Hey there! If you’re new to dbt (like I was not too long ago), you’re probably excited to dive in and start transforming your data. But before you do, take a minute to learn from those who’ve gone before you.
As a data engineer, I’ve worked with dbt and learned the hard way about some common pitfalls to avoid. So, let me share some rookie mistakes to watch out for:
## Misunderstanding the dbt Philosophy
One of the biggest mistakes is not fully grasping the dbt way of thinking. dbt is all about treating your data as code and versioning it. So, make sure you understand the concepts of snapshots, models, and transformations before you start building.
## Over-Engineering
It’s easy to get carried away with complex logic and over-engineer your dbt models. Keep it simple, and remember that dbt is meant to be a lightweight, flexible tool.
## Not Testing Thoroughly
Don’t assume that your dbt models will work flawlessly without testing. Take the time to write comprehensive tests to catch any errors or inconsistencies.
## Ignoring dbt Best Practices
dbt has established best practices for a reason. Follow them to ensure your code is maintainable, scalable, and easy to understand.
## Not Monitoring Performance
Keep an eye on your dbt pipeline’s performance. If you’re not monitoring it, you might not catch issues until it’s too late.
## Underestimating the Power of Macros
Macros can be incredibly powerful in dbt. Don’t underestimate their potential to simplify your code and make it more efficient.
By being aware of these common pitfalls, you can avoid some of the headaches that come with learning dbt. Happy transforming!
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*Further reading: [dbt Documentation](https://docs.getdbt.com/docs/introduction)*