Adding an asset for Fraud Detection with OCI Streaming with Apache Kafka

data-platform/open-source-data-platforms/oci-streaming-with-apache-kafka/code-examples/fraud-detection-demo/.gitattributes

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data-platform/open-source-data-platforms/oci-streaming-with-apache-kafka/code-examples/fraud-detection-demo/README.md

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+# Fraud Detection Kafka Demo
+
+This project demonstrates a real-time fraud detection pipeline using Kafka, OCI Object Storage, OCI Data Science model deployment and OCI Monitoring. A producer replays sample transaction data into Kafka, while a consumer scores each transaction with a deployed model, publishes detected fraud events, writes scored results to Object Storage and emits operational metrics.
+
+Two services are included:
+
+- `consumer`: consumes Kafka messages, scores them, publishes fraud events, writes scored CSV files to Object Storage and emits metrics.
+- `producer`: reads sample transactions from Object Storage and publishes them to Kafka.
+
+## Prerequisites
+
+- The default configuration file assumes you are working in /home/opc
+- Linux host with `systemd` available
+- Python 3
+- OCI CLI config available at `~/.oci/config`
+- Access to OCI Object Storage
+- Access to Kafka
+- A deployed OCI model endpoint that accepts the transaction fields used by this project
+
+## 1. Clone
+
+```bash
+git clone <repository-url>
+cd /path/to/fraud-detection-demo
+```
+
+## 2. Configure
+
+Create local config files:
+
+```bash
+cp producer/app_config.yaml.example producer/app_config.yaml
+cp producer/kafka_client.properties.example producer/kafka_client.properties
+cp consumer/app_config.yaml.example consumer/app_config.yaml
+cp consumer/kafka_client.properties.example consumer/kafka_client.properties
+```
+
+Edit the copied files with your OCI and Kafka values.
+
+In the consumer config, replace the model endpoint with your deployed OCI model URL:
+
+```yaml
+model:
+  endpoint: "https://<your-model-endpoint>/predict"
+```
+
+
+## 3. Kafka Topics
+
+Create the Kafka topics before starting the pipeline.
+
+The producer topic must match the consumer input topic.
+
+Example:
+
+- Producer topic: `sample-transactions`
+- Consumer input topic: `sample-transactions`
+- Consumer output topic: `fraud-scored-transactions`
+
+## 4. Install
+
+```bash
+sudo bash scripts/install_services.sh
+```
+
+The services run from this cloned repository folder.
+
+## 5. Start Services
+
+```bash
+bash scripts/start_services.sh
+```
+
+This starts the Flask services for the consumer and producer. It does not start processing messages yet.
+
+## 6. Start Pipeline
+
+```bash
+bash scripts/start_pipeline.sh
+```
+
+This starts the actual pipeline processing by calling the consumer and producer `/start` endpoints.
+
+Start the consumer before the producer so messages are consumed as they are produced. `start_pipeline.sh` does this order for you.
+
+## 7. Check Status
+
+```bash
+bash scripts/status_services.sh
+```
+
+Endpoints:
+
+```text
+Consumer: http://localhost:5001/status
+Producer: http://localhost:5000/status
+```
+
+## Logs
+
+```bash
+sudo journalctl -u fraud-producer -n 100 -l --no-pager
+sudo journalctl -u fraud-consumer -n 100 -l --no-pager
+```
+
+## Stop
+
+```bash
+bash scripts/stop_services.sh
+```