Updates to Understand Impact of Changes workshop content

content/en/scenarios/understand_impact_of_changes/1-build-application.md

@@ -19,23 +19,18 @@ You will start with an EC2 instance and perform some [initial steps](#initial-st
 
 ## Initial Steps
 
-The initial setup can be completed by executing the following steps on the command line of your EC2 instance.
-
-You'll be asked to enter a name for your environment.  Please use `tagging-workshop-yourname` (where `yourname` is replaced by your actual name).
+The initial setup can be completed by executing the following steps on the command line of your EC2 instance: 
 
 ``` bash
 cd workshop/tagging
-./1-deploy-otel-collector.sh
-./2-deploy-creditcheckservice.sh
-./3-deploy-creditprocessorservice.sh
-./4-deploy-load-generator.sh
+./0-deploy-collector-with-services.sh
 ```
 
 ## View your application in Splunk Observability Cloud
 
 Now that the setup is complete, let's confirm that it's sending data to **Splunk Observability Cloud**.  Note that when the application is deployed for the first time, it may take a few minutes for the data to appear.
 
-Navigate to APM, then use the Environment dropdown to select your environment (i.e. `tagging-workshop-name`).
+Navigate to APM, then use the Environment dropdown to select your environment (i.e. `tagging-workshop-instancename`).
 
 If everything was deployed correctly, you should see `creditprocessorservice` and `creditcheckservice` displayed in the list of services:
 

content/en/scenarios/understand_impact_of_changes/2-what-are-tags.md

@@ -24,10 +24,26 @@ While some tags can be added with the OpenTelemetry collector, the ones we’ll
 
 A note about terminology before we proceed. While this workshop is about **tags**, and this is the terminology we use in **Splunk Observability Cloud**, OpenTelemetry uses the term **attributes** instead. So when you see tags mentioned throughout this workshop, you can treat them as synonymous with attributes.
 
-## What are tags so important?
+## Why are tags so important?
 
 Tags are essential for an application to be truly observable. As we saw with our credit check service, some users are having a great experience: fast with no errors. But other users get a slow experience or encounter errors.  
 
 Tags add the context to the traces to help us understand why some users get a great experience and others don't.  And powerful features in **Splunk Observability Cloud** utilize tags to help you jump quickly to root cause.
 
-Let's proceed to look at how tags can be captured using OpenTelemetry.
+## Sneak Peak: Tag Spotlight
+
+**Tag Spotlight** uses tags to discover trends that contribute to high latency or error rates: 
+
+![Tag Spotlight Preview](../images/tag_spotlight_preview.png)
+
+The screenshot above provides an example of Tag Spotlight from another application.
+
+Splunk has analyzed all of the tags included as part of traces that involve the payment service.
+
+It tells us very quickly whether some tag values have more errors than others.
+
+If we look at the version tag, we can see that version 350.10 of the service has a 100% error rate, whereas version 350.9 of the service has no errors at all: 
+
+![Tag Spotlight Preview](../images/tag_spotlight_preview_details.png)
+
+We’ll be using Tag Spotlight with the credit check service later on in the workshop, once we’ve captured some tags of our own.

content/en/scenarios/understand_impact_of_changes/5-index-tags.md

@@ -48,7 +48,9 @@ Note that we aren't required to index tags to use them for filtering with **Trac
 
 ### Grouping
 
-With the grouping use case, we can surface trends for tags that we collect using the powerful **Tag Spotlight** feature in **Splunk Observability Cloud**, which we'll see in action shortly.
+With the grouping use case, we can use **Trace Analyzer** to group traces by a particular tag.
+
+But we can also go beyond this and surface trends for tags that we collect using the powerful **Tag Spotlight** feature in **Splunk Observability Cloud**, which we’ll see in action shortly.
 
 Tags used for grouping use cases should be low to medium-cardinality, with hundreds of unique values.
 

content/en/scenarios/understand_impact_of_changes/7-alerting-and-dashboards.md

@@ -41,25 +41,25 @@ To see how, let's click on the metric named `service.request.duration.ns.p99`, w
 
 ![Service Request Duration](../images/service_request_duration_chart.png)
 
-This metric tracks the p99 response time of service requests in nanoseconds, broken down by various attributes. Nanoseconds are bit too granular for our needs, so let's click **Enter formula** and convert this to seconds by entering `A / 1000000000`.  Then we can hide `A` and show only `B`:
+Add filters for `sf_environment`, `sf_service`, and `sf_dimensionalized`.  Then set the **Extrapolation policy** to `Last value` and the **Display units** to `Nanosecond`:
 
-![Chart with Seconds](../images/chart_with_seconds.png)
+![Chart with Seconds](../images/chart_settings.png)
 
-Next, let's break down the chart by credit score category. Click on the **Add analytics** button on the first row, select **Mean**, then **Mean:Aggregation**, then Group By the `credit_score_category` dimension:
+With these settings, the chart allows us to visualize the service request duration by credit score category:
 
 ![Duration by Credit Score](../images/duration_by_credit_score.png)
 
-Now we can see the duration by credit score category. In my example, the green line represents the `exceptional` category, and we can see that the duration for these requests sometimes goes all the way up to 5 seconds.
+Now we can see the duration by credit score category. In my example, the red line represents the `exceptional` category, and we can see that the duration for these requests sometimes goes all the way up to 5 seconds.
 
-The yellow line represents the `very good` category, and has very fast response times.
+The orange represents the `very good` category, and has very fast response times.
 
-The magenta line represents the `poor` category, and has response times between 2-3 seconds.
+The green line represents the `poor` category, and has response times between 2-3 seconds.
 
 It may be useful to save this chart on a dashboard for future reference. To do this, click on the **Save as...** button and provide a name for the chart:
 
 ![Save Chart As](../images/save_chart_as.png)
 
-When asked which dashboard to save the chart to, let's create a new one named `Credit Check Service`:
+When asked which dashboard to save the chart to, let's create a new one named `Credit Check Service - Your Name` (substituting your actual name):
 
 ![Save Chart As](../images/create_dashboard.png)