The article focuses on manipulating and transforming columns in a Spark DataFrame using Scala, essential for data preparation in engineering. It defines a DataFrame as a distributed collection organized like a relational table, featuring high-level APIs, optimized execution, lazy evaluation, and compatibility with multiple languages. The exercise aims to teach critical skills like adding, renaming, dropping columns, data type casting, conditional logic, and formatting. Mastering these operations forms the core of effective ETL development in Spark.
A Spark DataFrame is a distributed collection of name-organized data, offering high-level APIs for analysis, optimized execution, and compatibility with various programming languages.
This exercise covers essential DataFrame operations in Scala, including adding columns, renaming, dropping, casting types, performing conditional logic, and formatting date/numeric fields.
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