> For the complete documentation index, see [llms.txt](https://docs.gaiodataos.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gaiodataos.com/tools/tasks/etl/sql.md).

# SQL

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The **SQL Editor** in Gaio DataOS allows users to write, run, and test custom SQL queries directly on tables within the selected project bucket. It's a powerful tool for advanced users who prefer full control over data manipulation using SQL.

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## &#x20;How to Use the SQL Editor

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### 1. **Access the SQL Editor**

* In the **Studio**, open the left-side panel under **Tasks**.
* Select the **SQL** option to open the code editor.

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### 2. **Select the Bucket**

* At the top left, select the **bucket** that contains the tables you want to query.
* All available tables in that bucket will be listed below for reference.

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### 3. **Write Your SQL Query**

* In the central editor area, type your SQL query.\
  Example:

  ```sql
  SELECT * FROM sales;
  ```
* The editor supports syntax highlighting for better readability and clarity.

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### 4. **Run the Query**

* Click **Run query** to execute your SQL.
* The result will appear in a preview panel below the code.

You can paginate the result using the row limit control on the top right (`1,000` by default).

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### 5. **Save Your Query**

* Click **Save** to store the SQL block as a reusable task or part of a data flow.

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#### Additional Features

| Feature                  | Description                                              |
| ------------------------ | -------------------------------------------------------- |
| **Syntax Highlighting**  | Enhanced readability with colored keywords.              |
| **Result Table Preview** | View output immediately after running your query.        |
| **Multiple SQL Blocks**  | Add more SQL blocks using the **+** button.              |
| **Run Flow Integration** | SQL blocks can be integrated into larger data processes. |

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#### Best Practices

* Use aliases for column names when working with JOINs.
* Avoid `SELECT *` in production queries — explicitly define your columns.
* Test queries with limited row counts before running on full datasets.

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