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.
Redis was spawned during the No-SQL craze, and serves as a super fast, in-memory database, but also has persistence. Redis doesn’t use tables, but has collection types like Set, List, and Hash