Data Querying Framework for Developers.
The UI Engine for the Big Data is the Browser. While the backend can store and process terabytes of data, the front-end browser is capable of holding not more than 30 MB of data. Complex programs are not possible in the browser because a lot of the utilities that back-end programmers take for granted e.g. complex data structures, SQL, etc. are not available to front-end programmers. Hence, we built pykQuery as a means to improve the available tool-set to our visualisation developers.
— SQL Like Syntax on Columnar Data
— Select (metric functions, custom functions)
— Library Functions (math, datetime)
— Where (and/or conditions, nested filters, etc.)
— Group By (aggregation)
— Joins (full, inner, left, right)
— Order (ascending, descending)
— Interactivity Impacts (data, filter)
— Save Queries as JSON
— pykquery-postgresql npm package
— pykquery-mysql npm package
— Interactive Visualizations
— Exploratory Dashboards
— Data-driven Presentations
Here are some of the Pykih visualizations powered by PykQuery.
CPG Sales Distribution Dashboard
Real-time Survey Responses
Portfolio Review PPT
The Skier's Almanac
Responsible Business Index
Why Should I Hire Pykih? (PDF) to learn why we’re the smart choice.
Portfolio of work done - for Content Marketing (PDF) to see some of our past work.
Portfolio of work done - for Decision Making (PDF) to see some of our past work.
Get in touch and we’ll deliver a proposal in a week or less.