# Delete

<figure><img src="/files/er1uBOBa0JguLJChOMKw" alt=""><figcaption></figcaption></figure>

The **Delete** task in **Gaio DataOS** allows you to remove records from a table based on custom filter conditions. It is useful for cleaning outdated, inconsistent, or temporary data as part of automated ETL flows.

{% hint style="warning" %}
**Important:** This task can only be used when a table is selected in the flow. Be sure to add and connect a Table before configuring the deletion.
{% endhint %}

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## How to Use

### **1. Add and Select a Table Block**

* In your flow, add a **Table** with the dataset you want to work with.
* Select the table as input for the Delete task.

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### **2. Open the Delete Task**

* In the left-side menu, go to:\
  **Tasks > ETL > Delete**

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### **3. Fill Out Task Information**

* **Task label**: (optional) A descriptive name for the step.
* **Table**: Will be automatically filled based on the selected table (e.g., `new_sales`).

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### **4. Set Filter Conditions**

* In the **Filters** section, define one or more conditions to specify **which rows should be deleted**.
* For each filter, configure:

| Field        | Description                                                            |
| ------------ | ---------------------------------------------------------------------- |
| **Column**   | Choose the column to filter by (e.g., `order_id`)                      |
| **Type**     | Input type: `Value`, `Parameter`, etc.                                 |
| **Operator** | Comparison operator (`Equal to`, `Not equal to`, `Greater than`, etc.) |
| **Value**    | The value to compare against                                           |

**Example:**\
Delete rows from the `new_sales` table where `order_id` is equal to `1001`.

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### **5. Execute or Save**

* **Run**: Immediately performs the deletion (without saving the task).
* **Save**: Saves the task to the current flow.
* **Save and Run**: Saves and immediately executes the deletion.

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#### Use Cases

* Remove invalid or duplicate records
* Delete temporary test data
* Automate cleanup logic based on business rules
* Ensure data consistency before or after processing

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