Data Analytics applications and Golang are surprisingly two not very commonly associated terms.We call it surprising because most developers and Data Scientists prefer to use R or even Python .On the contrary, Big Data Applications can, in fact, be developed with great ease and efficiency using Golang.
Now the thing with Golang is that because it is a new language most people don't trust it to have the adequate tools and library resources to develop Data Analytics Applications. Thus, to bust this myth we have put together some of the most compelling reasons why Golang for Data Analytics is a great idea.
The first objective of a Big Data application is to collect and organize Data successfully. Golang is excellent at data gathering and organization. There are many databases and datastores written in Go, such as InfluxDB, Cayley, LedisDB and many more.It also has some libraries commonly used datastores such as Mongo, Postgres, etc. Even regarding parsing and cleaning data, Golang has proven itself to be more competent than many other languages. GJSON enables quick parsing of JSON values while ffjson is great for fast JSON serialization. Gota creates robust data frames while scrape is excellent for web scraping.
Post Data Storing, Organising and Parsing we now move onto handling complex statistical and arithmetical problems. A set of numeric libraries of Golang known as the Gonum organization power the language with numerical functionality.By virtue of its libraries for matrices, statistics and optimization Golang can better handle complex computations. Gophers are in fact producing some great arithmetic, data analysis, and statistics projects. This includes math a stdlib math functionality, gonum/matrix for matrices and matrix operations, gonum/floats for various helper functions for dealing with slices of floats among many others.
Golang is great for exploratory data analysis. Since it is extremely compatible with Web development, web apps and visualizations via custom APIs can be used to provide in-depth Visual analysis of results.Gophernotes, dashing-go, and gonum/plot each bring about Go kernel for Jupyter notebooks, dashboarding and plotting, respectively
Contrary to popular belief Golang facilitates data scientists to do some machine learning too. Sajari/regression enables multivariable regression while goml, golearn, and hector facilitates general purpose machine learning. Bayesian brings about Bayesian classification while neurgo brings about neural networks. These are only just some examples of Go libraries that enhance Machine Learning.Applications can be integrated into several machine learning frameworks and APIs (such as H2O or IBM Watson) to enable a whole host of machine learning functionality. A Go API for Tensorflow is also being planned.
To summarise it all, Golang for Data Analytics is a fast catching idea. We at Qwentic have developed several Big Data Analytics solutions for our clients in the advertisement industry, Manufacturing Industry, and Logistics Industry. To know more about these solutions, drop us a message, and we would be glad to help.
Qwentic is a leading technology consulting company, engaged in offering end to end consulting services. We are technology consulting partners to several leading businesses across a diverse range of industries spanning Logistics, Healthcare, Advertisement, and E-learning