Reading uncommented code is akin to maneuvering around a dense forest. Hence why most languages have the ability to leave annotations and comments. The goal is to highlight crucial points in the program and record comments that specifically draw links to essential features. This can help with systematic preparation for other programmers making changes to the program or users trying to understand the code more. Annotating any text can also assist you in composing a well-written answer to those who come to you for questions about your code.
Introduction
Goal: tackle the 5 W’s: Who what when where why
What are annotations (what)
What are comments
Why are they used (why)
Brief history on annotations & comments detailing who gave the sensation a name, where and when they were first used
Body 1
Goal: Now that we know what Annotations & Comments are and the history behind them, now we go into the actual strategies
General Spark annotation & Commenting strategies
General Hive annotation & strategies
Interesting Big data tech strategies on commenting and annotations (Moving the second section)
Body 2
Goal: Answer the questions I posed in the proposal, but gear them towards big data tech
Is there a universal annotating/commenting rubric for SPARK/HIVE or is it on an employer by employer basis
Are there some unspoken rules that data scientists follow when annotating/commenting
Are there differences based on region?
What is the consensus on when & where to annotate/comment in the world of data science?
What all information should your comment contain