DEV Community: luis zuñiga

🚀 Beyond the HCL: Trench Lessons from Deploying Critical Architectures on GCP with Terraform

luis zuñiga — Wed, 06 May 2026 21:54:39 +0000

The "Bunker" vs. Resilience: Scaling Windows Server Without the Burnout
By: Luis Alonso Zuñiga Carballo
Cloud Architect & Security Strategist

💣 The Challenge: The Problem That Kept Me Up at Night
Imagine this: You are tasked with deploying a critical-tier enterprise infrastructure on Google Cloud Platform (GCP). It’s not just about "spinning up VMs"; it’s about orchestrating an environment that supports Windows Server applications, ensures hybrid connectivity with on-premises offices, and—most importantly—doesn't break when traffic spikes or a node fails.

The true challenge was transforming a "functional bunker" into a High Availability Hybrid Architecture that was 100% reproducible and transparent for the stakeholder.

🏗️ The Strategy: Operational Symmetry in Action
For this deployment, I followed a three-stage validation workflow that ensures what is designed is exactly what is deployed:

Phase 1: Architectural Blueprint (ASCII): Before writing a single line of code, I mapped the entire logic using ASCII diagrams. This provided immediate clarity on traffic flow and subnet isolation without the distraction of complex tooling.

Phase 2: Infrastructure as Code (Terraform): Once the logic was solidified, I translated the ASCII blueprint into Terraform HCL. This allowed for the consistent deployment of 66 resources across multiple regions.

Phase 3: Stakeholder Visibility (PNG): Finally, I generated a high-fidelity PNG diagram based on the actual deployment. This served as the final "source of truth" to share with the client, providing full visibility into the security layers and hybrid connectivity established.

🛡️ Key Architectural Pillars

🌐 Global Networking
We utilized a custom VPC with Global routing mode to simplify BGP propagation across regions (us-east1 and us-east4).
🛡️ Layer 7 Shielding
We implemented Cloud Armor (WAF) and Identity-Aware Proxy (IAP). This eliminated public IPs for administration, allowing RDP access only through encrypted tunnels.
💾 Dual-Region Resilience
For critical backups, the standard was Dual-Region Cloud Storage, ensuring data survivability even in the event of a regional outage.

🛠️ The Hard Way: Lessons Learned from the Field
⚠️ The Quota Ghost: Never assume instance families are ready. Requesting vCPU quota increases in GCP can take at least one week.

🔌 The Routing "Trap": After establishing the IPsec tunnel, dynamic propagation often needs a manual nudge within the VPC Route Tables to ensure the Cloud Router is advertising correctly.

🤖 The Antigravity Factor: Using AI as a "copilot" to accelerate HCL generation is a force multiplier, but it requires human-in-the-loop auditing to maintain the Principle of Least Privilege (IAM).

💰 Business Value: Why Does This Matter?
This triple-stage workflow (ASCII → Terraform → PNG) isn't just about technical tidiness; it’s about Risk Mitigation:

Transparency: The client sees exactly what they are paying for.

Agility: We reduced deployment time from days to minutes through modular IaC.

Compliance: By following applied industry scenarios, we ensure that internal corporate procedures remain protected while delivering world-class security.

🏁 Call to Action
What is your preferred workflow for bridging the gap between a conceptual sketch and a production-ready environment? Let’s discuss in the comments! 👇

googlecloud #terraform #gcpcommunity #devops #cloudsecurity #iac #hybridcloud

⚖️ Technical & Legal Safe Harbor Disclaimer

AUTHORSHIP AND INDEPENDENT CAPACITY: This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.

INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE:

Independent Development: The workflows described are based on general industry best practices and were not developed as a "work for hire."

LIMITATION OF LIABILITY: All technical info is provided "AS IS" without warranty. The author shall not be liable for any claim arising from the use of this information.

COMPLIANCE: This contribution is made in good faith and adheres to global technical community standards.

🚀 Bridge to the Cloud: A Tactical Guide to Hybrid Resilience with Nutanix NC2 on AWS

luis zuñiga — Mon, 04 May 2026 17:31:21 +0000

The Challenge: Beyond the "Lift and Shift" Fatigue
In the Latin American market, many organizations face what I call the hybrid paradox: they want the elasticity and speed of AWS, but remain tightly bound to on-premises legacy workloads running on vCenter, VMware, or Hyper-V.

The real fear isn’t migration itself—it’s operational fragmentation: different tools, different processes, and different failure modes between the data center and the cloud. After deep-diving into the Nutanix ecosystem, I realized that the goal shouldn't be just moving VMs, but achieving operational symmetry. This is where Nutanix Cloud Clusters (NC2) on AWS becomes a game-changer.

🏗️ A Proven, Production-Grade Hybrid Workflow
This strategy focuses on technical execution and infrastructure integrity, moving away from commercial "fluff" to focus on what actually works in production.

The "Pre-Flight" Assessment: Behavior, Not Just Numbers Before touching the AWS Console, you need to know exactly what you’re moving. I recommend using the Nutanix Move module or RVTools to obtain a realistic view of RAM, vCPU, and disk density.

Field Tip: Don’t just analyze resource totals—analyze workload behavior. Use this data on the Nutanix Projects portal to estimate the exact dedicated host type and licensing required in AWS.

