Update section titles for consistency: change "What you've accomplished and what's next" to "What you've learned and what's next" in multiple documents.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/_index.md

@@ -1,13 +1,9 @@
 ---
 title: Deploy High-Performance Analytics with Apache Arrow and Arrow Flight on Google Cloud C4A Axion processors
 
-draft: true
-cascade:
-    draft: true
-    
 minutes_to_complete: 30
 
-who_is_this_for: This learning path is designed for data engineers, platform engineers, and developers who aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and Arrow Flight.
+who_is_this_for: This is an introductory topic for data engineers, platform engineers, and developers who aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and Arrow Flight.
 
 learning_objectives:
  - Deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors

content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/background.md

@@ -34,7 +34,7 @@ Common use cases include interactive analytics, data lake acceleration, machine
 
 To learn more, visit the [Apache Arrow website](https://arrow.apache.org/) and explore the [Arrow Flight documentation](https://arrow.apache.org/docs/format/Flight.html).
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 In this section, you learned about:
 

content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/columnar-analytics-with-arrow.md

@@ -226,7 +226,7 @@ In the arrow-data bucket after refreshing, you should now see:
 
 ![MinIO object browser showing dataset.orc stored inside the arrow-data bucket alt-txt#center](images/datset-orc.png "MinIO Web UI displaying dataset.orc object in arrow-data bucket")
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 In this section, you have:
 
@@ -238,10 +238,4 @@ In this section, you have:
 
 This forms the core analytics layer used by modern engines such as Spark, DuckDB, Trino, and Polars.
 
-## What's next
-
-In the next section, you will enable high-speed memory-to-memory analytics using Apache Arrow Flight, demonstrating:
-
-- gRPC-based data transfer
-- Zero-copy serialization
-- High-throughput analytics communication
+In the next section, you will enable high-speed memory-to-memory analytics using Apache Arrow Flight, demonstrating gRPC-based data transfer, zero-copy serialization, and high-throughput analytics communication.

content/learning-paths/servers-and-cloud-computing/apache_arrow_and_flight/environment-and-minio.md

@@ -217,13 +217,11 @@ AWS_ACCESS_KEY_ID=minioadmin
 
 to communicate with MinIO exactly like Amazon S3.
 
-## What you've accomplished and what's next
+## What you've learned and what's next
 
 - Prepared a SUSE arm64 analytics environment
 - Installed Apache Arrow and dependencies
 - Deployed MinIO as S3-compatible object storage
 - Configured secure access for analytics workloads
 
-## What's next
-
 In the next section, you will use Apache Arrow to write and read Parquet and ORC datasets from MinIO using vectorized analytics APIs.