<h2>
  
  
  🎯 Goal
</h2>

<p>Create a simple EC2 instance using:</p>

<ul>
<li>Variables → for AMI, instance_type, tags</li>
<li>Locals → for naming</li>
<li>Outputs → instance_id, public_ip</li>
<li>Remote backend → S3</li>
<li>State locking → DynamoDB</li>
</ul>

<p>Using your backend resources:</p>

<ul>
<li>S3 bucket: tf-backend-lab-123</li>
<li>DynamoDB table: tf-state-lock</li>
</ul>




<h2>
  
  
  📁 Folder Structure
</h2>

<p>We keep it clean for this task:<br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>terraform-ec2-basic/
├── backend.tf
├── main.tf
├── variables.tf
├── outputs.tf
├── terraform.tfvars
</code></pre>

</div>



<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fn5l4ui4nfk9fprbt38s3.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fn5l4ui4nfk9fprbt38s3.png" alt="1" width="800" height="210"></a></p>




<p><strong>🧩 backend.tf</strong><br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>terraform {
  backend "s3" {
    bucket       = "tf-backend-lab-123"
    key          = "ec2/basic/terraform.tfstate"
    region       = "us-east-1"
    encrypt      = true
    dynamodb_table = "tf-state-lock"
  }
}
</code></pre>

</div>






<p><strong>📌 variables.tf</strong></p>

<p>We define input variables:<br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>variable "instance_type" {
  description = "EC2 instance type"
  type        = string
  default     = "t3.micro"
}

variable "ami_id" {
  description = "AMI ID for EC2 instance"
  type        = string
}

variable "env" {
  description = "Environment name"
  type        = string
  default     = "dev"
}

variable "owner" {
  description = "Owner/Team name"
  type        = string
}
</code></pre>

</div>






<p><strong>🧠 locals</strong></p>

<p>Locals are used to build consistent resource names.</p>

<p>Add this inside main.tf or create locals.tf<br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>locals {
  name_prefix = "${var.env}-${var.owner}"
  common_tags = {
    Environment = var.env
    Owner       = var.owner
    ManagedBy   = "Terraform"
  }
}
</code></pre>

</div>



<p>Interview Tip:</p>

<blockquote>
<p>Locals help avoid repeating variables and maintain consistent naming conventions.</p>
</blockquote>




<p><strong>📌 main.tf</strong><br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>provider "aws" {
  region = "us-east-1"
}

resource "aws_instance" "server" {
  ami           = var.ami_id
  instance_type = var.instance_type

  tags = merge(
    local.common_tags,
    {
      Name = "${local.name_prefix}-ec2"
    }
  )
}
</code></pre>

</div>






<p><strong>📤 outputs.tf</strong><br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>output "instance_id" {
  value       = aws_instance.server.id
  description = "EC2 Instance ID"
}

output "public_ip" {
  value       = aws_instance.server.public_ip
  description = "Public IP address of the EC2 instance"
}

output "tags_used" {
  value = local.common_tags
}
</code></pre>

</div>






<p><strong>📌 terraform.tfvars</strong></p>

<p>Provide your values:<br>
</p>

<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>ami_id = "ami-0c02fb55956c7d316"   # Amazon Linux 2 in us-east-1
owner  = "lachu"
env    = "dev"
instance_type = "t3.micro"
</code></pre>

</div>






<h2>
  
  
  ▶️ Commands
</h2>



<div class="highlight js-code-highlight">
<pre class="highlight plaintext"><code>terraform init
terraform validate
terraform plan
terraform apply -auto-approve
</code></pre>

</div>



<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F6ar49fcuc4alebk7nsyu.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F6ar49fcuc4alebk7nsyu.png" alt="1" width="800" height="193"></a></p>

<p>If you can check with dynamoDB table</p>

<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F6z89l6mgmkwvwdvevmgr.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F6z89l6mgmkwvwdvevmgr.png" alt="2" width="800" height="315"></a></p>




<h2>
  
  
  🧠 Interview Notes (Very Important)
</h2>

<p><strong>✔ Why use variables?</strong></p>

<ul>
<li>Reusability</li>
<li>Runtime flexibility</li>
<li>Parameterizing environment-specific values</li>
</ul>

<p><strong>✔ Why use locals?</strong></p>

<ul>
<li>Avoid repeating variable values</li>
<li>Standardize naming conventions</li>
<li>Reduce mistakes in big projects</li>
</ul>

