Update ClickHouse GCP learning path content

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/benchmarking.md

@@ -1,16 +1,13 @@
 ---
-title: ClickHouse Benchmarking on Google Axion (Arm)
+title: Benchmark ClickHouse on Google Axion processors
 weight: 9
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 ## ClickHouse benchmarking on Axion processors
 
-
-## ClickHouse Benchmarking on Axion Processors
-This phase benchmarks **query latency on ClickHouse running on Google Axion (Arm64)**.  
-The goal is to measure **repeatable query latency** with a focus on **p95 latency**, using data ingested via the real-time Dataflow pipeline.
+In this section you'll benchmark query latency on ClickHouse running on Google Axion (Arm64). The goal is to measure repeatable query latency with a focus on p95 latency, using data ingested with the real-time Dataflow pipeline.
 
 ## Prepare ClickHouse for Accurate Latency Measurement
 
@@ -65,9 +62,9 @@ Peak memory usage: 106.54 MiB.
 
 These queries represent common real-time analytics patterns:
 
-- **Filtered count**: service-level analytics
-- **Time-windowed count**: recent activity
-- **Aggregation by service**: grouped analytics
+- Filtered count: service-level analytics
+- Time-windowed count: recent activity
+- Aggregation by service: grouped analytics
 
 Each query scans and processes millions of rows to stress the execution engine.
 
@@ -119,7 +116,8 @@ GROUP BY service
 ORDER BY total DESC;
 ```
 
-You should see an output similar to:
+The output is similar to:
+
 ```output
 Query id: c48c0d30-0ef6-4fb9-bbb9-815a509a5f91
 
@@ -236,16 +234,18 @@ sort -n latency-results.txt
 ```
 
 **Calculate p95 latency (manual):**
+
 The p95 latency represents the value under which 95% of query executions complete.
 
 **Formula:**
 
-```pqsql
+```
 p95 index = ceil(0.95 × N)
 ```
 
 For 10 samples:
-```cpp
+
+```
 ceil(0.95 × 10) = ceil(9.5) = 10
 ```
 
@@ -263,9 +263,9 @@ After executing the ClickHouse query 10 times on the GCP Axion (Arm) VM, the obs
 
 You've successfully completed a comprehensive ClickHouse benchmarking exercise on Google Axion (Arm64) processors. Key results from the `c4a-standard-4` (4 vCPU, 16 GB memory) Arm64 VM running SUSE:
 
-- ClickHouse on **Google Axion (Arm64)** delivered consistently low query latency, even while scanning ~6 million rows per query.
-- Across **10 repeat executions, the p95 latency was ~11 ms**, indicating stable and predictable performance.
+- ClickHouse on Google Axion (Arm64) delivered consistently low query latency, even while scanning ~6 million rows per query.
+- Across 10 repeat executions, the p95 latency was ~11 ms, indicating stable and predictable performance.
 - Disabling the query cache ensured true execution latency, not cache-assisted results.
-- Analytical queries sustained **500M+ rows/sec throughput** with minimal memory usage.
+- Analytical queries sustained 500M+ rows/sec throughput with minimal memory usage.
 
 Throughout this Learning Path, you provisioned an Arm-based VM on Google Cloud, deployed ClickHouse, configured a real-time streaming pipeline with Pub/Sub and Dataflow, and validated end-to-end analytical performance. You can now deploy, optimize, and benchmark ClickHouse workloads on Google Cloud Arm infrastructure.

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/dataflow-streaming-etl.md

@@ -1,5 +1,5 @@
 ---
-title: Dataflow Streaming ETL to ClickHouse
+title: Build a Dataflow streaming ETL pipeline to ClickHouse
 weight: 8
 
 ### FIXED, DO NOT MODIFY
@@ -138,11 +138,9 @@ Successful output confirms:
 - Subscription is readable
 - IAM is functioning correctly
 
-### Create Dataflow Streaming ETL Script
-Create the Dataflow pipeline file:
+### Create Dataflow streaming ETL script
 
-Purpose
-Defines a streaming Beam pipeline that:
+Create the Dataflow pipeline file that defines a streaming Beam pipeline to:
 
 - Reads JSON events from Pub/Sub
 - Parses messages
@@ -212,8 +210,9 @@ gcloud pubsub subscriptions list
 hostname -I
 ```
 
-### Run the Dataflow Streaming Job
-Launches the pipeline on managed Dataflow workers.
+### Run the Dataflow streaming job
+
+Launch the pipeline on managed Dataflow workers:
 
 ```console
 python3.11 dataflow_etl.py \
@@ -237,8 +236,9 @@ Autoscaling is enabled for Dataflow Streaming Engine. Workers will scale between
 - Streaming mode is active
 - Workers scale automatically
 
-### End-to-End Validation
-Publish live streaming data.
+### End-to-end validation
+
+Publish live streaming data:
 
 ```console
 gcloud pubsub topics publish logs-topic \

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/instance.md

@@ -28,8 +28,8 @@ To create a virtual machine based on the C4A instance type:
 
