# Creating a ruleset with data quality rules
<a name="profile.data-quality-rules-create"></a>

In the following procedure, you can find an example of creating a ruleset and applying it to a dataset. A *ruleset* is a set of rules that compare different data metrics against expected values. You then can use this ruleset in a profile job to validate the data quality rules that it includes.

**To create an example ruleset with data quality rules**

1. Sign in to the AWS Management Console and open the DataBrew console at [https://console.aws.amazon.com/databrew/](https://console.aws.amazon.com/glue/).

1. Choose **DQ RULES** from the navigation pane, and then choose **Create data quality ruleset**.

1. Enter a name for your ruleset. Optionally, enter a description for your ruleset.

1. Under **Associated dataset**, choose a dataset to associate with the ruleset.

   After you select a dataset, you can view the **Dataset preview** pane at right. 

1. Use the preview in the **Dataset preview** pane to explore the values and schema for the dataset as you determine the data quality rules to create. The preview can give you insight about potential issues that you might have with the data.

   Some data sources, such as databases, don't support data preview. In that case, you can run a profile job without validating the data quality rules first. Then you can get information about the data schema and values distribution by using the data profile. 

1. Check the **Recommendations** tab, which lists some rule suggestions that you can use when creating your ruleset. You can select all, some, or none of the recommendations. 

   After selecting relevant recommendations, choose **Add to ruleset**.

   This will add rules to your ruleset. Inspect and modify parameters if needed. Note that only columns of simple types such as *string*, *numbers* and *boolean* can be used in data quality rules.

1. Choose **Add another rule** to add a rule not covered by recommendations. You can change rule names to make it easier to interpret validation results later.

1. Use **Data quality check scope** to choose whether individual columns will be selected per each check in this rule or whether they should be applied to a group of columns you select. For example, if your dataset has several numeric columns that should have values between 0 and 100, you can define the rule once and select all these columns to be checked by this rule.

1. If your rule will have more than one check, then in the **Rule success criteria** dropdown, choose whether all checks should be met or which ones meet the criteria.

1. Select a check that will be performed to verify this rule in the **Data quality check** dropdown. For more information about available checks, see [Available checks](profile.data-quality-available-checks.md). 

1. If you chose **Individual check for each column** in the **Data quality check scope**, choose a column. Select or type the column name for this check.

1. Select parameters depending on the check. Some conditions accept only provided custom values and some also support reference to another column.

1. If you choose checks for **Column values** such as *Contains* condition for string values, then you can specify “passing” threshold. For example, if you want at least 95 percent of values to satisfy the condition, you need to choose *Greater than equals* as a threshold’s **Condition**, enter 95 as a **Threshold** and leave *"%(percent) rows"* in the next dropdown in the **Threshold** section. Or if you want no more than 10 rows where *value is missing* condition is true, then you can select *Less than equals* as a **Condition**, enter 10 for **Threshold** and choose **rows** in the next dropdown. Please note that you might get different results if you're using samples of different size during validation.

1. Add more rules if needed.

1. Choose **Create ruleset**.