The Quota Battle: Lessons from the Field One common "battle scar" in this workflow is assuming that dedicated hosts are ready for you. They aren't.

The Action: You must open an AWS Support Case to request a service quota increase for the specific instance family (e.g., i3 or i4i metal).

The Lead Time: This process can take at least one week. Once approved, launch a single EC2 of that type manually to ensure availability before initiating the Nutanix deployment.

Secure Identity and Access Management (IAM) For the NC2 manager to deploy resources, it needs "eyes" in your account. Treat the Nutanix Manager as a third-party control plane.

Security Best Practice: Create a dedicated IAM user (e.g., User-Nutanix-NC2) with a third-party policy. Use the principle of Least Privilege by scoping the keys only to the Nutanix Manager.

The "Nervous System": Connectivity & Routing The magic happens when Prism (Nutanix’s management plane) sees AWS as just another cluster.

The Hybrid Link: Configure an AWS Site-to-Site VPN between your on-premises edge and the AWS VPC.

The Routing Trap: After creating the NC2 cluster in AWS, it is normal for environments not to communicate cross-site immediately. You often need to manually adjust the VPC Route Tables to point the on-premises CIDR to the Virtual Private Gateway.

💰 ROI: Standardizing Operations
Implementing NC2 allows for a Disaster Recovery Plan (DRP) with significantly lower RTOs. You’re not just renting host capacity—you’re standardizing operations across failure domains through a single pane of glass.

🛠️ Hands-On Resources for Practitioners

Marketplace: NC2 on AWS Marketplace

Technical Guide: Nutanix Test Drive for NC2

Certification Path: Nutanix University - AWS Administration

⚖️ Technical & Legal Safe Harbor Disclaimer
AUTHORSHIP AND INDEPENDENT CAPACITY: This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.

INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE:

Zero Proprietary Disclosure: This content has been developed using publicly available information and personal research. No confidential information or trade secrets belonging to my employer have been disclosed.

Independent Development: The workflows described (applied industry scenarios and hands-on experimentation) are based on general industry best practices and were not developed as a "work for hire".

LIMITATION OF LIABILITY: All technical info is provided "AS IS" without warranty. The author shall not be liable for any claim arising from the use of this information. COMPLIANCE: This contribution is made in good faith under the AWS Builder Terms and the MIT-0 License.

What has been your biggest friction point with hybrid AWS deployments—host quotas, networking latency, or operational tooling? I’m curious to hear what’s breaking (or finally working) in the field. 👇

aws #nutanix #hybridcloud #nc2 #devops #cloudcomputing #infrastructure #awsbuilders

🚀 Elevating the Standard: From ASCII Diagrams to Visual Architectures with Kiro 101 and AWS MCP

luis zuñiga — Mon, 27 Apr 2026 20:33:57 +0000

In the world of Cloud Engineering, precision is everything. Recently, I migrated my virtual machine environment to a more cost-effective setup with higher performance and exclusive resource monitoring. This transition has allowed me to work with greater agility and control.

After installing the Kiro IDE and CLI on the latest version of Ubuntu, my workflow underwent a significant transformation. Today, I want to share this approach as an example of what I consider a "possible professional workflow" applicable to cloud architecture.

🆙 The Leap to “Kiro 101”: Precision with AWS MCP
By configuring the Kiro IDE and loading the official AWS MCP (Model Context Protocol), the environment gains deep visibility into cloud services. At this stage, I call it moving from a standard environment to “Kiro 101.”

This level enables key capabilities:

🔍 Full Visibility: The IDE recognizes actual AWS service names, APIs, and resource structures.

📄 Intelligent Documentation: Generating READMEs, runbooks, and playbooks for deployment and troubleshooting becomes highly precise and aligned with the cloud provider's reality.

🏗️ Visualization: The Language of Architects
One of the greatest challenges in any repository or community contribution is generating visual impact. While ASCII diagrams are extremely useful for their simplicity and portability, many scenarios demand the formality and standardization offered by official AWS icons.

📝 Step 1: The ASCII Skeleton
To quickly validate the architectural logic, I use the following prompt in Kiro:

"Generate an AWS architecture ASCII diagram: VPC with 1 EC2 in a public subnet and an Internet Gateway. Use boxes with ├┤┌┐└┘─│. Show CIDR blocks. Connect EC2 → IGW → Internet."

┌─────────────────────────────────────┐
│              AWS Cloud              │
│                                     │
│  ┌───────────────────────────────┐  │
│  │         VPC 10.0.0.0/16      │  │
│  │                               │  │
│  │  ┌─────────────────────────┐  │  │
│  │  │   Public Subnet         │  │  │
│  │  │   10.0.1.0/24           │  │  │
│  │  │                         │  │  │
│  │  │      ┌──────────┐      │  │  │
│  │  │      │   EC2    │      │  │  │
│  │  │      └──────────┘      │  │  │
│  │  │                         │  │  │
│  │  └─────────────────────────┘  │  │
│  │              │                 │  │
│  │     ┌────────┴────────┐       │  │
│  │     │ Internet Gateway│       │  │
│  │     └────────┬────────┘       │  │
│  └──────────────┼────────────────┘  │
│                 │                    │
└─────────────────┼────────────────────┘
                  │
              Internet

This approach allows for a rapid review of relationships, traffic flows, and network boundaries before investing time in more elaborate visual diagrams.

