Change image definitions inside collapser

src/content/docs/new-relic-solutions/observability-maturity/operational-efficiency/dg-baselining.mdx

@@ -20,6 +20,13 @@ redirects:
 ---
 
 import baseliningicon from 'images/oma-oe-dg-baseline-icon.png'
+import comparewithexample from 'images/oma-oe-dg-simple-compare-with-time-series.png'
+import slidingwindowjaggedexample from 'images/oma-oe-dg-jagged-daily-rate-timeseries.png'
+import slidingwindowsmoothexample from 'images/oma-oe-dg-smoothed-daily-rate-timeseries.png'
+import derivativeexample from 'images/oma-oe-dg-rate-of-growth-time-series.png'
+import sumtimeseriesexample from 'images/oma-oe-dg-simple-raw-sum-time-series.png'
+import entitytypeexample from 'images/om-oe-dg-facet-entitytype-example.png'
+import facetmetricnameexample from 'images/om-oe-dg-facet-metricname-example.png'
 
 <img
   src={baseliningicon}
@@ -369,7 +376,11 @@ Understand exactly which groups within the org are contributing which types of d
         SELECT sum(GigabytesIngested) FROM NrConsumption WHERE productLine = 'DataPlatform' AND usageMetric = 'BrowserEventsBytes' SINCE 6 months AGO UNTIL 1 week AGO TIMESERIES 7 weeks COMPARE WITH 2 months ago
         ```
 
-        ![Simple Compare With Time Series](images/oma-oe-dg-simple-compare-with-time-series.png)
+        <img
+            src={comparewithexample}
+            alt="Compare With Example"
+        />
+
       </Collapser>
 
       <Collapser
@@ -391,15 +402,22 @@ Understand exactly which groups within the org are contributing which types of d
         FROM NrConsumption SELECT rate(sum(GigabytesIngested), 1 day) WHERE productLine = 'DataPlatform' SINCE 26 weeks AGO TIMESERIES 1 DAY
         ```
 
-        ![Jagged Daily Rate](images/oma-oe-dg-jagged-daily-rate-timeseries.png)
+        <img
+            src={slidingwindowjaggedexample}
+            alt="Raw Jagged Data"
+        />
 
         Now if we use a [sliding window](/docs/query-your-data/nrql-new-relic-query-language/nrql-query-tutorials/create-smoother-charts-sliding-windows/) of 4 days to reduce the impact of single day events we will see a clearer picture.  Four days is a good choice since it will blur the impact of `weekends` so data for a Sunday will be combined somewhat with data for a Friday etc.
 
         ```sql
         FROM NrConsumption SELECT rate(sum(GigabytesIngested), 1 day) WHERE productLine = 'DataPlatform' since 26 weeks ago TIMESERIES 1 DAY SLIDE BY 4 days
         ```
 
-        ![Smoothed Daily Rate](images/oma-oe-dg-smoothed-daily-rate-timeseries.png)
+        <img
+            src={slidingwindowsmoothexample}
+            alt="Sliding Window Smoothed"
+        />
+
       </Collapser>
 
       <Collapser
@@ -419,7 +437,10 @@ Understand exactly which groups within the org are contributing which types of d
         SELECT derivative(sum(GigabytesIngested) , 7 day) FROM NrConsumption WHERE productLine = 'DataPlatform'  and usageMetric = 'BrowserEventsBytes'  LIMIT MAX SINCE 3 MONTHS AGO UNTIL THIS MONTH TIMESERIES 1 MONTH slide by 3 days COMPARE WITH 1 WEEK AGO
         ```
 
-        ![Rate of Growth Time Series](images/oma-oe-dg-rate-of-growth-time-series.png)
+        <img
+            src={derivativeexample}
+            alt="Derivative Example"
+        />
 
         In this scenario the uptick was so blatant a simple time series of the rate will suffice.  However the benefit of the derivative is it can be more sensitive at assessing the relative quantity of growth and give us a sense of when it first started.  This can be useful if we are in the early stages of a major uptick.
 
@@ -429,7 +450,11 @@ Understand exactly which groups within the org are contributing which types of d
         SELECT sum(GigabytesIngested) FROM NrConsumption WHERE productLine = 'DataPlatform'  and usageMetric = 'BrowserEventsBytes'  TIMESERIES  7 days SINCE 6 MONTHS AGO
         ```
 
-        ![Simple SUM Time Series](images/oma-oe-dg-simple-raw-sum-time-series.png)
+        <img
+            src={sumtimeseriesexample}
+            alt="Raw Sum Example"
+        />
+
       </Collapser>
 
       <Collapser
@@ -571,7 +596,12 @@ Understand exactly which groups within the org are contributing which types of d
         When we need to drill deeper to know how much data specific event is consuming, we can use `entityType()` in a facet clause to get that result.
         Adding the clause `FACET entityType()` to the previous query gives us:
 
-        ![Entity Type Example](images/om-oe-dg-facet-entitytype-example.png)
+        <img
+            src={entitytypeexample}
+            alt="Entity Type Example"
+        />
+
+
       </Collapser>
 
       <Collapser
@@ -604,8 +634,11 @@ Understand exactly which groups within the org are contributing which types of d
         ```sql
         SELECT bytecountestimate()/10e8 as 'Gigabytes' from Metric facet metricName where metricName like '%kube_pod%' since 1 day ago limit max
         ```
+        <img
+            src={facetmetricnameexample}
+            alt="Facet Metric Name Example"
+        />
 
-        ![Facet Metric Name Example](images/om-oe-dg-facet-metricname-example.png)
       </Collapser>
     </CollapserGroup>
   </Collapser>