Typo fixed - small adjustment

website/docs/terms/data-wrangling.md

@@ -51,7 +51,7 @@ The cleaning stage involves using different functions so that the values in your
 - Removing appropriate duplicates or nulls you found in the discovery process
 - Eliminating unnecessary characters or spaces from values
 
-Certain cleaning steps, like removing rows with null values, are helpful to do at the beginning of the process because removing nulls and duplicates from the start can increase the performance of your downstream models.  In the cleaning step, it’s important to follow a standard for your transformations here. This means you should be following a consistent naming convention for your columns (especially for your <Term id="primary-key">primary keys</Term>) and casting to the same timezone and datatypes throughout your models. Examples include making sure all dates are in UTC time rather than source timezone-specific, all strings in either lower or upper case, etc. 
+Certain cleaning steps, like removing rows with null values, are helpful to do at the beginning of the process because removing nulls and duplicates from the start can increase the performance of your downstream models.  In the cleaning step, it’s important to follow a standard for your transformations here. This means you should be following a consistent naming convention for your columns (especially for your <Term id="primary-key">primary keys</Term>) and casting to the same timezone and datatypes throughout your models. Examples include making sure all dates are in UTC time rather than source timezone-specific, all strings are in either lower or upper case, etc. 
 
 :::tip dbt to the rescue!
 If you're struggling to do all the cleaning on your own, remember that dbt packages ([dbt expectations](https://github.com/calogica/dbt-expectations), [dbt_utils](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/), and [re_data](https://www.getre.io/)) and their macros are also available to help you clean up your data.