cleaning up

Signed-off-by: Fellipe Resende <[email protected]>

Author: fresende

content/en/docs/components/spark-operator/user-guide/notebooks-spark-operator.md

@@ -19,14 +19,16 @@ The following diagram illustrates how the components work together:
      class="mt-3 mb-3 border rounded">
 </img>
 
+---
+
 ## Overview
 
 In a typical Kubeflow setup, users access JupyterLab Notebooks through the central dashboard. These notebooks can now be configured to run PySpark code remotely through kernels managed by Jupyter Enterprise Gateway (JEG).
 
 Behind the scenes:
 
 1. JEG receives execution requests from notebooks.
-2. JEG creates `SparkApplication` Custom Resources.
+2. JEG creates and submits `SparkApplication` Custom Resources.
 3. The Spark Operator handles the lifecycle of Spark driver and executor pods in Kubernetes.
 
 This architecture enables scalable, elastic execution of big data or distributed ML workloads.
@@ -36,12 +38,13 @@ This architecture enables scalable, elastic execution of big data or distributed
 - A running Kubeflow deployment with Notebook Controller enabled
 - Spark Operator installed and configured in the cluster
 - Helm installed locally
-- (Optional) Minikube for local development or testing
 
 ---
 
 ## Step 1: Deploy Enterprise Gateway
 
+This step creates a dedicated Kubernetes namespace (enterprise-gateway) and sets up a local persistent volume and claim using hostPath.
+
 Begin by creating the necessary storage resources. Save the following manifest as `enterprise-gateway-storage.yaml`:
 
 ```yaml
@@ -80,28 +83,15 @@ spec:
       storage: 1Gi
 
 ```
-Apply it:
 
-This step creates a dedicated Kubernetes namespace (enterprise-gateway) and sets up a local persistent volume and claim using hostPath.
+Apply it:
 ```yaml
 kubectl apply -f enterprise-gateway-storage.yaml
 ```
-Then deploy Enterprise Gateway using Helm:
+Now we will be deploying Jupyter Enterpise Gateway with support for remote kernel management and persistent kernelspec storage.
 
-The command below uses a YAML file named enterprise-gateway-helm.yaml, which includes an example configuration shown below.
+Save the following manifest as `enterprise-gateway-helm.yaml` which will be used as the basic configuration for the gateway.
 
-```yaml
-helm upgrade --install enterprise-gateway \
-  https://github.com/jupyter-server/enterprise_gateway/releases/download/v3.2.3/jupyter_enterprise_gateway_helm-3.2.3.tar.gz \
-  --namespace enterprise-gateway \
-  --values enterprise-gateway-helm.yaml \
-  --create-namespace \
-  --wait
-
-```
-Example configuration yaml:
-
- Save the following manifest as `enterprise-gateway-helm.yaml`.
 ```yaml
 image: elyra/enterprise-gateway:3.2.3
 imagePullPolicy: Always
@@ -128,7 +118,20 @@ kip:
   defaultContainerRegistry: quay.io
 
 ```
-This deploys JEG with remote kernel management and persistent kernelspec storage.
+
+Then deploy Enterprise Gateway using Helm:
+
+The command below uses a YAML file named enterprise-gateway-helm.yaml, which includes an example configuration shown above.
+
+```yaml
+helm upgrade --install enterprise-gateway \
+  https://github.com/jupyter-server/enterprise_gateway/releases/download/v3.2.3/jupyter_enterprise_gateway_helm-3.2.3.tar.gz \
+  --namespace enterprise-gateway \
+  --values enterprise-gateway-helm.yaml \
+  --create-namespace \
+  --wait
+
+```
 
 ## Step 2: Configure the Notebook to connect to the Jupyter Gateway