<p><strong>✔ Why use outputs?</strong></p>

<ul>
<li>To expose useful information after apply</li>
<li>Helpful for automation (CI/CD, scripts)</li>
<li>Used by other Terraform modules</li>
</ul>

<p><strong>✔ Why S3 + DynamoDB?</strong></p>

<ul>
<li>S3 → stores remote state</li>
<li>DynamoDB → prevents two people from running apply at the same time</li>
</ul>

<p><strong>✔ Typical interview question:</strong></p>

<p><strong>How does Terraform handle state locking?</strong></p>

<p>Answer:</p>

<blockquote>
<p>When using S3 backend with DynamoDB, Terraform creates a lock entry in DynamoDB during operations such as plan or apply. This prevents concurrent modifications of the state, ensuring consistency.</p>
</blockquote>




<p>🌟 Thanks for reading! If this post added value, a like ❤️, follow, or share would encourage me to keep creating more content.</p>




<p><strong>— Latchu | Senior DevOps &amp; Cloud Engineer</strong></p>

<p>☁️ AWS | GCP | ☸️ Kubernetes | 🔐 Security | ⚡ Automation<br>
📌 Sharing hands-on guides, best practices &amp; real-world cloud solutions</p>

تتناول المقالة خطوات إنشاء مثيل EC2 بسيط باستخدام Terraform. تركز على استخدام المتغيرات لـ AMI ونوع المثيل والعلامات، والمحليين للتسمية، والمخرجات لـ ID المثيل و IP العامة. تتضمن الإعدادات استخدام خلفية بعيدة عبر S3 وقفل الحالة باستخدام DynamoDB. هيكل المجلدات منظم مع ملفات Terraform الأساسية مثل backend.tf و main.tf و variables.tf و outputs.tf و terraform.tfvars.

El artículo describe los pasos para crear una instancia EC2 simple utilizando Terraform. Se enfatiza el uso de variables para AMI, tipo de instancia y etiquetas, locales para nombrar, y salidas para el ID de la instancia y la IP pública. La configuración incluye un backend remoto utilizando S3 y bloqueo de estado con DynamoDB. La estructura de carpetas está organizada con archivos esenciales de Terraform como backend.tf, main.tf, variables.tf, outputs.tf y terraform.tfvars.

L'article décrit les étapes pour créer une instance EC2 simple à l'aide de Terraform. Il met l'accent sur l'utilisation de variables pour l'AMI, le type d'instance et les balises, de locaux pour le nommage, et de sorties pour l'ID de l'instance et l'IP publique. La configuration inclut un backend distant utilisant S3 et un verrouillage d'état avec DynamoDB. La structure des dossiers est organisée avec des fichiers Terraform essentiels tels que backend.tf, main.tf, variables.tf, outputs.tf et terraform.tfvars.

The article outlines the steps to create a simple EC2 instance using Terraform. It emphasizes the use of variables for AMI, instance type, and tags, locals for naming, and outputs for instance ID and public IP. The setup includes a remote backend using S3 and state locking with DynamoDB. The folder structure is organized with essential Terraform files such as backend.tf, main.tf, variables.tf, outputs.tf, and terraform.tfvars.

✅ Task — 3: Use Variables, Locals, and Outputs for a Simple EC2 Instance Setup

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<p>SQL Server 2025 has been <a href="https://techcommunity.microsoft.com/blog/sqlserver/sql-server-2025-is-now-generally-available/4470570" rel="noopener noreferrer">officially announced</a> at Microsoft Ignite 2025 and is now generally available.  It had been in public preview since May of 2025 and consumers have been eagerly anticipating the official release of one of Microsoft's most popular software products. I had previous written a fun article where I used AI and historical date like previews, events, and past release dates to try and guess the <a href="https://www.trustedtechteam.com/blogs/sql-server/sql-server-2025-release-date" rel="noopener noreferrer">release date of SQL Server 2025</a>.  </p>

<p>Now it's finally here, and with it comes a whole host of highly anticipated features, most notably AI integration. </p>

<h2>
  
  
  Major Themes
</h2>

<p><strong><a href="https://www.trustedtechteam.com/blogs/sql-server/sql-server-2025-new-ai-features" rel="noopener noreferrer">Built-in AI</a> &amp; vector search</strong></p>

