian" }, "ingredient_preferences": { "nulls": 0, "distinct": 499 }, "target_goal": { "nulls": 0, "distinct": 4, "min": "general health", "max": "weight loss" }, "creation_date": { "nulls": 52, "distinct": 448, "min": "2020-01-01 10:55:26", "max": "2023-10-01 19:59:11" } } } }, "artifacts": [ { "file": "csv/culinary_customization.csv", "format": "csv", "table": "culinary_customization", "key": "datasets/culinary-customization-dataset/v1/csv/culinary_customization.csv", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/csv/culinary_customization.csv", "bytes": 138851, "sha256": "ec3a700c179cc5b5b94072bcf6418f678eabaef7b097f327d9b3c42050b97db3", "rows": 500, "contentType": "text/csv; charset=utf-8" }, { "file": "culinary-customization-dataset.json", "format": "json", "table": null, "key": "datasets/culinary-customization-dataset/v1/culinary-customization-dataset.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/culinary-customization-dataset.json", "bytes": 232193, "sha256": "475555118c61724369aaf263f5b5daeaf82cd996b759b481e6fb450cbbe5d596", "rows": null, "contentType": "application/json; charset=utf-8" }, { "file": "sql/postgres.sql", "format": "sql-postgres", "table": null, "key": "datasets/culinary-customization-dataset/v1/sql/postgres.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/sql/postgres.sql", "bytes": 150916, "sha256": "874d76095a7108a59d98129a6b251719ad47100bc770f5493a17abd4da8b7c31", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mysql.sql", "format": "sql-mysql", "table": null, "key": "datasets/culinary-customization-dataset/v1/sql/mysql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/sql/mysql.sql", "bytes": 150784, "sha256": "885266f5bc187b7d8cc5cd9cdac87fcce0af3a72cc907abfe5738fe0b326957b", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/mssql.sql", "format": "sql-mssql", "table": null, "key": "datasets/culinary-customization-dataset/v1/sql/mssql.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/sql/mssql.sql", "bytes": 153334, "sha256": "927eb73a5f6ffd3ca6a0930d2d3c664019b1e96c9fa2fffd349f375de61aeb3d", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "sql/sqlite.sql", "format": "sql-sqlite", "table": null, "key": "datasets/culinary-customization-dataset/v1/sql/sqlite.sql", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/sql/sqlite.sql", "bytes": 150745, "sha256": "7595b404b08121103d48fd08f3459e133e4be9194aac1dc23effc77cff2c9582", "rows": null, "contentType": "application/sql; charset=utf-8" }, { "file": "prisma/seed.ts", "format": "prisma-seed", "table": null, "key": "datasets/culinary-customization-dataset/v1/prisma/seed.ts", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/prisma/seed.ts", "bytes": 211864, "sha256": "985ab450eddb4fcfd07d02bad95b52fdf272ba30aa70e430274c26020ecb21f0", "rows": null, "contentType": "application/typescript; charset=utf-8" }, { "file": "django/fixtures.json", "format": "django-fixture", "table": null, "key": "datasets/culinary-customization-dataset/v1/django/fixtures.json", "url": "https://database-sample-website.s3.eu-north-1.amazonaws.com/datasets/culinary-customization-dataset/v1/django/fixtures.json", "bytes": 261607, "sha256": "60510b996bec2d047d9050cd5c09370df95ae9173f8d9e3f53d5c75b539009b8", "rows": null, "contentType": "application/json; charset=utf-8" } ], "publishedAt": 1788767381794, "updatedAt": 1788767381794 }