Best practices for writing efficient DBT code

As data engineers, we know that having clean and well-structured data is critical for successful data analysis. DBT is an amazing tool that makes this possible for us. But what if we need to run DBT code on massive datasets? What if our queries take forever to run? Here's where writing efficient DBT code comes in. In this blog post, we’re going to dive deep into the best practices for writing efficient DBT code.

Why is writing efficient DBT code important?

Let's start with the basics: writing efficient DBT code is important because it saves time, money, and effort. Imagine having a massive data warehouse with petabytes of data. When you run a model that takes 10 hours to complete, you're not only wasting precious time, but you're also wasting money on cloud computing resources. Not to mention the frustration and decreased productivity that comes from waiting for a model to finish.

Writing efficient code means that your models will run faster, more smoothly, and with fewer errors. This is important not only for productivity but for the quality of your data warehouse.

Best practices for writing efficient DBT code

  1. Use the --defer flag to only run the models that have changed.

When you run DBT, it recompiles all models, even those that haven't changed since the last time you ran them. This wastes time and resources. To avoid this issue, you can use the --defer flag, which tells DBT to only run the models that have changed since the last run. This can make a huge difference in the time it takes for DBT to complete.

  1. Break up complex models into smaller ones

In some cases, you may have a complex model that joins multiple tables, applies complex transformations, and is difficult to understand. If this is the case, consider breaking up the model into smaller ones. This makes it easier to understand, write and test.

By breaking up models, you can also avoid the need to re-run an entire model if one small piece of it changes. Instead, you can simply run the smaller model that has changed.

  1. Use DBT's dependency graph to optimize run order

DBT automatically builds a dependency graph for all of your models. This means it knows which models depend on which other models. By optimizing the order in which models are run, you can significantly reduce the time it takes for DBT to complete.

If you have a group of models that don't depend on other models, run them first. This allows DBT to get through them quickly, without waiting for dependencies to resolve. Conversely, if you have a group of models that depend on other models, run those last.

  1. Use --threads to run models in parallel

In some cases, you may be able to run models in parallel. This means that DBT can run multiple models at the same time, without waiting for the first one to finish.

To do this, use the --threads flag. This tells DBT how many threads to use when running models. The optimal number of threads will depend on your hardware and the size of your data warehouse.

  1. Use SQL best practices

Finally, don't forget about SQL best practices when writing DBT code. Here are a few quick tips:


Writing efficient DBT code is essential for successful data analysis. By following these best practices, you can optimize the performance of your DBT models, reduce the time it takes for them to run, and ultimately save time and resources.

So, go ahead and implement these tips into your DBT code today! Your data warehouse will thank you.

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