🎨 Step 2: PNG Magic with Official Icons
Using the Kiro CLI, it is possible to transform that logic into a professional diagram using the Python diagrams library—a gold standard for cloud architecture documentation.

💻 Prompt for Visual Generation:
"Generate a PNG diagram of an AWS architecture using official icons and the Python 'diagrams' library (pip install diagrams). Requires pre-installation of: Python 3, pip, and Graphviz (apt install graphviz on Ubuntu/Debian).

Architecture:

VPC 10.0.0.0/16

1 Public Subnet 10.0.1.0/24

1 EC2 in the public subnet

Internet Gateway connected to the EC2

Use Cluster for VPC and Subnet. Diagram direction: TB (top-bottom). Save the result as vpc-diagram.png."

The result is a professional diagram ready for technical documentation, architectural proposals, or design reviews.

🎯 Conclusion: Understand to Deploy
This hybrid approach enables the creation of robust proposals, even starting from initial estimates defined in JSON files or incomplete configurations. The ability to build diagrams incrementally or with granular detail makes it easier to identify missing values before executing a live deployment.

In short: With this workflow, it is much easier to visually grasp what is actually being built before moving it into production.

⚖️ Technical & Legal Safe Harbor Disclaimer
AUTHORSHIP AND INDEPENDENT CAPACITY
This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy, position, or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.

INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE

Zero Proprietary Disclosure: This content has been developed using publicly available information and personal research. No confidential information or proprietary source code has been disclosed.

Independent Development: The workflows described are based on general industry best practices and personal experimentation. They were not developed as a "work-for-hire" nor as part of assigned organizational duties.

LIMITATION OF LIABILITY (NO WARRANTY)
All code snippets and architectural patterns are provided “AS IS”, without warranty of any kind. The author shall not be held liable for any claim arising from the use of this information.

COMPLIANCE
This contribution is shared in good faith under the AWS Builder Terms and the MIT-0 License for any included source code.

Actionable Packages — paqueteAction: AWS Account Hardening Playbook

luis zuñiga — Sat, 25 Apr 2026 05:07:24 +0000

🌟 The Core Concept

paqueteAction is a high-performance CloudFormation suite designed to automate AWS hardening. Covering Identity Center, Security Hub, GuardDuty, and centralized logging, it ensures every account starts with a rock-solid security baseline.

[!TIP]
Each template is validated via cfn-lint and checkov to ensure compliance before deployment.

🛑 1. The Problem

Provisioning a "production-ready" AWS account manually is a nightmare:

🐌 Slow: Days of clicking through the AWS Console.
⚠️ Error-prone: High risk of human misconfiguration.
📉 Misaligned: Configurations often drift from the Security Pillar of the AWS Well-Architected Framework.

🛠️ 2. The Solution: `paqueteAction`

This modular playbook streamlines 16 templates into three strategic pillars:

🔐 Identity + Networking: Identity Center (SSO), MFA enforcement, VPC, and Transit Gateway.
🛡️ Advanced Security: Security Hub (CIS/FSBP), AWS Config, Macie, and Inspector.
📊 Logging: Immutable CloudTrail (KMS/Object Lock) and VPC Flow Logs in Parquet format.

⚙️ 3. Workflow: Validated IaC with Kiro

I leverage Kiro CLI as an AI co-pilot to maintain a rigorous validation gauntlet:

✨ Creation: Kiro generates YAML based on security-first prompts.
🔍 Linting: bash cfn-lint template.yaml shell
🛡️ Security Scanning:
```
checkov -f template.yaml
```
🧠 Review: Kiro identifies complex logic gaps, such as over-permissive IAM roles in Lambda Custom Resources.

💡 Note: Kiro complements (but does not replace) human review. Engineering oversight remains the final filter.

🔍 4. Deep Dive: Key Security Patterns

🔒 Immutable Logs: CloudTrail is backed by S3 Object Lock (Compliance Mode). Logs cannot be deleted—even by the root user—during the retention period.
💰 Cost-Effective Analytics: VPC Flow Logs are stored in Parquet. This makes Athena queries 10x faster and significantly cheaper than text formats.
📐 Least-Privilege: Zero "star-policies". All IAM roles are strictly scoped to specific API actions.

📊 5. Impact: Before vs. After

Metric	📝 Manual Setup	🚀 With paqueteAction
Time per service	2-3 Days	15-30 Minutes
Validation	Visual / None	cfn-lint + checkov + Kiro
Consistency	Low (Manual Drift)	High (Reproducible IaC)

🎯 6. Conclusions

Security must be Validated Code. By combining the power of CloudFormation with automated linting and AI-assisted reviews, paqueteAction transforms account hardening from a manual chore into a reliable, Well-Architected process.

#AWSCommunityBuilders #SecurityAsCode #WellArchitected #CloudFormation #Kiro

⚖️ Legal Disclaimer

AUTHORSHIP: Authored in my private capacity. Views are my own.
COMPLIANCE: Developed using public info. No proprietary code disclosed.
LICENSE: Provided "AS IS" under the MIT-0 License.

