doc: refine getting started start command for script tasks

content/docs/01.getting-started.md

@@ -12,10 +12,10 @@ Start Kestra using Docker-Compose and create your first flow.
 
 ## Start Kestra
 
-Make sure that Docker is running. Then, you can start Kestra in a single command using Docker.
+Make sure that Docker is running. Then, you can start Kestra in a single command using Docker (*if you run it on Windows, make sure to use [WSL](https://docs.docker.com/desktop/wsl/)*):
 
 ```bash
-docker run --rm -it -p 8080:8080 kestra/kestra:develop-full server local
+docker run --pull=always --rm -it -p 8080:8080 --user=root -v /var/run/docker.sock:/var/run/docker.sock -v /tmp:/tmp kestra/kestra:develop-full server local
 ```
 
 Open [http://localhost:8080](http://localhost:8080) in your browser to launch the UI and start building your first flows.

content/docs/02.tutorial/03.outputs.md

@@ -116,6 +116,49 @@ This example flow passed data between tasks using outputs. The `inputFiles` argu
 
 To sum up, our flow extracts data from an API, uses that data in a Python script, executes a SQL query and generates a downloadable artifact.
 
+::alert{type="info"}
+If you encounter any issues while executing the above flow, this might be a Docker-related issue (e.g. Docker-in-Docker setup, which might be difficult to configure on Windows). Set the runner property to `PROCESS` to run the Python script task in the same process as the flow rather than in a Docker container, as shown in the example below. This will avoid any Docker-related issues.
+::
+
+```yaml
+id: getting_started
+namespace: dev
+
+inputs:
+  - name: api_url
+    type: STRING
+    defaults: https://dummyjson.com/products
+
+tasks:
+  - id: api
+    type: io.kestra.plugin.fs.http.Request
+    uri: "{{ inputs.api_url }}"
+
+  - id: python
+    type: io.kestra.plugin.scripts.python.Script
+    runner: PROCESS
+    beforeCommands:
+      - pip install polars
+    warningOnStdErr: false
+    script: |
+      import polars as pl
+      data = {{outputs.api.body | jq('.products') | first}}
+      df = pl.from_dicts(data)
+      df.glimpse()
+      df.select(["brand", "price"]).write_csv("{{outputDir}}/products.csv")
+
+  - id: sqlQuery
+    type: io.kestra.plugin.jdbc.duckdb.Query
+    inputFiles:
+      in.csv: "{{ outputs.python.outputFiles['products.csv'] }}"
+    sql: |
+      SELECT brand, round(avg(price), 2) as avg_price
+      FROM read_csv_auto('{{workingDir}}/in.csv', header=True)
+      GROUP BY brand
+      ORDER BY avg_price DESC;
+    store: true
+```
+
 
 ::next-link
 [Next, let's cover `triggers` to schedule the flow.](./04.triggers.md)