Apply suggestions from code review

Co-authored-by: Shaun Struwig <[email protected]>

Author: gingerwizard

docs/use-cases/observability/clickstack/migration/elastic/migrating-data.md

@@ -31,7 +31,7 @@ Set up your data collection pipeline to send data to both Elastic and ClickStack
 Configure Elastic's TTL settings to match your desired retention period. Set up the ClickStack [TTL](/use-cases/observability/clickstack/production#configure-ttl) to maintain data for the same duration.
 
 3. **Validate and Compare**:
-
+<br/>
 - Run queries against both systems to ensure data consistency
 - Compare query performance and results
 - Migrate dashboards and alerts to ClickStack. This is currently a manual process.
@@ -506,7 +506,7 @@ ORDER BY (`host.name`, `@timestamp`)
 
 Note that:
 
- - We've used Tuples to represent nested structures instead of dot notation
+ - Tuples are used to represent nested structures instead of dot notation
  - Used appropriate ClickHouse types based on the mapping:
    - `keyword` → `String`
    - `date` → `DateTime`
@@ -523,9 +523,9 @@ Note that:
 This strict schema has a number of benefits:
 
 - **Data validation** – enforcing a strict schema avoids the risk of column explosion, outside of specific structures. 
-- **Avoids risk of column explosion** - Although the JSON type scales to potentially thousands of columns, where subcolumns are stored as dedicated columns, this can lead to a column file explosion where an excessive number of column files are created that impacts performance. To mitigate this, the underlying [Dynamic type](/sql-reference/data-types/dynamic) used by JSON offers a [`max_dynamic_paths`](/sql-reference/data-types/newjson#reading-json-paths-as-sub-columns) parameter, which limits the number of unique paths stored as separate column files. Once the threshold is reached, additional paths are stored in a shared column file using a compact encoded format, maintaining performance and storage efficiency while supporting flexible data ingestion. Accessing this shared column file is, however, not as performant. Note, however, that the JSON column can be used with [type hints](/integrations/data-formats/json/schema#using-type-hints-and-skipping-paths). "Hinted" columns will deliver the same performance as dedicated columns.
+- **Avoids risk of column explosion**: although the JSON type scales to potentially thousands of columns, where subcolumns are stored as dedicated columns, this can lead to a column file explosion where an excessive number of column files are created that impacts performance. To mitigate this, the underlying [Dynamic type](/sql-reference/data-types/dynamic) used by JSON offers a [`max_dynamic_paths`](/sql-reference/data-types/newjson#reading-json-paths-as-sub-columns) parameter, which limits the number of unique paths stored as separate column files. Once the threshold is reached, additional paths are stored in a shared column file using a compact encoded format, maintaining performance and storage efficiency while supporting flexible data ingestion. Accessing this shared column file is, however, not as performant. Note, however, that the JSON column can be used with [type hints](/integrations/data-formats/json/schema#using-type-hints-and-skipping-paths). "Hinted" columns will deliver the same performance as dedicated columns.
 - **Simpler introspection of paths and types** - Although the JSON type supports [introspection functions](/sql-reference/data-types/newjson#introspection-functions) to determine the types and paths that have been inferred, static structures can be simpler to explore e.g. with `DESCRIBE`.
-
+<br/>
 Alternatively, users can simply create a table with one `JSON` column.
 
 ```sql