537 use confluent cloud apache flink to handle avro flight consolidator app

README.md

@@ -28,8 +28,9 @@ Explore the repo, run the Docker containers (compatible with both Mac M chips an
         + [2.2.1 Run from the cloud](#221-run-from-the-cloud)
     - [2.3 Visualizing the Terraform Configuration](#23-visualizing-the-terraform-configuration)
 + [3.0 Hands-On Kickoff: Practical Examples for Rapid Learning](#30-hands-on-kickoff-practical-examples-for-rapid-learning)
-    - [3.1 Flink Applications Powered by Java](#31-flink-applications-powered-by-java)
-    - [3.2 Flink Applications Powered by Python](#32-flink-applications-powered-by-python)
+    - [3.1 Flink Applications Powered by Java on a locally running Apache Flink Cluster in Docker](#31-flink-applications-powered-by-java-on-a-locally-running-apache-flink-cluster-in-docker)
+    - [3.2 Flink Applications Powered by Python on a locally running Apache Flink Cluster in Docker](#32-flink-applications-powered-by-python-on-a-locally-running-apache-flink-cluster-in-docker)
+    - [3.3 Flink Applications Powered by Python on Confluent Cloud Apache Flink](#33-flink-applications-powered-by-python-on-confluent-cloud-apache-flink)
 + [4.0 Powering Analytics with Apache Iceberg tables in Snowflake](#40-powering-analytics-with-apache-iceberg-tables-in-snowflake)
 + [5.0 Resources](#50-resources)
 + [6.0 Important Note(s)](#60-important-notes)
@@ -167,12 +168,15 @@ terraform graph | dot -Tpng > .blog/images/terraform-visualization.png
 
 ## 3.0 Hands-On Kickoff: Practical Examples for Rapid Learning
 
-### 3.1 Flink Applications Powered by Java
+### 3.1 Flink Applications Powered by Java on a locally running Apache Flink Cluster in Docker
 [Let's go!](java/README.md)
 
-### 3.2 Flink Applications Powered by Python
+### 3.2 Flink Applications Powered by Python on a locally running Apache Flink Cluster in Docker
 [Let's go!](python/README.md)
 
+### 3.3 Flink Applications Powered by Python on Confluent Cloud Apache Flink
+[Let's go!](python_ccaf/README.md)
+
 ## 4.0 Powering Analytics with Apache Iceberg tables in Snowflake
 After running the Flink applications, you can now leverage the data stored in the Apache Iceberg tables in Snowflake for analytics.  Click [here](.blog/using-non-managed-apache-iceberg-tables-in-snowflake.md) to learn more about using **Non-Managed Apache Iceberg tables** in Snowflake.  This blog post will help you understand the trade-offs, operational characteristics, and integration patterns of using Apache Iceberg tables in Snowflake.  These terraform configurations:
 

java/README.md

@@ -1,4 +1,4 @@
-# Flink Applications Powered by Java
+# Flink Applications Powered by Java on a locally running Apache Flink Cluster in Docker
 Discover how Apache Flink® can transform your data pipelines! Explore hands-on examples of Flink applications using the [DataStream API](https://nightlies.apache.org/flink/flink-docs-master/docs/learn-flink/datastream_api/) and [Table API](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/overview/) in Java. You'll see how these technologies integrate seamlessly with AWS Services, Apache Kafka, and  Apache Iceberg.
 
 Curious about the differences between the DataStream API and Table API? Click [here](../.blog/datastream-vs-table-api.md) to learn more and find the best fit for your next project.

python/README.md

@@ -1,4 +1,4 @@
-# Flink Applications Powered by Python
+# Flink Applications Powered by Python on a locally running Apache Flink Cluster in Docker
 Discover how Apache Flink® can transform your data pipelines! Explore hands-on examples of Flink applications using the [DataStream API](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/python/datastream/intro_to_datastream_api/), [Table API](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/python/table/intro_to_table_api/), and [Flink SQL](https://nightlies.apache.org/flink/flink-docs-release-1.19/docs/dev/table/sql/overview/#sql)—all built in Python with PyFlink, which compiles these apps to Java. You'll see how these technologies integrate seamlessly with AWS Services, Apache Kafka, and  Apache Iceberg.
 
 Curious about the differences between the DataStream API and Table API? Click [here](../.blog/datastream-vs-table-api.md) to learn more and find the best fit for your next project.

python/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "kickstarter"
-version = "0.54.00.000"
+version = "0.55.00.000"
 description = "Apache Flink Kickstarter"
 readme = "README.md"
 requires-python = "~=3.11.9"

python_ccaf/.gitignore

@@ -0,0 +1,6 @@
+.venv/
+.DS_Store
+__pycache__
+.pytest_cache
+python_files.zip
+tf_snowflake_user

python_ccaf/.python-version

@@ -0,0 +1 @@
+3.11.9

python_ccaf/README.md

@@ -0,0 +1 @@
+# Flink Applications Powered by Python on Confluent Cloud Apache Flink

python_ccaf/pyproject.toml

@@ -0,0 +1,7 @@
+[project]
+name = "kickstarter"
+version = "0.56.00.000"
+description = "Confluent Cloud Apache Flink Kickstarter"
+readme = "README.md"
+requires-python = "~=3.11.9"
+dependencies = []