Final tweaks, including labelling diagram.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/spark-on-azure/_index.md

@@ -1,5 +1,5 @@
 ---
-title: Run Spark applications on the Microsoft Azure Cobalt 100 processors
+title: Run Spark applications on Microsoft Azure Cobalt 100 processors
 
 minutes_to_complete: 60
 

content/learning-paths/servers-and-cloud-computing/spark-on-azure/background.md

@@ -1,12 +1,12 @@
 ---
-title: Overview of Microsoft Azure Cobalt 100, Azure Linux 3.0, and Apache Spark
+title: Getting started with Microsoft Azure Cobalt 100, Azure Linux 3.0, and Apache Spark
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: "learningpathall"
 ---
 
-## Learn about Azure Cobalt 100, Azure Linux 3.0, and Apache Spark
+## Key technologies for running Apache Spark on Azure Cobalt 100
 
 This section introduces the key technologies you will use when running Spark applications on Microsoft Azure Cobalt 100 processors. You will learn about the Azure Cobalt 100 Arm-based processor, Azure Linux 3.0, and Apache Spark.
 
@@ -16,16 +16,18 @@ Azure Cobalt 100 is Microsoft’s first-generation Arm-based processor, designed
 
 Typical workloads include web and application servers, data analytics, open-source databases, and caching systems.
 
-To learn more, see the blog [Announcing the preview of new Azure virtual machines based on the Azure Cobalt 100 processor](https://techcommunity.microsoft.com/blog/azurecompute/announcing-the-preview-of-new-azure-vms-based-on-the-azure-cobalt-100-processor/4146353).
+To learn more, see the Microsoft blog [Announcing the preview of new Azure virtual machines based on the Azure Cobalt 100 processor](https://techcommunity.microsoft.com/blog/azurecompute/announcing-the-preview-of-new-azure-vms-based-on-the-azure-cobalt-100-processor/4146353).
 
 ## Azure Linux 3.0 
 
-Azure Linux 3.0 is Microsoft’s lightweight Linux distribution optimized for cloud-native workloads on Azure. It is designed for performance, security, and reliability, and is supported by Microsoft. Azure Linux 3.0 is tailored for containers, microservices, and Kubernetes.
+Azure Linux 3.0 is Microsoft’s lightweight Linux distribution optimized for cloud-native workloads on Azure. It is designed for performance, security, and reliability. Azure Linux 3.0 is tailored for containers, microservices, and Kubernetes.
 
 With native support for Arm64 (AArch64) architecture, Azure Linux 3.0 enables efficient execution of workloads on Arm-based infrastructure, making it a scalable and cost-effective choice for cloud deployments.
 
 ## Apache Spark 
 
 Apache Spark is an open-source, distributed computing system for fast, general-purpose big data processing. It provides high-level APIs in Java, Scala, Python, and R, and supports in-memory computation for improved performance.
 
-Spark is widely used for large-scale data analytics, machine learning, and real-time data processing. Learn more at the [Apache Spark official website](https://spark.apache.org/) and in the [official documentation](https://spark.apache.org/docs/latest/).
+Spark is widely used for large-scale data analytics, machine learning, and real-time data processing. 
+
+Learn more at the [Apache Spark official website](https://spark.apache.org/) and in the [official documentation](https://spark.apache.org/docs/latest/).

content/learning-paths/servers-and-cloud-computing/spark-on-azure/baseline.md

@@ -1,12 +1,12 @@
 ---
-title: Functional validation of Apache Spark on Azure Cobalt 100 Arm64
+title: Validate Apache Spark on Azure Cobalt 100 Arm64 VMs
 weight: 6
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Functional validation of Apache Spark on Azure Cobalt 100 Arm64
+## Run a functional test of Apache Spark on Azure Cobalt 100 Arm64
 
 After installing Apache Spark on your Arm64 virtual machine, you can perform simple baseline testing to validate that Spark runs correctly and produces the expected output.
 