🛠️ From Foundations to Advanced SecOps: The GCP Developer’s Toolkit 🛡️

luis zuñiga — Fri, 24 Apr 2026 01:42:03 +0000

Hey GCP Community,

As developers and cloud engineers, we often start our journey learning how to deploy a VM or configure a bucket. But in today's landscape, "working code" isn't enough. We need "secure-by-design" architectures.

I’ve been documenting my journey and best practices in my latest project: GCP-ToolKit101. While the toolkit starts with the essentials, the goal is to bridge the gap between basic infrastructure and elite security operations using Chronicle, Mandiant, and SCC.

Here is how we evolve from 101 basics to enterprise-grade security:

⚡ Scaling Beyond Logs: Google Chronicle Once you master VPC Flow Logs in the "101" stage, the next level is Chronicle. It’s not just about storing logs; it's about sub-second detection using YARA-L.

Real-world case: Detecting Insider Threats in financial systems by correlating VPN access with unusual BigQuery exports.

🧠 Intelligence-Driven Code: Mandiant Advantage Security isn't just about blocking IPs; it's about knowing who is attacking. Integrating Mandiant APIs into your automated workflows allows you to:

Proactively block spear-phishing domains before they hit your users.

Prioritize vulnerabilities based on what actual APT groups are exploiting right now.

🏯 Total Posture: Security Command Center (SCC) Your infrastructure is only as strong as its weakest configuration. SCC acts as the "Command Tower" for:

Identity Leakage: Detecting when service account keys are accidentally pushed to public repos.

Compliance: Real-time monitoring of PCI-DSS or CIS benchmarks.

🚀 The Roadmap: GCP-ToolKit101
I created GCP-ToolKit101 to be a living resource. It’s the starting point for developers who want to master the Google Cloud ecosystem with a professional edge.

What you’ll find in the repo:

🏗️ Core Infrastructure: Clean, reusable patterns for GCP deployments.

🔒 Security First: Standardized configurations to harden your cloud environment.

📈 Evolution: I am currently integrating advanced SecOps modules, including YARA-L rule templates for Chronicle and automation scripts for SCC.

🛠️ Join the Journey
If you are a developer looking to bridge the gap between "it works" and "it's secure," this toolkit is being built for you. I’m sharing everything I learn while building B2B solutions for the Latam market.

👉 Check out the repo, drop a ⭐, and let’s build more secure cloud environments together:
https://github.com/luiszuniga1990/GCP-ToolKit101.git

GCP #GCPDev #CloudSecurity #DevSecOps #GoogleCloud #Mandiant #Chronicle #CompanyOfOne #TechCommunity

🚀 Building an Intelligent Document Processing System with AI on AWS: From YAML to Production with Kiro

luis zuñiga — Thu, 23 Apr 2026 23:58:48 +0000

📌 Executive Summary (TL;DR)
We designed, implemented, and deployed a fully serverless intelligent document processing system leveraging Amazon Bedrock, Textract, Lambda, SQS, and OpenSearch Serverless. The entire ecosystem was orchestrated through CloudFormation in a three-tier architecture and developed using Kiro as an AI-driven development co-pilot.

🏗️ The Challenge: Automation at Scale The primary objective was to automate the lifecycle of thousands of documents. This required a system capable of:

Autonomous Classification 📂

High-precision OCR Extraction 🔍

Business Rule Validation ✅

Data Persistence 💾

Key Requirements:

Asynchronous Architecture: Decoupled via SQS for durability.

Cognitive Intelligence: Next-gen Generative AI reasoning.

Enterprise Security: VPC isolation, WAF, and least-privilege IAM.

🗺️ The Architecture: 3 Stacks, 0 Servers To reduce the blast radius, we modularized the infrastructure into three independent CloudFormation stacks:

Stage 1: Networking & Database Foundation 🌐
VPC with multi-AZ private subnets.

RDS MySQL + RDS Proxy for efficient connection pooling.

VPC Endpoints (Interface & Gateway) to keep traffic within the AWS backbone.

Stage 2: AI-Driven Processing Pipeline 🧠
S3 Raw → Lambda: Data ingestion.

SQS → Bedrock Batch: Classification via Amazon Nova Pro.

Amazon Textract: Native OCR for tables and forms.

RAG (Bedrock KB + OpenSearch Serverless): Validation using business context.

Stage 3: Frontend & API Layer 💻
AWS Amplify + API Gateway.

Amazon Cognito (MFA-enabled).

AWS WAF (SQLi protection & Geo-blocking).

⚡ Core Engine: Amazon Nova Pro We utilized Amazon Nova Pro due to its balance of latency and reasoning capabilities, making it well-suited for:

🖼️ Multimodal Classification: Identifying docs from base64 visual data.

🏷️ Intelligent Entity Extraction: Semantic mapping of raw text to business fields.

⚖️ Logical Validation: Consistency checks via RAG.

🤖 The Role of Kiro: Your Infrastructure Co-pilot Kiro functioned as a specialized architectural co-pilot throughout the lifecycle:

📜 Kiro Specs: Used "Steering" files to provide the AI with persistent context.

🛠️ IaC Generation: Streamlined the creation of ~200KB of CloudFormation templates.

