# Create rulesets
To create a ruleset, first navigate to the **Data quality** tab for a catalog table, then define your rules using DQDL syntax.
## Navigate to the Data quality tab
1. In the left navigation pane, choose **Data**.
1. Under the **Catalogs** tab, expand a catalog (for example, AwsDataCatalog), then expand the database (for example, sagemaker\_sample\_db).
1. Select the table you want to evaluate (for example, churn).
1. On the table detail page, choose the **Data quality** tab.
## Create a ruleset
When you first open the **Data quality** tab on a table, it shows an empty state with the option to create your first ruleset.
1. Choose **Create**.
1. The **Create ruleset** page opens with the following fields: **Ruleset name**, **Description** (Optional), and the DQDL rules editor.
1. Enter a name for your ruleset. The name identifies this set of rules in run results, the dashboard, and scheduled evaluations. Choose a descriptive name that reflects what the rules check (for example, `completeness-checks` or `value-range-validation`).
1. In the DQDL rules section, use the code editor to define your rules. A **Rule types** reference panel lists all 31 available rule types. You can search rules by name and filter by category. Expand any rule type to see its description and syntax.
1. After defining your rules, choose **Create ruleset**.
## Write rules using DQDL
Define rules in the code editor using DQDL syntax. The editor provides autocomplete for rule types and column names from the table schema.
**Tip**
Use Ctrl\+Tab (or Cmd\+Tab on Mac) to trigger column name suggestions.
The following example shows a ruleset with two rules.
```
Rules = [
RowCount > 1000,
Completeness "account_length" between 0.1 and 0.25
]
```
Common rule types include:
| Rule type | What it checks |
| --- | --- |
| RowCount | Total row count meets a threshold. |
| Completeness | Percentage of non-null values in a column. |
| ColumnExists | A specific column is present in the table. |
| ColumnLength | String column values meet a length condition. |
| IsUnique | All values in a column are unique. |
| IsComplete | A column has no null values. |
| ColumnValues | Column values satisfy a condition (for example, greater than 0, in a set). |
| CustomSql | A custom SQL expression evaluates to true. |
You can also use referential integrity rules to validate relationships between columns in different tables. For details on syntax and examples, see [ReferentialIntegrity](https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types-ReferentialIntegrity.html) in the AWS Glue documentation.
**Note**
Referential integrity and other cross-table rule types are supported only for tables under the default AWS Glue Data Catalog, using the format `.`.
For the complete reference of all 31 rule types, expressions, composite rules, and syntax, see [DQDL rule types](https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types.html) in the AWS Glue documentation.
## Migrate existing rulesets to your project
If you have existing rulesets in AWS Glue that you want to view and manage within your Amazon SageMaker Unified Studio project, tag them so the project can discover them.
1. Add the following tag to your existing ruleset:
+ Key: `AmazonDataZoneProject`
+ Value: Your project ID (found in the project settings page in Amazon SageMaker Unified Studio)
1. Ensure your project role has adequate permissions to access the underlying data, including access to the Amazon S3 bucket where the table data is stored and appropriate Lake Formation grants for the table.
Once tagged, the ruleset appears on the **Data quality** tab for the corresponding table within your project. You can then run, schedule, and view results the same way as rulesets created directly in Amazon SageMaker Unified Studio.