[jupyter] Apply suggestions

Co-authored-by: Vojtěch Juránek <vjuranek@redhat.com>
Co-authored-by: Fiore Mario Vitale <mvitale86@gmail.com>
Signed-off-by: Jiri Pechanec <jiri.pechanec@centrum.cz>

Author: 3 people

_posts/2026-05-05-debezium-and-jupyter-integration.adoc

@@ -283,7 +283,7 @@ And once the data is in a data frame, you can naturally extend the notebook furt
 
 == Why This Matters
 
-At first glance, a notebook may seem like a toy compared to a full CDC pipeline.
+At the first glance, a notebook may seem like a toy compared to a full CDC pipeline.
 I do not think that is the right way to look at it.
 
 Interactive environments are often the fastest route to clarity.
@@ -321,12 +321,12 @@ That is the right choice for a first example, but it should also give you ideas
 
 For instance, you could:
 
-* capture multiple tables and analyze them together,
-* flatten events before analysis,
-* persist the captured records into Parquet or DuckDB,
-* visualize event rates with matplotlib,
-* connect the notebook to a machine learning workflow,
-* compare snapshot and streaming latency under different connector settings.
+* capture multiple tables and analyze them together
+* flatten events before analysis
+* persist the captured records into Parquet or DuckDB
+* visualize event rates with matplotlib
+* connect the notebook to a machine learning workflow
+* compare snapshot and streaming latency under different connector settings
 
 If this sounds familiar, it should.
 We already saw in earlier Debezium examples that notebooks can be useful for machine learning scenarios too.