use-case-deployment

name	use-case-deployment
description	End-to-end orchestration and deployment runner for all solution guides in this repository. Use when deploying, running, ingesting test data into, or validating any of the 8 solution guides: GenAI & ML (Gemma 4 with vLLM on GPU), ETL & Integration (Spanner CDC), Customer Data Platform (CDP), Anomaly Detection, Marketing Intelligence, Clickstream Analytics, IoT Analytics, or Log Replication.

Use Case Deployment Skill

This skill provides step-by-step execution workflows for deploying, running, verifying, and safely tearing down each of the 8 solution guides in this repository.

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1. Solution Guide Matrix

Guide Name	Terraform Directory	Pipeline Directory	Launch Command / Script	Input Generator / Test Data	Output Validation Target
GenAI & ML	terraform/ml_ai	pipelines/ml_ai_python	./scripts/02_run_dataflow.sh	Pub/Sub messages topic	Pub/Sub predictions-sub subscription
ETL & Integration	terraform/etl_integration	pipelines/etl_integration_java	./scripts/02_run_publisher_dataflow.sh & ./scripts/03_run_changestream_template.sh	Pub/Sub Taxirides feed	Cloud Spanner events table & BigQuery replica.events_changelog
Customer Data Platform (CDP)	terraform/cdp	pipelines/cdp	./scripts/02_run_dataflow_job.sh	python cdp_pipeline/generate_transaction_data.py	BigQuery output_dataset.unified-table
Anomaly Detection	terraform/anomaly_detection	pipelines/anomaly_detection	./scripts/02_run_dataflow.sh	Pub/Sub anomaly-detection-transactions topic	Pub/Sub anomaly-detection-detections, BigQuery anomaly_detection.detections, errors anomaly-detection-errors
Marketing Intelligence	terraform/marketing_intelligence	pipelines/marketing_intelligence	./scripts/02_run_dataflow.sh	Pub/Sub user activity stream	BigQuery marketing attribution tables
Clickstream Analytics	terraform/clickstream_analytics	pipelines/clickstream_analytics_java	./scripts/01_launch_pipeline.sh	Pub/Sub events	Cloud Bigtable & BigQuery analytics table
IoT Analytics	terraform/iot_analytics	pipelines/iot_analytics	./scripts/02_submit_job.sh	python scripts/publish_on_pubsub.py	BigQuery iot.maintenance_analytics & Pub/Sub maintenance-alerts
Log Replication	terraform/log_replication_splunk	pipelines/log_replication_splunk	./scripts/01_launch_ps_to_splunk.sh	Pub/Sub logging topic	Splunk HTTP Event Collector (HEC)

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2. Pre-Flight Checks & Best Practices

Before deploying infrastructure or submitting Dataflow jobs:

1. Service Account Collision Check: Check if the default service account name already exists in the project:

   gcloud iam service-accounts list --project=$PROJECT --filter="email:<sa-name>"
   

   If a collision exists, set a custom service_account_name = "<custom-name>" in terraform.tfvars.

2. Shared VPC / Subnetwork Verification: If running workers in a Shared VPC, verify that the full subnetwork URI is configured: https://www.googleapis.com/compute/v1/projects/HOST_PROJECT/regions/REGION/subnetworks/SUBNET_NAME

3. GCS Staging Bucket: Ensure the staging bucket (gs://BUCKET_NAME/tmp) exists in the same region as the Dataflow workers or set create_bucket = true in terraform.tfvars.

4. Java Pipeline Toolchains: For Java pipelines (etl_integration_java, clickstream_analytics_java), ensure build.gradle specifies JavaLanguageVersion.of(25).

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3. End-to-End Execution Workflows

1. GenAI & ML (Gemma 4 with vLLM on GPU)

# 1. Terraform
cd terraform/ml_ai
terraform init && terraform apply -auto-approve

# 2. Upload Gemma Model to GCS
gcloud storage cp -r LOCAL_GEMMA_PATH gs://$PROJECT/gemma_2B

# 3. Build Container & Launch Pipeline
cd ../../pipelines/ml_ai_python
source scripts/00_set_environment.sh
./scripts/01_build_and_push_container.sh
./scripts/02_run_dataflow.sh

# 4. Ingest & Validate
gcloud pubsub topics publish messages --message='{"prompt": "What is Google Cloud Dataflow?"}'
gcloud pubsub subscriptions pull predictions-sub --auto-ack --limit=1

2. ETL & Spanner Change Data Capture (Java)

# 1. Terraform
cd terraform/etl_integration
terraform init && terraform apply -auto-approve

# 2. Launch Spanner Ingestion & Change Stream Flex Template
cd ../../pipelines/etl_integration_java
source scripts/01_set_variables.sh
./scripts/02_run_publisher_dataflow.sh
./scripts/03_run_changestream_template.sh

# 3. Validate CDC output in Spanner & BigQuery
gcloud spanner databases execute-sql taxis_database --instance=test-spanner-instance --sql='SELECT COUNT(*) FROM events'
bq query --use_legacy_sql=false 'SELECT COUNT(*) FROM replica.events_changelog'

