<ul><li>There are common mistakes in data engineering and ML apps that should be avoided.</li><li>One mistake is overestimating the size of data. With modern hardware, 100GB is not considered a massive amount of data.</li><li>The 'Big Data' label is more applicable for petabytes of data or when data's velocity, variety, or veracity pose challenges.</li><li>Simpler and faster approaches, like using Python pandas on a laptop, can outperform complex and time-consuming Spark clusters for smaller datasets.</li></ul>

8 Common Mistakes in Data Engineering and ML Apps (and How to Avoid Them)

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