@@ -47,6 +47,6 @@ You should see output similar to:
 
 ## Output summary
 
-- Spark successfully generated code (10.5 ms) and executed a simple DataFrame operation  
-- The test data **[1, "ARM64"]** and **[2, "Azure"]** was displayed before cleanly shutting down (exitCode 0)  
-- This confirms a working Spark deployment on Arm64  
+- Spark successfully generated code (10.5 ms) and executed a simple DataFrame operation.  
+- The test data **[1, "ARM64"]** and **[2, "Azure"]** was displayed before cleanly shutting down (exitCode 0).  
+- This confirms a working Spark deployment on Arm64.  

content/learning-paths/servers-and-cloud-computing/spark-on-azure/benchmarking.md

@@ -181,7 +181,7 @@ You should see output similar to the following:
 [info] broadcast nested loop join wholestage on           18857          18928          84          1.1         899.2       1.4X
 [success] Total time: 1644 s (27:24), completed Jul 25, 2025, 6:27:46 AM
 ```
-### Benchmark Results Table Explained:
+## Benchmark Results Table Explained:
 
 - **Best Time (ms):** Fastest execution time observed (in milliseconds).
 - **Avg Time (ms):** Average time across all iterations.
@@ -191,11 +191,11 @@ You should see output similar to the following:
 - **Relative Speed comparison:** baseline (1.0X) is the slower version.
 
 {{% notice Note %}}
-Benchmarks were performed in both an Azure Linux 3.0 Docker container and an Azure Linux 3.0 virtual machine. Results were found to be comparable.
+Benchmark results on Azure Linux 3.0 were consistent across both Docker containers and virtual machines.
 {{% /notice %}}
 
 
-### Benchmark summary on Arm64:
+## Benchmark summary on Arm64:
 For easier comparison, shown here is a summary of benchmark results collected on an Arm64 `D4ps_v6` Azure virtual machine created from a custom Azure Linux 3.0 image using the AArch64 ISO.
 | Benchmark                              | Wholestage | Best Time (ms) | Avg Time (ms) | Stdev (ms) | Rate (M/s) | Per Row (ns) | Relative |
 |----------------------------------------|------------|----------------|----------------|------------|-------------|----------------|----------|
@@ -222,7 +222,7 @@ For easier comparison, shown here is a summary of benchmark results collected on
 | Broadcast nested loop join             | Off        | 26847          | 26870          | 32         | 0.8         | 1280.2         | 1.0X     |
 |                                        | On         | 18857          | 18928          | 84         | 1.1         | 899.2          | 1.4X     |
 
-### Benchmark summary on x86_64:
+## Benchmark summary on x86_64:
 Shown here is a summary of the benchmark results collected on an `x86_64` `D4s_v4` Azure virtual machine using the Azure Linux 3.0 image published by Ntegral Inc.
 | Benchmark                                | Wholestage | Best Time (ms) | Avg Time (ms) | Stdev (ms) | Rate (M/s) | Per Row (ns) | Relative |
 |------------------------------------------|------------|----------------|----------------|------------|-------------|----------------|----------|
@@ -250,7 +250,7 @@ Shown here is a summary of the benchmark results collected on an `x86_64` `D4s_v
 |                                           | On         | 31254          | 31346          | 78         | 0.7         | 1490.3         | 1.2X     |
 
 
-### Benchmark comparison insights
+## Benchmark comparison insights
 
 When comparing the results on Arm64 vs x86_64 virtual machines:
 

content/learning-paths/servers-and-cloud-computing/spark-on-azure/container-setup.md

@@ -14,7 +14,7 @@ You can deploy your Spark workload either in an Azure Linux 3.0 Docker container
 
 The Azure Linux Container Host is an operating system image optimized for running container workloads on Azure Kubernetes Service (AKS). Microsoft maintains the Azure Linux Container Host, which is based on CBL-Mariner, an open-source Linux distribution created by Microsoft. 
 
-To learn more, see [What is Azure Linux Container Host for AKS](https://learn.microsoft.com/en-us/azure/azure-linux/intro-azure-linux).
+To learn more, see [What is Azure Linux Container Host for AKS?](https://learn.microsoft.com/en-us/azure/azure-linux/intro-azure-linux)
 
 Azure Linux 3.0 supports AArch64. However, a standalone virtual machine image for Azure Linux 3.0 or CBL Mariner 3.0 is not yet available for Arm. To use the default Microsoft software stack, you can run a Docker container with Azure Linux 3.0 as the base image and run your Spark application inside the container.
 
@@ -36,8 +36,10 @@ Currently, the Azure Marketplace offers official virtual machine images of Azure
 
 For detailed steps, see [Create an Azure Linux 3.0 virtual machine with Cobalt 100 processors](/learning-paths/servers-and-cloud-computing/azure-vm).
 
----
+## Next steps
 
+{{% notice Note %}}
