Pete Warden - Böcker

If you're a developer looking to supplement your own data tools and services, this concise ebook covers the most useful sources of public data available today. You'll find useful information on APIs that offer broad coverage, tie their data to the outside world, and are either accessible online or feature downloadable bulk data. You'll also find code and helpful links. This guide organizes APIs by the subjects they cover-such as websites, people, or places-so you can quickly locate the best resources for augmenting the data you handle in your own service. Categories include: * Website tools such as WHOIS, bit.ly, and Compete * Services that use email addresses a search term, including Github * APIs for finding information from just a name, including WhitePages * Services that help you locate people with accounts, such as Klout * Search APIs, including BOSS and Wikipedia * Geographical data sources, including SimpleGeo and US Census * Company information APIs, such as CrunchBase and ZoomInfo * APIs that list IP address, such as MaxMind * Services that list books, films, music, and products

There's been a massive amount of innovation in data tools over the last few years, thanks to a few key trends: * *Learning from the web*. Techniques originally developed by website developers coping with scaling issues are increasingly being applied to other domains. * *CS+?=$$$*. Google have proven that research techniques from computer science can be effective at solving problems and creating value in many real-world situations. That's led to increased interest in cross-pollination and investment in academic research from commercial organizations. * *Cheap hardware*. Now that machines with a decent amount of processing power can be hired for just a few cents an hour, many more people can afford to do large-scale data processing. They can't afford the traditional high prices of professional data software though, so they've turned to open-source alternatives. These trends have led to a Cambrian Explosion of new tools, which means when you're planning a new data project you have a lot to choose from. This guide aims to help you make those choices by describing each tool from the perspective of a developer looking to use them in an application.Wherever possible, this will be from my first-hand experiences, or from colleagues who have used the systems in production environments. I've made a deliberate choice to include my own opinions and impressions, so you should see this guide as a starting point for exploring the tools, not the final word. I'll do my best to explain what I like about each service but your tastes and requirements may well be quite different. Since the goal is to help experienced engineers navigate the new data landscape, the guide only covers tools that have been created or risen to prominence in the last few years. For example, PostGres is not covered because it's been widely used for over a decade, but its Greenplum derivative is newer and less well-known, so it is included.