NetTraceArcade | Cybersecurity, AI & Cloud

Windows Server is still the backbone of a huge number of company systems. It powers Active Directory, SQL Server, .NET applications, file servers, remote work desktops and a long list of legacy business software that is not going away any time soon. The good news is that you no longer need to buy hardware, rack it in a server room or install an operating system from a disc to run it. On Google Cloud, you can have a Windows Server virtual machine up and running in a few minutes, and the Compute Engine: Qwik Start - Windows (GSP093) lab on Google Cloud Skills Boost is one of the fastest ways to learn how. The lab itself is short, usually around fifteen minutes. You create a Windows virtual machine, wait for it to finish booting, generate a password, and connect to its desktop using Remote Desktop Protocol (RDP). It sounds simple, but those few steps touch several ideas that every cloud beginner needs to understand: machine types, boot disks, regions and zones, firewall rules, serial...

Almost every project on Google Cloud ends up needing somewhere to put files. Images for a website, exported reports, database backups, log archives, training data for a model. The service Google built for this is Cloud Storage, and the Cloud Storage: Qwik Start lab (GSP073) on Google Cloud Skills Boost is the usual first contact with it. The lab itself is short. You create a bucket, upload a file, make that file public, and create a folder. It takes maybe fifteen minutes if nothing goes wrong. The reason it is worth writing about is that those few clicks hide a handful of decisions (bucket names, locations, storage classes, access control) that matter a lot once you build something real. This post covers the lab step by step and also explains what each setting actually does, so you are not just copying values from the instructions. The full video walkthrough is embedded below. Watch it once, then keep this page open as a reference while you do the lab yourself. Lab Ove...

AI agents become far more useful when they can work with real data. In this step-by-step tutorial, we build an AI agent using Google's Agent Development Kit (ADK) and Gemini that can securely access and analyze structured data in BigQuery through the BigQuery MCP Server , and then deploy it to Google Cloud Run . Watch the full walkthrough below, then use this post as a reference while you follow along. 📌 Introduction Large Language Models are powerful, but on their own they can't see your business data. To build agents that answer real questions about sales, customers, or operations, they need a safe way to connect to data sources and tools. This tutorial shows one way to do that: an ADK agent powered by Gemini uses the BigQuery MCP server to query structured data, and the whole application runs as a live service on Cloud Run. It is based on the Google Cloud codelab "Build and Deploy AI Agents with Gemini and BigQuery MCP server in Cloud Run." 🎯 Wha...

Looking for an easy way to complete the Cloud Storage: Qwik Start - CLI/SDK (GSP074) lab? In this Google Cloud Skills Boost tutorial, we use the Google Cloud CLI (Command Line Interface) to create a Cloud Storage bucket, upload objects, organize them into folders, and make them publicly accessible. Watch the full walkthrough below, then follow the steps in this post to complete the lab successfully. 📌 Introduction Cloud Storage is one of the most widely used services on Google Cloud. It lets you store and retrieve any amount of data, from images and documents to backups and application files. While you can manage it from the Google Cloud Console, using the command line is faster, repeatable, and a skill every cloud beginner should learn. This lab is an introductory exercise that teaches you the core Cloud Storage operations using the CLI. 🧪 Lab Details Lab Name: Cloud Storage: Qwik Start - CLI/SDK Lab ID: GSP074 Duration: 15 minutes Level: Introductory Cred...

In this hands-on Google Cloud Skills Boost lab, we build and deploy a customer-facing AI agent using the Google Agent Development Kit (ADK) , Retrieval-Augmented Generation (RAG) , and Cloud Run . The result is an intelligent coffee shop assistant that gives personalized recommendations by combining an AI agent with a knowledge base. The video below shows the full walkthrough, including the build and deployment errors I ran into along the way and how I fixed them. 📌 Introduction AI agents are quickly moving from demos to real customer-facing applications. Instead of a chatbot that only answers from its training data, an agent can reason, use tools, and pull in your own business information to give relevant answers. This lab walks through that idea end to end: build the agent locally, connect it to a knowledge base, and deploy it to Google Cloud Run so it runs as a live web service. 🎯 What You'll Learn Build an AI agent using Google ADK Understand how Retrieval-A...