Don’t Get Tripped Up! 10 Common Data Engineering Pitfalls

How to Avoid Common Data Engineering Pitfalls

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You might be working on a new analytics platform, trying to figure out how users behave, or just learning more about data pipelines. That is fantastic! Decision-making and insights are made possible in large part by data engineering. However, data engineering has its own distinct set of hidden pitfalls that can turn a promising project into an overnight debugging session, just like any other specialized field.

Knowing these typical errors can save you (and your team) a great deal of suffering, regardless of whether you are a full-time data engineer, a software engineer experimenting with data, or a product manager attempting to comprehend the technical landscape. Let us examine ten common pitfalls that I have observed.

1. The “Current Date” Deception

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Using current_date or now() in your data processing seems okay, right? It just grabs the current timestamp. What could go wrong?

• The Pitfall: When you use current_date timestamp indicates when the data was processed, not when the event…