Asteria is a specification for building data analytics streams that are executed through sequential data processors in a JavaScript environment.
Asteria Project is a complete set of data analytics tools built over TypeScript and Node.js. It has been designed with different purposes in mind:
- provide a structure for creating modules-based data analytics processors
- focus on extensibility and ease-of-use
- put forward flexibility for many different uses (e.g. big data, signal processing, real-time cryptography, etc.)
The Asteria Project Documentation will take you from total beginner to Asteria expert.
In this sample application, we use the Hyperion processor to load a CSV file of all cities over the world and extract only US cities that have more than 1,000,000 people.
(The raw data set used to build this sample is available under OPEN DATA LICENSE at: https://public.opendatasoft.com/explore/dataset/worldcitiespop)
Hyperion.build({
name: 'UsMegaCities',
processes: [
{
type: 'read-file',
config: 'worldcitiespop.csv'
},
{
type: 'csv-to-list',
config: { separator: ';' }
},
{
type: 'filter',
config: 'Population > 1000000 AND Country = \'us\''
}
]
}).run();Preceding code will produce the following data set:
{"Country":"us","City":"san diego","AccentCity":"San Diego","Region":"CA","Population":1287050,"Latitude":32.7152778,"Longitude":-117.1563889,"geopoint":"32.7152778, -117.1563889"}
{"Country":"us","City":"houston","AccentCity":"Houston","Region":"TX","Population":2027712,"Latitude":29.7630556,"Longitude":-95.3630556,"geopoint":"29.7630556, -95.3630556"}
{"Country":"us","City":"chicago","AccentCity":"Chicago","Region":"IL","Population":2841952,"Latitude":41.85,"Longitude":-87.65,"geopoint":"41.85, -87.65"}
{"Country":"us","City":"phoenix","AccentCity":"Phoenix","Region":"AZ","Population":1428509,"Latitude":33.4483333,"Longitude":-112.0733333,"geopoint":"33.4483333, -112.0733333"}
{"Country":"us","City":"san antonio","AccentCity":"San Antonio","Region":"TX","Population":1256810,"Latitude":29.4238889,"Longitude":-98.4933333,"geopoint":"29.4238889, -98.4933333"}
{"Country":"us","City":"philadelphia","AccentCity":"Philadelphia","Region":"PA","Population":1453268,"Latitude":39.9522222,"Longitude":-75.1641667,"geopoint":"39.9522222, -75.1641667"}
{"Country":"us","City":"new york","AccentCity":"New York","Region":"NY","Population":8107916,"Latitude":40.7141667,"Longitude":-74.0063889,"geopoint":"40.7141667, -74.0063889"}
{"Country":"us","City":"dallas","AccentCity":"Dallas","Region":"TX","Population":1211704,"Latitude":32.7833333,"Longitude":-96.8,"geopoint":"32.7833333, -96.8"}
{"Country":"us","City":"los angeles","AccentCity":"Los Angeles","Region":"CA","Population":3877129,"Latitude":34.0522222,"Longitude":-118.2427778,"geopoint":"34.0522222, -118.2427778"}