Golang For Big Data Analytics

by Reemi Shirsath Chief Operating Officer

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 Big Data Analytics is a great idea.

Data Collection

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.

Arithmetical Solutions

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.