Trying to understand the big picture behind DNA sequencing, alignment and searching
Here's a quick summary of a few mis-hits in your otherwise good analysis:

Not many bioinformatics applications use Hadoop, Apache Spark or Apache Flink. In fact, I have never heard of the Apache Spark and Flink tools, and I've seen only 2 people use Hadoop to process alignment files.


Reads are not "converted" real, physical DNA. They are representations of the signals from the molecules making up the DNA, as read by sequencing machines.
A,T,C,G are molecules making up DNA. N refers to "any of A,T,C,G", which translates to either unknown or ambiguous.
Reads are stored in FASTQ files. Sequences are stored in FASTA files. Reads include sequence as well as quality information, so, FASTA+QUAL=FASTQ
Re-ordering is a crude but approximate way to think about it. Remember, this process involves overlaps while reordering seldom does. Overlaps are crucial to the process of assembly/alignment. You are correct about the alignment to reference sequence and de novo assembly parts.
This is correct, though statistical models are applied to account for differences that are not necessarily significant variants (such as for sequencing errors)
Yes, variants, which include SNPs, can be correlated to differences in phenotype (traits). Emotional tendencies is something a bit too advanced, think of something more basic like diabetes or eye color.


The picture you have here is of a regular NGS analysis pipeline. This involves alignment/assembly, variant calling and biological analysis with relevant hypotheses. The assembly/alignment is the most computationally expensive part, and we use either HPC clusters or scalable cloud services such as AWS to get this done.

You should definitely talk to a biologist that has some computational experience to gain insight into the reason behind our analyses. Once you understand the motivations, your contribution will be more relevant and helpful to the community.