3. Customer Data Platform (CDP)

# 1. Terraform
cd terraform/cdp
terraform init && terraform apply -auto-approve

# 2. Build Container & Launch Dataflow
cd ../../pipelines/cdp
source scripts/00_set_variables.sh
./scripts/01_cloudbuild_and_push_container.sh
./scripts/02_run_dataflow_job.sh

# 3. Generate Streaming Transactions
python3 ./cdp_pipeline/generate_transaction_data.py

# 4. Validate Unified BigQuery Table
bq query --use_legacy_sql=false 'SELECT * FROM output_dataset.`unified-table` LIMIT 10'

4. Clickstream Analytics with Bigtable (Java)

# 1. Terraform
cd terraform/clickstream_analytics
terraform init && terraform apply -auto-approve

# 2. Launch Java Pipeline
cd ../../pipelines/clickstream_analytics_java
source scripts/00_set_variables.sh
./scripts/01_launch_pipeline.sh

5. Anomaly Detection with Vertex AI

Follow the executable guide from the repository root. Use an existing project/network and an existing bucket by default. Python 3.14 is required across all components: Dataflow pipeline, local tooling, custom training container, and custom prediction serving container.

After provisioning Terraform, source scripts/00_set_variables.sh in pipelines/anomaly_detection. Build worker, training, and serving images through the provided Cloud Build scripts (scripts/01_build_and_push_container.sh, scripts/01_build_training_container.sh, scripts/01_build_serving_container.sh), then source their environment files (.deployment/training_environment.sh, .deployment/serving_environment.sh). Run python -m anomaly_detection_pipeline.workflow stages train, validate and deploy; source the separate scripts/03_endpoint_environment.sh, then verify, seed, launch scripts/02_run_dataflow.sh and run smoke --count 20 --timeout 600. The guide contains the exact commands, identities, quotas, and non-Terraform cleanup procedures.

Keep the ignored deployment manifest to resume partial runs and clean up only owned resources. Ambiguous creates must be reconciled before retrying. Compatible external MODEL_ENDPOINT values are supported with endpoint-level worker prediction access (roles/anomalyDetectionPredictor); never adopt or delete external endpoints. Workers use private IPs and CPU n1-standard-2. Bigtable customer profiles (customer_profiles) and BigQuery archival (anomaly_detection.detections) are active parts of the graph. Stop Dataflow, run workflow cleanup, execute any required manual resource deletions, then destroy Terraform. Report local/container tests separately from live-cloud smoke results.

6. Marketing Intelligence

# 1. Terraform
cd terraform/marketing_intelligence
terraform init && terraform apply -auto-approve

# 2. Launch Pipeline
cd ../../pipelines/marketing_intelligence
source scripts/00_set_environment.sh
./scripts/02_run_dataflow.sh

7. IoT Analytics

# 1. Terraform
cd terraform/iot_analytics
terraform init && terraform apply -auto-approve

# 2. Build Container & Seed Metadata
cd ../../pipelines/iot_analytics
source scripts/00_set_environment.sh
./scripts/01_cloud_build_and_push.sh
python scripts/create_and_populate_bigtable.py

# 3. Launch Pipeline & Simulator
./scripts/02_submit_job.sh
python scripts/publish_on_pubsub.py

8. Log Replication into Splunk

# 1. Terraform
cd terraform/log_replication_splunk
terraform init && terraform apply -auto-approve

# 2. Launch Dataflow Pipeline
cd ../../pipelines/log_replication_splunk
source scripts/00_set_variables.sh
./scripts/01_launch_ps_to_splunk.sh

# 3. Optional: Access Demo Splunk Web UI (Port 8501 via IAP Tunnel)
# If deploy_demo_splunk = true:
gcloud compute start-iap-tunnel $SPLUNK_DEMO_INSTANCE 8000 --local-host-port=localhost:8501 --zone=$ZONE --project=$PROJECT

# 4. Ingest Test Log Event & Verify
gcloud pubsub topics publish splunk-logs --message='{"message": "Test replication log", "severity": "INFO"}'

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4. Post-Deployment Observability Checklist

1. Job Status Check:

   gcloud dataflow jobs list --project=$PROJECT --region=$REGION --status=active --limit=10 --format="table(id, name, type, state)"
   

2. Worker Log Inspection:

   gcloud logging read 'resource.labels.job_id="<JOB_ID>" severity>=ERROR' --project=$PROJECT --limit=20
   

3. Data Freshness / Backlog: Check the System Lag and Data Freshness metrics in the Cloud Monitoring / Dataflow UI.

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5. Safe Teardown & Resource Cleanup

When cleaning up or concluding test deployments, always follow this order:

1. Stop / Cancel Active Dataflow Jobs:

   gcloud dataflow jobs cancel <JOB_ID_1> <JOB_ID_2> --project=$PROJECT --region=$REGION
   

2. Wait for Job Cancellation: Ensure jobs have reached Cancelled or Drained state before destroying infrastructure:

   gcloud dataflow jobs list --project=$PROJECT --region=$REGION --status=active
   

3. Run Terraform Destroy:

   cd terraform/<use_case>
   terraform destroy -auto-approve
   

4. Verify Clean Teardown:

   terraform state list