🩹 Real-time Troubleshooting: Rapidly diagnosed circular dependencies and complex OpenSearch access policies.

💡 Key Lessons & Troubleshooting Bedrock Batch Constraints: Batch inference requires batching (e.g., ≥100 records). We built a buffering mechanism in Lambda for cost-optimization. 📈

Strict Security Posture: All AI service access is restricted via VPC Endpoints and resource-based policies to prevent public egress. 🔒

OpenSearch Serverless: Requires distinct Network, Encryption, and Data Access policies—traditional IAM isn't enough! 🗝️

S3 Notification Hierarchy: Used a strict directory structure (stage2/classification/) to avoid prefix overlap errors. 📁

🏁 Conclusion The synergy between advanced models like Amazon Nova Pro and AI-assisted development tools like Kiro allows cloud professionals to move from manual configuration to high-level architecture.

Why this matters:
This architecture reduced manual document handling to near zero while maintaining auditability and deterministic deployments. 🚀

"The most resilient infrastructure is the one you can describe in YAML, version in Git, and deploy with a single command."

🔗 Technical Resources
Amazon Bedrock - Nova Model Family

OpenSearch Serverless Vector Search

Kiro AI-Powered IDE

⚖️ Technical & Legal Safe Harbor Disclaimer
AUTHORSHIP: This publication is authored solely by me in my individual capacity. Views expressed are my own.
COMPLIANCE: Developed using public info; no proprietary code disclosed. Provided "AS IS".
LICENSE: MIT-0 for included source code patterns.

Building AI-Powered Business Solutions for LATAM with Amazon Quick: A Hands-on Technical Guide

luis zuñiga — Mon, 20 Apr 2026 16:29:59 +0000

Over the past few weeks, I built and deployed five production-ready use cases using Amazon Quick (the agentic evolution of QuickSight) specifically tailored for the Small and Mid-size Business (SMB) market in Latin America.What makes Amazon Quick particularly relevant for our region is the current market gap: while enterprise AI deployment grew by 68% in 2025, only 3% of SMBs have achieved full integration (IDC Latin America ICT Spending). Amazon Quick bridges this divide with a simplified pricing model (starting at $20/user) and natural language capabilities that eliminate the need for massive technical departments.🗺️ Mental Map: The Amazon Quick EcosystemPlaintextAMAZON QUICK ARCHITECTURE
│
├── 🧠 INTERFACE (Natural Language)
│ ├── Chat Agents (Contextual conversation per use case)
│ └── Spaces (Environment organization by project/team)
│
├── 🤖 AGENTIC MODULES
│ ├── Quick Research (Deep research: S3 + Web + Premium sources)
│ ├── Quick Flows (Automated workflows for business users)
│ └── Quick Automate (Complex multi-step automation for engineers)
│
├── 📊 ANALYTICS CORE
│ ├── Quick Sight (Visual BI + SPICE In-Memory Engine)
│ └── Topic Q (NLQ with bilingual synonym support)
│
└── 🛠️ INFRASTRUCTURE (IaC)
├── S3 Knowledge Base (Data Lake: CSV, JSON, TXT)
├── CloudFormation (Modular deployment via Stacks)
└── IAM (Least Privilege & SourceAccount conditions)
🏗️ Infrastructure Design & Technical DecisionsFor these deployments, I utilized an Infrastructure as Code (IaC) approach based on 11 CloudFormation templates. My core design principles were:Stack Isolation: One independent stack per use case to simplify maintenance.Layered Security: All S3 buckets utilize AES-256 encryption, full Block Public Access, and DeletionPolicy: Retain.Strict Least Privilege: IAM roles scoped specifically to each bucket, strictly avoiding wildcards (*).QuickSight Deployment Pattern via CloudFormationOne of my key takeaways was splitting the QuickSight deployment into 5 layers. The QuickSight API requires propagation time and has complex resource dependencies:Layer 1: S3 + IAM Roles.Layer 2: DataSource (S3/JSON Connector).Layer 3: DataSet (SPICE ingestion with explicit type casting).Layer 4: Topic Q (Natural language semantic configuration).Layer 5: Analysis & Dashboard (36-column grid layout definition).🤖 Success Case: Sales Automation with Quick AutomateThe most significant impact was seen in sales, where we eliminated 2 hours of manual work every Monday. We leveraged Quick Automate to generate dynamic reporting pipelines.The "Inline Agent": The Heart of AutomationI used an AI Inline Agent within the flow to transform raw DataTable objects into professional HTML reports with embedded CSS, which are then automatically uploaded to S3.[!CAUTION]Technical Gotcha: The Quick Automate runtime has critical quirks:Double Datetime: You must use datetime.datetime.now(). Using a simple datetime.now() will trigger a NameError.No Boto3: External modules cannot be imported. All S3 interactions must be performed via Action Connectors.⚠️ Builder’s Log: Lessons LearnedThe S3 Typing Challenge: By default, S3/CSV sources import everything as a STRING. If you do not perform a CastColumnTypeOperation within your CloudFormation LogicalTableMap, Topic Q will be unable to perform aggregations.Localization for LATAM: To ensure the AI functions effectively in Spanish, I configured bilingual synonyms in the column metadata (e.g., total_usd → [monto, revenue, ingreso, venta]).Quick Research & Dual Citation: In the Regulatory Compliance use case, Quick Research's ability to cite internal sources (our PDFs in S3) and external sources simultaneously was the primary trust-builder for stakeholders.💰 Cost Analysis (Why LATAM ❤️ Quick)ComponentEstimated Cost (2026)Infrastructure Fee$250/month per account (fixed)Professional User$20/month (Includes Research & Flows)Enterprise User$40/month (Includes Automate Authoring)A deployment for 36 users across 5 critical areas cost approximately $994/month, providing a massive ROI compared to manual reporting hours.ConclusionAmazon Quick is no longer just a visualization tool; it is an Agentic AI platform that democratizes technology for LATAM SMBs. As builders, our mission is to shield these systems with robust architectures and IaC.⚖️ Technical & Legal Safe Harbor DisclaimerAUTHORSHIP AND INDEPENDENT CAPACITY: This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy, position, or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE:Zero Proprietary Disclosure: This content has been developed using publicly available information and personal research. No confidential information or internal proprietary source code belonging to my employer has been disclosed.Independent Development: The workflows described are based on general industry best practices and were not developed as a "work for hire".LIMITATION OF LIABILITY (NO WARRANTY): All code snippets and architectural patterns are provided "AS IS" without warranty of any kind.COMPLIANCE: This contribution is made in good faith under the AWS Builder Terms and the MIT-0 License for any included source code.

