Troubleshooting Common DBT Errors and Issues

Are you tired of running into the same problems when running DBT code? Do you want to improve your experience with DBT by learning how to tackle some common errors and issues?

You're in luck! In this article, we'll explore some common errors and issues you might encounter when using DBT and offer suggestions for how to fix them.


DBT is a top choice for many data professionals looking to handle data transformations and analysis. Its open-source software, Python-based, and expert-led community make it an attractive platform to explore data transformations.

However, as with any software or platform, DBT can sometimes lead to issues with its code or implementation. This can lead to significant delays, sometimes resulting in data loss or incorrect analysis.

The following are some typical problems you may experience when working with DBT:

In this article, we'll take a closer look at these common problems, as well as some tips and tricks you can use to fix them.

Error messages in DBT

Error messages are perhaps the most common issue encountered by data analysts when using DBT. They can stem from many potential areas, including syntax, data formatting, and data flow processes. Some of the most common error messages include:

To fix these issues, you'll need to first deviate from typical SQL issue fixes, and look closer at DBT's syntax, including Jinja macros.

Troubleshooting Parsing Errors

Parsing errors occur when DBT can't parse the code. It could be a syntax error or an error in a macro. Here’s how to fix them:

  1. Check the code for missing commas, incorrect spelling, or capitalization issues. These could be the reason for the error message.
  2. Consider the use of Jinja inside the templates. If you're using if statements or loops, make sure they are correctly formatted.

Fixing Runtime Errors

When running DBT code, sometimes you will encounter runtime errors. These usually have to do with the SQL code being used. Here's how to fix them:

  1. Retrace the steps that led to the error. Look for issues like using invalid-length strings or incorrect syntax.
  2. Always remember the SQL documentation or language specification. It's critical to ensure you are following the correct syntax or using a valid function.

Rectifying Undefined Objects

Undefined objects occur when DBT can't find a table, model, or view that's being referenced in a specific application. Here's how to fix them:

  1. Double check the spelling of the reference, and ensure they're referring to a pre-existing table or model.
  2. Remember to run DBT's command to refresh the dependencies in the target database and directory.

Issues with data pipeline orchestration

Data pipeline orchestration involves aligning data pipelines, ensuring they're complete, and that the relevant data is available for analysis. When working with DBT, data pipeline orchestration may encounter issues, such as:

These issues typically stem from a lack of data documentation before production and primary focus on data processing.

The following tips can help data professionals fix these data pipeline issues:

Problems with data pipeline testing

Testing is an essential component of any data process or analytic project. DBT provides many testing tools, but often, data professionals may encounter a few testing issues, including:

To fix these testing issues, you can use the following suggestions:

Difficulties with code deployment

Code deployment can be challenging, regardless of coding expertise, and it's not unusual for data professionals to experience issues when deploying DBT code. Some common errors that occur during deployment are as follows:

These errors can lead to incorrect data analysis or the corruption of batches of data.

To fix these issues, consider the following tips:

Reporting and visualization issues

The final stage of any DBT data transformation project involves data analytics, reporting, and visualization. Reporting and visualization issues most commonly entail:

These issues can be addressed by considering the following tips:


By using the tips outlined above, data analysts and developers can minimize the issues encountered when working with DBT, making it simpler to transform and analyze data. If you take time to address the errors and solutions outlined in this article, you should see an immediate improvement in the smoothness of data transformations and analysis. Put these tips to work and enjoy fewer error messages and more efficient data analysis pipelines.

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