# IsComplete
<a name="dqdl-rule-types-IsComplete"></a>

Checks whether all of the values in a column are complete (non-null). 

**Syntax**

```
IsComplete <COL_NAME>
```
+ **COL\$1NAME** – The name of the column that you want to evaluate the data quality rule against.

  **Supported column types**: Any column type

**Example: Null values**

The following example checks whether all of the values in a column named `email` are non-null.

```
IsComplete "email"
IsComplete "Email" where "Customer_ID between 1 and 50"
IsComplete "Customer_ID"  where "Customer_ID < 16 and Customer_ID != 12"
IsComplete "passenger_count" where "payment_type<>0"
```

**Null behavior**

 Note on CSV Data Formats: Blank rows on CSV columns can display multiple behaviors. 
+  If a column is of `String` type, the blank row will be recognized as an empty string and will not fail the `Completeness` rule. 
+  If a column is of another data type like `Int`, the blank row will be recognized as `NULL` and will fail the `Completeness` rule.