Deep Dive: Accelerating Infrastructure as Code (IaC) on GCP using Terraform and Antigravity

luis zuñiga — Fri, 17 Apr 2026 00:42:09 +0000

Key Stack: Terraform, Google Cloud Platform (GCP), Cloud Armor, Cloud SQL, Antigravity AI

When designing robust and scalable architectures for production environments, efficiency is non-negotiable. Traditionally, SRE and Infrastructure teams spend significant cycles managing network segregation, variable consistency, and manual security audits. However, the paradigm has shifted: Generative AI applied to Platform Engineering has arrived to eliminate operational toil.

In this article, we will technically analyze the paquetesaction project. We will explore how to deploy advanced Terraform modules on Google Cloud by operating alongside Antigravity—an AI-powered assistant tailored for infrastructure workflows based on Google DeepMind technology—which acts as an additional software engineer within your terminal.

The Multi-Project Modularity Challenge For this use case, the requirements demanded four distinct architectures designed to coexist within an enterprise ecosystem. The goal was clear: total isolation and automated scalability.

Base VPC (Core Networking): Implementation of custom networks with Private Google Access enabled for internal consumption of Google APIs without internet egress.

Private Data Workloads: Cloud SQL (MySQL) with restricted access via VPC Peering, eliminating any public IP exposure.

Resilient L7 Frontend: Global HTTP(S) Load Balancer supported by Managed Instance Groups (MIG) and perimeter protection via Cloud Armor.

Management Access (Bastion): e2-micro instances for administration, using strict tag-based routing.

Antigravity: Pair Programming "On Steroids" The true disruption of Antigravity lies not in static code generation, but in its ability to execute an iterative framework within the DevOps lifecycle.

Rather than generating isolated code, the agent operated as a collaborator aware of the repository and the Terraform lifecycle. While orchestrating the environments/dev directory, the agent autonomously structured:

The file architecture (main.tf, variables.tf, outputs.tf).

Initialization logic via CLI commands (terraform init and terraform fmt).

Selection of optimized images (Debian 11) to meet internal compliance policies.

Technical Architecture & Data Flow Below is a breakdown of the critical infrastructure components designed for this project.

A. Managed Database Isolation (Cloud SQL)
Exposing a database to the internet is an unacceptable risk. We utilized Private Services Access to connect our VPC with the Google Tenant Project.


+----------------------------------------------------+
| Your GCP Project (Consumer VPC)                    |
|                                                    |
|  +----------------------------------------------+  |
|  | VPC: "mysql-vpc-dev"                         |  |
|  |                                              |  |
|  |  [Global IP Range Reservation: /16]          |  |
|  |             |                                |  |
|  +-------------|--------------------------------+  |
|                |                                   |
|                v (Automatic VPC Peering)           |
|                                                    |
|  +----------------------------------------------+  |
|  | Google Managed Services (Tenant VPC)          |  |
|  |                                              |  |
|  |  +---------------------------------------+   |  |
|  |  | Cloud SQL Instance (MySQL 8.0)         |   |  |
|  |  | - IPv4_enabled: OFF                   |   |  |
|  |  | - Private IP (from reserved range)    |   |  |
|  |  +---------------------------------------+   |  |
|  +----------------------------------------------+  |
+----------------------------------------------------+

B. Next-Gen WAF Defenses via Cloud Armor
To protect backends, we delegate security to Google's Edge. Cloud Armor acts as a Layer 7 shield, filtering threats before they ever reach our compute instances.