<ul>
<li>SQL Server 2025 adds native support for vector-stores/indexing (via DiskANN) enabling semantic search over large datasets. </li>
<li>You can combine traditional keyword SQL queries with vector‐based search in one engine. </li>
<li>Support for retrieval-augmented generation (RAG) patterns, connecting to AI models via REST endpoints, etc. </li>
</ul>

<p><strong>Modern developer productivity enhancements</strong></p>

<ul>
<li>Native JSON support, regular expression support, REST APIs, GraphQL via Data API Builder, etc. </li>
<li>The idea is to let developers build AI-driven/modern applications whether on-premises or cloud with more extensibility. </li>
</ul>

<p><strong>Security, performance &amp; scalability</strong></p>

<ul>
<li>New authentication support via Microsoft Entra ID managed identities and outbound authentication via Azure Arc for hybrid/edge. </li>
<li>Improved query optimization: e.g., persisted statistics on replicas, improved locking mechanisms (TID Locking, LAQ) to reduce contention, improved batch processing and columnstore support for analytics. </li>
</ul>

<p><strong>Hybrid and cloud‐centric analytics &amp; data integration</strong></p>

<ul>
<li>Integration with Microsoft Fabric: near real-time replication of SQL Server data (mirrored database) into OneLake for analytics. </li>
<li>Better support for hybrid cloud, on-premises, and edge scenarios — managing, governing, monitoring across environments with Azure Arc, centralized management etc. </li>
</ul>

<h2>
  
  
  Why this matters
</h2>

<ul>
<li>The database is being positioned as a first‐class AI platform, not just a relational engine.</li>
<li>For companies with large datasets, or those looking to embed AI/semantic search capabilities, this version could significantly change how they build data and app-architectures.</li>
<li>For hybrid/edge scenarios (on-premises + cloud + edge), the enhancements around Azure Arc, Fabric integration, and real-time replication make it easier to maintain consistency, governance, and analytics across environments.</li>
<li>Developers get more flexible tooling (JSON, REST, GraphQL etc) so the barrier to building modern apps on top of the database is lower.</li>
<li>Performance/scalability/security updates mean this is not just “shiny new features” but also foundation</li>
</ul>

تم الإعلان رسميًا عن SQL Server 2025 في مؤتمر Microsoft Ignite 2025 وهو متاح الآن بشكل عام. بعد معاينة عامة منذ مايو 2025، يتضمن الإصدار ميزات متوقعة بشدة، لا سيما دمج الذكاء الاصطناعي، مما يعزز قدراته كواحد من أكثر منتجات البرمجيات شعبية من Microsoft.

SQL Server 2025 ha sido anunciado oficialmente en Microsoft Ignite 2025 y ya está disponible. Después de una vista previa pública desde mayo de 2025, el lanzamiento incluye características muy esperadas, especialmente la integración de IA, mejorando sus capacidades como uno de los productos de software más populares de Microsoft.

SQL Server 2025 a été officiellement annoncé lors de Microsoft Ignite 2025 et est désormais disponible. Après une période de prévisualisation publique depuis mai 2025, cette version comprend des fonctionnalités très attendues, notamment l'intégration de l'IA, renforçant ses capacités en tant que l'un des produits logiciels les plus populaires de Microsoft.

SQL Server 2025 has been officially announced at Microsoft Ignite 2025 and is now generally available. After a public preview since May 2025, the release includes highly anticipated features, particularly AI integration, enhancing its capabilities as one of Microsoft's most popular software products.

SQL Server 2025 is Here!

<h1>
  
  
  AI Amnesia: Erasing Knowledge Without a Trace
</h1>

<p>Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?</p>

<p>The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.</p>

<p>Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.</p>

<p><strong>Benefits of Selective Forgetting:</strong></p>

<ul>
<li>  <strong>Enhanced Data Privacy:</strong> Remove sensitive data without compromising the overall model's performance.</li>
<li>  <strong>Reduced Retraining Costs:</strong> Avoid full model retraining, saving significant time and resources.</li>
<li>  <strong>Improved Model Security:</strong> Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li>  <strong>Adaptable Learning:</strong> Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li>  <strong>Compliance Ready:</strong> Supports compliance with data privacy regulations like GDPR.</li>
<li>  <strong>Scalable Solutions:</strong> Works efficiently even with limited access to training data.</li>
</ul>

<p><strong>Practical Tip:</strong> One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.</p>

<p>The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.</p>

<p><strong>Related Keywords:</strong> machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance</p>

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

✅ Task — 3: Use Variables, Locals, and Outputs for a Simple EC2 Instance Setup

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