Made For Developers With


Your customers like your product, but they are demanding something more: analytics. With Popily, you can add advanced customer-facing analytics with only a few lines of code. In this tour we will see how to do just that, with real data, and real code.

Order ID Order Date Sales Profit Customer Name Customer Segment Product Category State Location
3 10/13/10 261.5400 -213.25 Muhammed MacIntyre Small Business Office Supplies Alabama (-6.0816889999999999, 145.39188100000001)
293 10/1/12 10123.0200 457.81 Barry French Consumer Office Supplies Alaska (-5.2070829999999999, 145.78870000000001)
... ... ... ... ... ... ... ... ...
56550 4/8/11 823.7800 343.05 Frank Hawley Home Office Furniture Maryland (-27.466666700000001, -153.0166667)
56550 4/8/11 469.8375 -159.24 Frank Hawley Home Office Technology Massachusetts (-27.466666700000001, 153.0166667)
56581 2/8/09 2026.0100 580.43 Grant Donatelli Consumer Furniture Michigan (-23.378941000000001, 150.51232300000001)

Above is a few rows from a sales database that might be relevant to your users. The data includes dates, names, prices, categories, and locations. With traditional analytics products, turning this data into customer-facing analytics would require substantial data restructuring and modeling. With Popily, all that is taken care of for you in a few lines of code.

var sourceOptions = {
    connection_string: 'mysql://username:password@host:port/database',
    query: 'SELECT * FROM my_table'

popily.api.addSource(sourceOptions, function(err, source) {});

Notice that Popily does not require the user to specify any complicated data models or data type schemas. Instead, Popily's intelligent analytics engine constructs the appropriate data model automatically for the user's data.

Simple right? Now let's get started creating some analytics.