A dataset with car models categorized by manufacturer, type (SUV, Sedan, etc), and manufacturing year. A developer is able to clone, connect, and download the dataset in a JSON format (transform to CSV or Excel after downloading). The dataset is open-source and provides detailed information about motor vehicles manufactured in the US between the years 1992 and 2022.
This is a comprehensive database with all cities in India. The list includes around 4,000 cities, towns, villages, it's free to download, and consume via APIs. Developers can retrieve data using their preferred programming language, for example, Javascript, Swift, Dart, NodeJS, PHP, Phyton, Swift, Java, etc. Data gathered from Wikipedia and Geonames and licensed under Creative Commons 4.0.
This database contains a single dataset (class) called Color with 957 colors, their names, and their RGB codes.
This public database contains datasets with all NAICS (North American Industry Classification System) sectors, subsectors, groups, industries, national industries, and activities. All the data can be fetched via GraphQL or REST API.
This public database contains datasets with all SOC (Standard Occupational Classification System) and STEM (Science, Technology, Engineering, and Math) occupations in addition to all job titles according to BLS (Bureau of Labor Statistics of the USA). All the data can be accessed via GraphQL or REST APIs.
SWAPI is the Star Wars API built on top of Back4App. This dataset can be consumed either via REST or GraphQL API.
This is a dataset with US states that is accessible via GraphqQL API.
Database with all postal codes of Correios (Brazilian Post Office). The database covers all addresses, streets, neighborhoods, towns, villages, cities, and states of Brazil. All data is available to download or consume via APIs (GraphQL or REST).
Complete list of cities of Indonesia containing around 400 populated locations including cities, towns, and villages. Specially designed for developers and all data is available to fetch via APIs or edit cloning the database. Datasources are Geonames and Wikipedia.
This is the most comprehensive database with the Zip Codes from Portugal. It's created for developers and all data can be retrieved via REST and GraphQL APIs. Ready to use code over Swift, NodeJS, Javascript, Python, etc is available and fully integrated with the dataset.