 
 - Under **OS and Storage**, select **Change**, then choose an Arm64-based OS image. For this Learning Path, use **SUSE Linux Enterprise Server**. 
-- If using use **SUSE Linux Enterprise Server**. Select "Pay As You Go" for the license type.
-- Once appropriately selected and configured, please Click **Select**.
+- If using **SUSE Linux Enterprise Server**, select "Pay As You Go" for the license type.
+- Once appropriately selected and configured, click **Select**.
 - Under **Networking**, enable **Allow HTTP traffic**.
 - Click **Create** to launch the instance.
 - Once created, you should see a "SSH" option to the right in your list of VM instances.  Click on this to launch a SSH shell into your VM instance:

content/learning-paths/servers-and-cloud-computing/clickhouse-gcp/pub_sub_creation.md

@@ -1,5 +1,5 @@
 ---
-title: GCP Pub/Sub and IAM Setup for ClickHouse Real-Time Analytics on Axion
+title: Set up GCP Pub/Sub and IAM for ClickHouse real-time analytics on Axion
 weight: 5
 
 ### FIXED, DO NOT MODIFY
@@ -13,31 +13,31 @@ It focuses on **Pub/Sub resource creation and IAM roles**, ensuring Dataflow and
 ### Create Pub/Sub Topic
 The Pub/Sub topic acts as the **ingestion entry point** for streaming log events.
 
-1. Open **Google Cloud Console**
-2. Navigate to **Pub/Sub → Topics**
-3. Click **Create Topic**
-4. Enter:
+- Open **Google Cloud Console**
+- Navigate to **Pub/Sub → Topics**
+- Click **Create Topic**
+- Enter:
    - **Topic ID:** `logs-topic`
-5. Leave encryption and retention as the default
-6. Click **Create**
+- Leave encryption and retention as the default
+- Click **Create**
 
 This topic will receive streaming log messages from producers.
 
-![ GCP onsole alt-text#center](images/pub_sub1.png "Figure 1: Pub/Sub Topic")
+![Google Cloud Console showing Pub/Sub topic creation screen with logs-topic configured alt-txt#center](images/pub_sub1.png "Pub/Sub Topic")
 
 ### Create Pub/Sub Subscription
 
 The subscription allows **Dataflow to pull messages** from the topic.
 
-1. Open the newly created `logs-topic`
-2. Click **Create Subscription**
-3. Configure:
+- Open the newly created `logs-topic`
+- Click **Create Subscription**
+- Configure:
    - **Subscription ID:** `logs-sub`
    - **Delivery type:** Pull
    - **Ack deadline:** Default (10 seconds)
-4. Click **Create**
+- Click **Create**
 
-![ GCP onsole alt-text#center](images/pub_sub2.png "Figure 2: Pub/Sub Subscription")
+![Google Cloud Console displaying Pub/Sub subscription configuration screen for logs-sub with Pull delivery alt-txt#center](images/pub_sub2.png "Pub/Sub Subscription")
 
 This subscription will later be referenced by the Dataflow pipeline.
 
@@ -50,7 +50,7 @@ Navigate to **Pub/Sub → Topics** and confirm:
 
 This confirms the messaging layer is ready.
 
-![ GCP onsole alt-text#center](images/verify_pub_sub.png "Figure 3: Pub/Sub Resources")
+![Google Cloud Console Pub/Sub topics list showing logs-topic and logs-sub subscription verified alt-txt#center](images/verify_pub_sub.png "Pub/Sub Resources")
 
 ### Identify Compute Engine Service Account
 
@@ -82,30 +82,30 @@ Grant the following roles to the **Compute Engine default service account**:
 | Service Account User | Allow service account usage |
 
 **Steps (UI):**
-1. Go to **IAM & Admin → IAM**
-2. Click **Grant Access**
-3. Add the service account
-4. Assign the roles listed above
-5. Save
+- Go to **IAM & Admin → IAM**
+- Click **Grant Access**
+- Add the service account
+- Assign the roles listed above
+- Save
 
-![Google Cloud Console IAM page displaying assigned roles for Compute Engine service account including Dataflow and Pub/Sub permissions alt-txt#center](images/roles.png "Figure 4: Required IAM Roles")
+![Google Cloud Console IAM page displaying assigned roles for Compute Engine service account including Dataflow and Pub/Sub permissions alt-txt#center](images/roles.png "Required IAM Roles")
 
 VM OAuth scopes are limited by default. IAM roles are authoritative.
 
 ### Create GCS Bucket for Dataflow (UI)
 
 Dataflow requires a Cloud Storage bucket for staging and temp files.
 
-1. Go to **Cloud Storage → Buckets**
-2. Click **Create**
-3. Configure:
+- Go to **Cloud Storage → Buckets**
+- Click **Create**
+- Configure:
    - **Bucket name:** `imperial-time-463411-q5-dataflow-temp`
    - **Location type:** Region
    - **Region:** `us-central1`
-4. Leave defaults for storage class and access control
-5. Click **Create**
+- Leave defaults for storage class and access control
+- Click **Create**
 
-![ GCP onsole alt-text#center](images/bucket.png "Figure 5: GCS Bucket")
+![Google Cloud Console showing Cloud Storage bucket creation screen with dataflow-temp bucket configured alt-txt#center](images/bucket.png "GCS Bucket")
 
 ### Grant Bucket Access