              Inbound Web Traffic
                      |
                      v
       +-------------------------------+
       | Global HTTP Load Balancer     | 
       +--------------+----------------+
                      |
       +--------------v----------------+
       | Cloud Armor Security Policy   |  (L7 Filtering)
       | -> Blocks SQLi, XSS, LFI      |
       +--------------+----------------+
                      |
                      v (Sanitized Traffic)
+---------------------+-----------------------------------+
| Principal VPC                                           |
|   +-------------------------------------------------+   |
|   | Subnet                                          |   |
|   | Firewall: Allow ONLY Google LB IPs              |   |
|   |           (130.211.0.0/22 & 35.191.0.0/16)      |   |
|   |                                                 |   |
|   |   +-----------------------------------------+   |   |
|   |   | Managed Instance Group (MIG)            |   |   |
|   |   |  [ Apache Web Server VM - Debian 11 ]   |   |   |
|   |   +-----------------------------------------+   |   |
|   +-------------------------------------------------+   |
+---------------------------------------------------------+

Checkov: Closing the Governance Loop In a production workflow, compliance is vital. When integrating tools like Checkov, it is common to trigger security alerts. Antigravity helped us apply the Principle of Least Privilege, replacing default accounts with dedicated IAM Service Accounts.

For cases where the design required specific exceptions, the agent injected formal suppression syntax:

Terraform resource "google_compute_instance" "public_instance" { # checkov:skip=CKV_GCP_40: Public IP explicitly required for administrative bastion # checkov:skip=CKV_GCP_32: OS Login bypass authorized for this specific use-case name = "bastion-dev" machine_type = "e2-micro" ... }
Each exception was evaluated during the design phase and documented inline to preserve traceability and facilitate future audits.

The Future of Infrastructure The paquetesaction project demonstrates that the future of the cloud is hybrid: human strategic judgment amplified by AI execution speed. Our next steps involve expanding toward Vertex AI and consolidating security operations with Mandiant.

The infrastructure is code, and AI is its most powerful catalyst!

⚖️ Technical & Legal Safe Harbor Disclaimer
AUTHORSHIP AND INDEPENDENT CAPACITY: This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy, position, or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.

INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE:

Zero Proprietary Disclosure: This content has been developed using publicly available information and personal research. No confidential information or internal proprietary source code belonging to any specific organization has been disclosed.

Independent Development: The workflows described are based on general industry best practices and were not developed as a "work for hire".

LIMITATION OF LIABILITY (NO WARRANTY): All code snippets and architectural patterns are provided "AS IS" without warranty of any kind.

COMPLIANCE: This contribution is made in good faith under the MIT-0 License for any included source code patterns.

🚀 Mastering the Hybrid Workflow: AWS Development with Kiro Pro (IDE + CLI)

luis zuñiga — Wed, 15 Apr 2026 15:48:29 +0000

Description
Stop choosing between a GUI and a Terminal. Learn how to leverage Kiro Pro, AWS MCPs, and a hybrid Windows/Linux workflow to build secure, well-architected cloud projects from scratch to deployment.

The Content
Hey fellow builders! 👋

As a Cloud Engineer, I’m always looking for ways to bridge the gap between "writing code" and "deploying securely." Lately, I’ve been experimenting with Kiro Pro and its AWS Model Context Protocol (MCP) integration.

I’ve found that the secret sauce isn't just using the tool—it's how you split the work between Windows (IDE) for planning and Linux (CLI) for the heavy lifting. I recently published a deep dive on this methodology, which you can check out here:

👉 The Art of Hybrid Development: Optimizing Application Lifecycles with Kiro Pro and AWS

Here’s the breakdown of one possible professional workflow for building high-quality AWS projects.

🏗️ Phase 1: The "Architect" (Kiro IDE on Windows)
I recommend using the IDE when you are in "creation mode." It’s where the logic is born.

The Setup
First, ensure your AWS MCPs are active. This gives the AI the "eyes" it needs to see your AWS environment in real-time.
The "Requirements-First" Strategy
Don't just prompt "build an app." Create a requirements.md file. This is your project's source of truth. Include:

User Stories: Step-by-step behavior.

Hard Constraints: e.g., "Never hardcode credentials—use AWS Secrets Manager." 🛡️

Visualizing with ASCII
Ask Kiro to generate an ASCII Flowchart. Seeing the data flow between the Frontend, Backend, and S3/DynamoDB in plain text helps catch logic flaws before you even start coding.
The Power Move: Claude 3.5/Opus
When it's time to generate the code, I suggest switching to Claude 3.5 Sonnet or Opus. The reasoning capabilities for Infrastructure as Code (IaC) are top-tier. Once validated, push everything to a private GitHub repo.

🐧 Phase 2: The "Operator" (Kiro CLI on Linux)
When it's time to get "dirty" with deployment, moving to a Linux VM ensures superior operational control.

Secure Credential Injection
Use a custom script to inject temporary AWS credentials. No long-term keys, no leaks.
Deep Contextualization
Once you clone the repo, run:
kiro prompt "Deep dive into local repo XXX and build full context."
This ensures the CLI knows exactly what was built in Phase 1.
The "Least Privilege" Audit 🔍
This is the most critical step. Ask Kiro to:

Validate credentials against the repo resources.

Output a JSON of required permissions. This allows for the creation of a scoped IAM Policy that follows the Principle of Least Privilege before hitting "deploy."

The "Smart" README & Technical Memory After a successful deploy, let Kiro handle the documentation. It can update the README.md with the actual execution findings and generate a Technical Memory file for future reference.