 Whether you use an Azure Linux 3.0 Docker container or a virtual machine created from a custom image, the Spark deployment and benchmarking steps in the following sections remain the same.
+{{% /notice %}}
 
 Once the setup is complete, continue to the next section to install and run Spark.

content/learning-paths/servers-and-cloud-computing/spark-on-azure/create-instance.md

@@ -1,31 +1,31 @@
 ---
-title: Create an Arm-based virtual machine using Azure Cobalt 100
+title: Create an Azure Cobalt 100 Arm64 virtual machine
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Create an Arm-based virtual machine using Azure Cobalt 100
+## Create an Azure Cobalt 100 Arm64 VM using the Azure portal
 
-You can create an Azure Cobalt 100 Arm-based virtual machine in several ways: using the Microsoft Azure console, the Azure CLI, or your preferred Infrastructure as Code (IaC) tool. In this Learning Path, you will use the Azure console to create a virtual machine with the Azure Cobalt 100 Arm-based processor.
+You can create an Azure Cobalt 100 Arm64 virtual machine in several ways, including the Azure portal, the Azure CLI, or an Infrastructure as Code (IaC) tool. 
 
-## Create an Arm-based Azure virtual machine
+In this Learning Path, you’ll use the Azure portal to create a VM with the Cobalt 100 processor, following a process similar to creating any other virtual machine in Azure.
 
-Creating a virtual machine based on Azure Cobalt 100 is similar to creating any other virtual machine in Azure. To create an Azure virtual machine, open the Azure portal and go to **Virtual Machines**.
+## Step-by-step: create the virtual machine
 
-1. Select **Create**  
-2. Enter details such as **Name** and **Region**  
-3. Choose the image for your virtual machine (for example, Ubuntu 24.04) and select **Arm64** as the architecture  
-4. In the **Size** field, click **See all sizes**, then choose the D-Series v6 family of virtual machines  
-5. Select **D4ps_v6** from the list and create the virtual machine  
+1. In the Azure portal, go to **Virtual Machines** and select **Create**.  
+2. Enter details such as **Name** and **Region**.  
+3. Choose the image for your virtual machine (for example, Ubuntu 24.04) and select **Arm64** as the architecture.  
+4. In the **Size** field, select **See all sizes**, then choose the D-Series v6 family of virtual machines.  
+5. Select **D4ps_v6** from the list and create the virtual machine.  
 
-![Azure portal showing VM creation with D4ps_v6 selected](./instance-new.png)
+![Azure portal VM creation — Azure Cobalt 100 Arm64 virtual machine (D4ps_v6) alt-text#center](./instance-new.png "Figure 1: Create an Azure Cobalt 100 Arm64 VM in the Azure portal")
 
-Once the virtual machine is running, you can SSH into it using the generated PEM key and the public IP address of the instance.
+Once the Arm64 virtual machine is running, you can SSH into it using the generated PEM key and the public IP address of the instance.
 
 {{% notice Note %}}
 
-To learn more about Arm-based virtual machines in Azure, see [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/azure).
+To learn more about Arm-based virtual machines in Azure, see the Learning Path [Get started with Arm-based cloud instances](/learning-paths/servers-and-cloud-computing/csp/azure).
 
 {{% /notice %}}

content/learning-paths/servers-and-cloud-computing/spark-on-azure/deploy.md

@@ -1,16 +1,17 @@
 ---
-title: Install Apache Spark on Azure Cobalt 100 processors
+title: Install Apache Spark on Azure Cobalt 100 Arm64 processors
 weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Install Apache Spark
+## Install Apache Spark on Azure Cobalt 100 Arm64
 Within your running docker container image or your custom Azure Linux VM, follow the instructions to install Spark.
 
 Start by installing Java, Python, and other essential tools: 
-### Install Required Packages 
+
+## Install Java, Python, and tools for Apache Spark
 
 ```console
 sudo tdnf update -y
@@ -38,7 +39,7 @@ The output will look like:
 Python 3.12.9
 ```
 
-### Install Apache Spark on Arm
+## Download and install Apache Spark on Azure Cobalt 100 (Arm64)
 
 You can now download and configure Apache Spark on your Arm-based machine:
 
@@ -47,7 +48,7 @@ wget https://downloads.apache.org/spark/spark-3.5.6/spark-3.5.6-bin-hadoop3.tgz
 tar -xzf spark-3.5.6-bin-hadoop3.tgz
 sudo mv spark-3.5.6-bin-hadoop3 /opt/spark
 ```
-### Set Environment Variables 
+## Configure environment variables for Apache Spark
 Add this line to ~/.bashrc or ~/.zshrc to make the change persistent across terminal sessions.
 
 ```cosole
@@ -61,7 +62,7 @@ Apply changes immediately in your running shell:
 source ~/.bashrc
 ```
 
-### Verify Spark Installation
+## Verify Apache Spark installation on Azure Cobalt 100
 
 ```console
 spark-submit --version