ektravel commented on code in PR #16762:
URL: https://github.com/apache/druid/pull/16762#discussion_r1691841709


##########
docs/tutorials/tutorial-rollup.md:
##########
@@ -24,18 +24,21 @@ sidebar_label: Aggregate data with rollup
   -->
 
 
-Apache Druid can summarize raw data at ingestion time using a process we refer 
to as "rollup". Rollup is a first-level aggregation operation over a selected 
set of columns that reduces the size of stored data.
+Apache Druid® can summarize raw data at ingestion time using a 
process known as "rollup." [Rollup](../ingestion/rollup.md) is a first-level 
aggregation operation over a selected set of columns that reduces the size of 
stored data.
 
-This tutorial will demonstrate the effects of rollup on an example dataset.
+The tutorial demonstrates how to apply rollup at ingestion and shows the 
effect of rollup at query time. See [ingesting with 
rollup](https://druid.apache.org/docs/latest/multi-stage-query/concepts/#rollup)
 to learn more. 
 
-For this tutorial, we'll assume you've already downloaded Druid as described in
-the [single-machine quickstart](index.md) and have it running on your local 
machine.
+## Prerequisites
 
-It will also be helpful to have finished [Load a 
file](../tutorials/tutorial-batch.md) and [Query 
data](../tutorials/tutorial-query.md) tutorials.
+Before proceeding, download Druid as described in [Quickstart 
(local)](index.md) and have it running on your local machine. You don't need to 
load any data into the Druid cluster.
 
-## Example data
+You should be familiar with data querying in Druid. If you haven't already, go 
through the [Query data](../tutorials/tutorial-query.md) tutorial first.
 
-For this tutorial, we'll use a small sample of network flow event data, 
representing packet and byte counts for traffic from a source to a destination 
IP address that occurred within a particular second.
+
+## Load the example data
+
+For this tutorial, you use a small sample of network flow event data, 
representing IP traffic.

Review Comment:
   ```suggestion
   For this tutorial, you use a small sample of network flow event data 
representing IP traffic.
   ```



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