💡 Pro-Tips for the Community
Sync your MCPs: If your IDE knows something your CLI doesn't, you're going to have a bad time. Keep them updated.

Separation of Concerns: Use the IDE for Design and the CLI for Implementation.

Score your work: Ask Kiro for a "Project Score" at the end to see where you can optimize your AWS architecture.

What about you? Are you using Kiro or other AI-assisted tools for your AWS deployments? Check out my full article on AWS Builder Center and let’s discuss in the comments! 👇

aws #cloudcomputing #devops #kiropro #productivity #architecture #awsbuilders

⚖️ Technical & Legal Safe Harbor Disclaimer
AUTHORSHIP AND INDEPENDENT CAPACITY: This publication is authored solely by me in my individual and private capacity. The views, methodologies, and technical workflows expressed herein are my own and do not necessarily reflect the official policy, position, or strategic direction of my current or former employers, clients, or any legal entity I am affiliated with.

INTELLECTUAL PROPERTY & CONFIDENTIALITY COMPLIANCE:

Zero Proprietary Disclosure: This content has been developed using publicly available information, official documentation, and personal research. No confidential information, trade secrets, internal proprietary source code, or non-public infrastructure schemas belonging to my employer or any third party have been used, referenced, or disclosed in this publication.

Independent Development: The workflows described (including the Kiro Pro / AWS hybrid methodology) are based on general industry best practices and were not developed as a "work for hire" or as part of specific assigned duties for any organization.

Standard Industry Tools: References to third-party tools (AWS, Kiro, Anthropic/Claude) are for educational purposes and based on commercially available features.

LIMITATION OF LIABILITY (NO WARRANTY): All code snippets, scripts, and architectural patterns are provided "AS IS" without warranty of any kind, express or implied, including but not limited to the warranties of merchantability or fitness for a particular purpose. In no event shall the author be liable for any claim, damages, or other liability arising from the use of this technical information.

COMPLIANCE: This contribution is made in good faith and intended to foster community knowledge under the AWS Builder Terms and the MIT-0 License for any included source code.

From Learning to Practice: Why I Decided to Stop Studying Privately and Start Sharing 🚀

luis zuñiga — Thu, 02 Apr 2026 02:38:58 +0000

Hi everyone! 👋
I’ve spent a long time immersed in courses, labs, and documentation. For months (and even years), my focus has been on absorbing as much as possible about Cloud Engineering, Data Analysis, and beyond. However, today I’ve made an important decision: to stop keeping my projects in local folders and start sharing them with the community.
I’ve realized that the best way to grow isn’t just by studying, but by exposing my work to the eyes of other professionals—to receive feedback, improve, and hopefully help someone else on a similar path.
🛠️ My first contribution: Data Processing
Today, I’m sharing a repository I’ve been working on. It’s a tool designed to standardize and streamline data processing in Python, which is essential when seeking efficiency in Cloud environments.
🌟 Why did I decide to publish it today?
Validation: I want to know if the solutions I design follow industry best practices.
Community: I firmly believe the developer ecosystem thrives when we share what we learn.
Transparency: This marks the beginning of my public portfolio, showcasing my real evolution in technologies like AWS and GCP.
💻 Explore the repository
I invite you to check out the code, clone it, or even critique it (constructively, of course!). Any suggestions on how to optimize it are more than welcome.
👉 Project URL: https://github.com/luiszuniga1990/data-processing.git

DEV Community: luis zuñiga

🚀 Beyond the HCL: Trench Lessons from Deploying Critical Architectures on GCP with Terraform

googlecloud #terraform #gcpcommunity #devops #cloudsecurity #iac #hybridcloud

🚀 Bridge to the Cloud: A Tactical Guide to Hybrid Resilience with Nutanix NC2 on AWS

aws #nutanix #hybridcloud #nc2 #devops #cloudcomputing #infrastructure #awsbuilders

🚀 Elevating the Standard: From ASCII Diagrams to Visual Architectures with Kiro 101 and AWS MCP

Actionable Packages — paqueteAction: AWS Account Hardening Playbook

🌟 The Core Concept

🛑 1. The Problem

🛠️ 2. The Solution: paqueteAction

⚙️ 3. Workflow: Validated IaC with Kiro

🔍 4. Deep Dive: Key Security Patterns

📊 5. Impact: Before vs. After

🎯 6. Conclusions

⚖️ Legal Disclaimer

🛠️ From Foundations to Advanced SecOps: The GCP Developer’s Toolkit 🛡️

GCP #GCPDev #CloudSecurity #DevSecOps #GoogleCloud #Mandiant #Chronicle #CompanyOfOne #TechCommunity

🚀 Building an Intelligent Document Processing System with AI on AWS: From YAML to Production with Kiro

Building AI-Powered Business Solutions for LATAM with Amazon Quick: A Hands-on Technical Guide

Deep Dive: Accelerating Infrastructure as Code (IaC) on GCP using Terraform and Antigravity

🚀 Mastering the Hybrid Workflow: AWS Development with Kiro Pro (IDE + CLI)

aws #cloudcomputing #devops #kiropro #productivity #architecture #awsbuilders

From Learning to Practice: Why I Decided to Stop Studying Privately and Start Sharing 🚀

🛠️ 2. The Solution: `paqueteAction`