Let's say you want to count the number of lines in each file for a list of samples, and report a table of counts for each sample. Define a rule to process each input file, and a rule to collect the results.

    with open('data/samples.txt') as slist:
        SAMPLES = [l.strip() for l in slist.readlines()]
    
    rule all:
        input:
            "results/line_counts.txt"
    
    rule count_lines:
        input:
            "data/lines/{sample}.txt"
        output:
            "processed/count_lines/{sample}.txt"
        shell:
            """
            wc -l {input} | paste <(echo -e {wildcards.sample}) - > {output}
            """
    
    rule collect_counts:
        input:
            expand("processed/count_lines/{sample}.txt", sample=SAMPLES)
        output:
            "results/line_counts.txt"
        shell:
            """
            cat <(echo -e "sample\tn_lines") {input} > {output}
            """

When you are in Next Generation Sequencing data analysis, it is basically THE tool. Snakemake comes with anything I ever needed, since it uses conda to install workflow dependencies, tools like BWA, fastqc, multiqc etc, are all present and fresh. I feel that Snakemake was made for NGS data analysis.

Many Snakemake users, like me, are biologists that know some Python and bash, I have never used Make for example. The people pointing out weaknesses here have complex, CS related use cases it seems, for bio-informaticians in the NGS field it's a godsend.