Broken documentation links

Author: rcastley

content/en/ninja-workshops/1-automatic-discovery/2-petclinic-kubernetes/6-profiling-db-query/1-profiling.md

@@ -49,7 +49,7 @@ OpenJDK 64-Bit Server VM warning: Sharing is only supported for boot loader clas
 
 {{% /tab %}}
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-We are interested in the section written by the  `com.splunk.opentelemetry.profiler.ConfigurationLogger` or the **Profiling Configuration**.
+We are interested in the section written by the `com.splunk.opentelemetry.profiler.ConfigurationLogger` or the **Profiling Configuration**.
 
 We can see the various settings you can control, such as the `splunk.profiler.directory`, which is the location where the agent writes the call stacks before sending them to Splunk. (This may be different depending on how you configure your containers.)
 

content/en/ninja-workshops/1-automatic-discovery/2-petclinic-kubernetes/6-profiling-db-query/2-waterfall.md

@@ -4,20 +4,20 @@ linkTitle: 2. Trace Waterfall
 weight: 2
 ---
 
-Make sure you have your original (or similar) Trace & Span **(1)** selected in the APM Waterfall view and select  **Memory Stack Traces (2)** from the right-hand pane:
+Make sure you have your original (or similar) Trace & Span **(1)** selected in the APM Waterfall view and select **Memory Stack Traces (2)** from the right-hand pane:
 
 ![profiling from span](../../images/flamechart-in-waterfall.png)
 
 The pane should show you the Memory Stack Trace Flame Graph **(3)**. You can scroll down and/or expand by dragging the right side of the pane.
 
-AlwaysOn Profiling is constantly taking snapshots, or stack traces, of your application’s code. Imagine having to read read through thousands of stack traces! It is simply not practical. To assist with this, AlwaysOn Profiling aggregates and summarizes profiling data, providing a convenient way to explore Call Stacks in a view called the **Flame Graph**. It represents a summary of all stack traces captured from your application.  You can use the Flame Graph to discover which lines of code might be causing performance issues and to confirm whether changes you make to the code have the intended effect.
+AlwaysOn Profiling is constantly taking snapshots, or stack traces, of your application’s code. Imagine having to read through thousands of stack traces! It is simply not practical. To assist with this, AlwaysOn Profiling aggregates and summarizes profiling data, providing a convenient way to explore Call Stacks in a view called the **Flame Graph**. It represents a summary of all stack traces captured from your application.  You can use the Flame Graph to discover which lines of code might be causing performance issues and to confirm whether changes you make to the code have the intended effect.
 
 To dive deeper into Always-on Profiling, select Span **(3)** (as referenced in the above image) in the Profiling Pane under **Memory Stack Traces**. This will open the Always-on Profiling main screen, with the Memory view pre-selected:
 
 ![Profiling main](../../images/profiling-memory.png)
 
 * The Time filter will be set to the time frame of the span we selected **(1)**
-* Java Memory Metric Charts **(2)**  allow you to `Monitor Heap Memory, Application Activity` like `Memory Allocation Rate` and `Garbage Collecting` Metrics.
+* Java Memory Metric Charts **(2)** allow you to `Monitor Heap Memory, Application Activity` like `Memory Allocation Rate` and `Garbage Collecting` Metrics.
 * Ability to focus/see metrics and Stack Traces only related to the Span **(3)**, This will filter out background activities running in the Java application if required.
 * Java Function calls identified **(4)**, allowing you to drill down into the Methods called from that function.
 * The Flame Graph **(5)**, with the visualization of hierarchy based on the stack traces of the profiled service.

content/en/ninja-workshops/1-automatic-discovery/2-petclinic-kubernetes/6-profiling-db-query/3-dbquery.md

@@ -4,7 +4,7 @@ linkTitle: 3. Database Query Performance
 weight: 3
 ---
 
-With Database Query Performance, you can monitor the impact of your database queries on service availability directly in Splunk APM. This way, you can quickly identify long-running, un-optimized, or heavy queries and mitigate issues they might be causing, without having to instrument your databases.
+With Database Query Performance, you can monitor the impact of your database queries on service availability directly in Splunk APM. This way, you can quickly identify long-running, unoptimized, or heavy queries and mitigate issues they might be causing, without having to instrument your databases.
 
 To look at the performance of your database queries, make sure you are on the APM **Service Map** page either by going back in the browser or navigating to the APM section in the Menu bar, then click on the **Service Map** tile.
 
@@ -19,7 +19,7 @@ This will bring us to the DB-query Performance overview screen:
 ![DB-query full](../../images/db-query-full.png)
 
 {{% notice title="Database Query Normalization" style="info" %}}
-By default, Splunk APM instrumentation sanitizes database queries to remove or mask sensible data, such as secrets or personally identifiable information (PII) from the `db.statements`. You can find how to turn off database query normalization [here](https://docs.splunk.com/observability/en/apm/db-query-perf/db-perf-troubleshooting.html#turn-off-database-query-normalization).
+By default, Splunk APM instrumentation sanitizes database queries to remove or mask sensible data, such as secrets or personally identifiable information (PII) from the `db.statements`. You can find how to turn off database query normalization [here](https://help.splunk.com/en/splunk-observability-cloud/monitor-application-performance/monitor-database-query-performance/troubleshoot-database-query-performance#turn-off-database-query-normalization).
 {{% /notice %}}
 
 This screen will show us all the Database queries **(1)** done to our database from your application, based on the Traces & Spans sent to the Splunk Observability Cloud.  Note that you can compare them across a time block or sort them on Total Time, P90 Latency & Requests **(2)**.
@@ -32,7 +32,7 @@ If you need to see a detailed view of a query:
 
 ![details](../../images/query-details.png)
 
-click on the specific Query **(1)**. This will open  the Query Details pane **(2)**, which you can use for more detailed investigations.
+Click on the specific Query **(1)**. This will open the Query Details pane **(2)**, which you can use for more detailed investigations.
 
 <!--
 

content/en/workshop-setup/_index.md

@@ -65,4 +65,4 @@ The official workshop presentation is available [**here**](https://docs.google.c
 
 ##### **Splunk4Rookies - FSI Vertical Observability Cloud Workshop Presentation**
 
-The official FSI workshop presentation is available [**here**](https://docs.google.com/presentation/d/162ucBDaAEuJamtBDYEWe07js0ju4dkxfFdG2uAw0Vwc/edit?usp=sharing). Use this resource to prepare your workshop and to guide your participants through the material.
+The official FSI workshop presentation is available [**here**](https://docs.google.com/presentation/d/162ucBDaAEuJamtBDYEWe07js0ju4dkxfFdG2uAw0Vwc/edit?usp=sharing). Use this resource to prepare your workshop and to guide your participants through the material.