RAD Deployment & Fraud Detection with Data Agent Kit

Welcome to this week’s dual-track hands-on build! Modern cloud engineering demands both rapid application delivery and intelligent data workflows. In this session, we tackle both sides of the modern stack: first by exploring RAD (Rapid Application Deployment) and its free hands-on training tracks to launch full-stack tools on Google Cloud, and second by engineering an end-to-end Fraud Detection Pipeline using Google's Data Agent Kit inside the Antigravity IDE.

Whether you want to deploy enterprise cloud modules without boilerplates or orchestrate complex, agentic data investigations across transactional and analytical engines, this session delivers practical, production-grade skills.

🏗️ Today’s Technical Roadmap

We will step through two core practical tracks:

1. Track 1: Rapid Application Deployment (RAD) & Cloud Training:

   • Introduction to the RAD Portal: Learn how to deploy production-ready cloud architectures to Google Cloud in minutes using automated, modular configurations.

   • Serverless & Scalable Deployments: Discover how RAD provisions managed infrastructure across Cloud Run, GKE Autopilot, Cloud SQL, and Secret Manager without writing complex IaC scripts from scratch.

   • Hands-On Certification Training: Get oriented with the free RAD training modules mapped directly to Google Cloud certification tracks (Associate Cloud Engineer, Cloud Architect, and Security Engineer).

2. Track 2: Fraud Detection Pipeline with Data Agent Kit & Antigravity IDE:

   • Unified Data Discovery: Map and catalog payment logs, customer entities, and historical chargebacks across BigQuery, Cloud SQL, and Cloud Storage using the Dataplex Knowledge Catalog.

   • Federated Investigations via MCP: Query and correlate transactional records across distributed storage backends in a single conversation using Model Context Protocol (MCP) tools.

   • dbt Pipelines & Feature Engineering: Scaffold a modular dbt project to compute transaction velocity metrics, handle deduplication, and execute automated schema tests.

   • Self-Healing SQL & In-Engine Scoring: Watch the agent diagnose join fan-outs and logic bugs in real time, then deploy native BigQuery ML anomaly detection models to flag suspicious transactions.

🌟 Why This Session is Critical

• Zero-Boilerplate Cloud Delivery: Master RAD to launch scalable applications, analytics dashboards, and open-source infrastructure onto GCP in record time.

• Agentic Data Engineering: Learn how agentic frameworks and MCP tooling coordinate multi-service investigations without leaving your local developer workspace.

• Production-Grade Data Quality: Build robust, self-healing dbt pipelines backed by automated tests to maintain clean data feeds for financial risk systems.

• Dual Portfolio Assets: Walk away with an active deployment on Google Cloud via RAD and a complete, GitHub-ready fraud detection repository powered by BigQuery ML.

📝 What You Need to Bring

• Your Laptop (fully charged).

• Antigravity IDE Installed: Configured in your local development environment.

• A Google Cloud Project: With billing enabled to access Cloud Run, BigQuery, Cloud Storage, and Dataplex.

• A GitHub Account: Ready to commit and version-control your deployment templates and dbt models.

📚 Essential Developer Resources

• RAD Platform & Docs: Rapid Application Deployment Portal

• BigQuery ML Documentation: Detecting Anomalies with BigQuery ML

• Dataplex Knowledge Catalog: Data Discovery & Governance on Google Cloud

• dbt & BigQuery: Transforming and Testing Data in BigQuery with dbt