Should we build in-house or buy a custom software solution to help grow our business? This is a question we hear a lot as a geospatial consulting company on engagement calls. Most growing companies face this question. Should you build an in-house solution that sort of fits your needs, or should you pay more for something custom-fit to your business? There are pros and cons to both, but companies need to think first if a) Read more…
In blog post #2 of 3, Mosaic Software examines the strategic value of integrating weather data into GIS applications.
This is blog post #1 of a 3-part series focused on how technology c|an support GIS decision making using analytics and innovative design-thinking software development methodologies.
In post #3 of 3 Mosaic Software examines the application of GIS beyond maps by way of developing a recommender system to match up shipments and providers via profitability.
For this installment, we are going to look at how you can build Lambda functions in Java. We’ll take an existing Trajectory Modeler utility and make it available on Lambda.
In this blog post, we’ll explore serverless computing. This will be the first in a short series, exploring the technology and getting “hands-on” by developing some basic serverless functions.
This is the fourth and final post in a series on choosing a language for Big Data projects. This post presents a particular Big Data case in which Mosaic Software assisted NASA by building a tool to help identify inefficiencies in the national air space.
Julia is an open-source language built to solve the ‘two-language’ problem. This post completes the evaluation by looking at the strengths and weaknesses of Julia for use by big data project consultants.
Scala, evaluating some of the strengths and weaknesses of the programming language as it relates to big data applications.
There are numerous tasks that big data software consultants might want to perform on big data collections that might include data capture and storage, analysis, visualization, and others. When it comes to big data applications, there are three languages that have emerged (or are in the process of emerging) to tackle big data problems: Python, Scala, and Julia.