Understanding Dataframe and Spark
Before building a dataframe, let’s take a brief introduction about it. A dataframe is a data structure in the Spark Language. Spark is used to develop distributed products i.e. a code that can be run on many machines at the same time. The main purpose of such products is to process large data for business analysis. The dataframe is a tabular structure that can store structured and semi-structured data. For unstructured data, we need to modify it to fit in the dataframe. Dataframes are built on the core API of Spark called RDDs to provide type-safety, optimization, and other things.
How to print dataframe in Scala?
Scala stands for scalable language. It was developed in 2003 by Martin Odersky. It is an object-oriented language that provides support for functional programming approach as well. Everything in scala is an object e.g. – values like 1,2 can invoke functions like toString(). Scala is a statically typed language although unlike other statically typed languages like C, C++, or Java, it doesn’t require type information while writing the code. The type verification is done at the compile time. Static typing allows to building of safe systems by default. Smart built-in checks and actionable error messages, combined with thread-safe data structures and collections, prevent many tricky bugs before the program first runs.