DEV Community: Takneekigyanguru

Step-by-Step Guide: Scanning Your .NET Project with SonarQube

Takneekigyanguru — Sun, 09 Nov 2025 16:18:40 +0000

🧩 Step-by-Step Guide: Scanning Your .NET Project with SonarQube

Want to ensure your .NET codebase is clean, secure, and high quality?
Let’s explore how to scan your .NET project using SonarQube — a powerful platform for detecting bugs, code smells, vulnerabilities, and enforcing best practices across your applications.

🎥 Full Video Tutorial:
👉 Step-by-Step Guide: Scanning Your .NET Project with SonarQube

🧠 What You’ll Learn

In this tutorial, you’ll learn how to:

Set up SonarQube locally or on a server

Integrate SonarScanner for .NET

Analyze code quality and security metrics

Identify code smells, bugs, and vulnerabilities

Generate a comprehensive dashboard report for your project

By the end, you’ll know how to keep your .NET projects clean, consistent, and maintainable — whether you’re a solo developer or part of a CI/CD pipeline.

⚙️ Prerequisites

Before you start, make sure you have:

.NET SDK installed (6.0 or later)

SonarQube (Developer or Community Edition)

PostgreSQL or any supported database

Basic understanding of build pipelines

🔧 Step 1: Install and Configure SonarQube

You can install SonarQube locally with PostgreSQL on Windows.
Follow this guide:
🎬 Installing SonarQube with PostgreSQL on Windows: Step-by-Step Guide

Once installed:

Start the SonarQube server (StartSonar.bat).

Create a new project and generate a token.

🔍 Step 2: Configure SonarScanner for .NET

Install the SonarScanner for .NET tool:

dotnet tool install --global dotnet-sonarscanner

Initialize the scanner in your project:

dotnet sonarscanner begin /k:"ProjectKey" /d:sonar.host.url="http://localhost:9000" /d:sonar.login="your_token"
dotnet build
dotnet sonarscanner end /d:sonar.login="your_token"

🧪 Step 3: Run the Scan and Analyze Results

Once the scan completes, open your SonarQube dashboard.

Navigate to Issues → Security Hotspots → Code Smells.

Review and fix the highlighted issues directly in your code editor.

📊 Step 4: Continuous Integration (Optional)

You can integrate SonarQube analysis into your CI/CD pipelines:

Jenkins: Add SonarQube plugin and call the same scanner steps.

Azure DevOps: Use the “Prepare Analysis on SonarQube” task.

GitHub Actions: Use SonarSource/sonarcloud-github-action.

This ensures that every code commit is automatically scanned for quality and compliance.

💡 Why Use SonarQube?

✅ Detect vulnerabilities early
✅ Improve team-wide code consistency
✅ Enforce quality gates before merging
✅ Build confidence in every release

🧱 Ideal For

.NET Developers aiming to improve code health

DevOps Engineers integrating static code analysis

Teams adopting DevSecOps practices

🧾 Tags

DotNet #SonarQube #CodeQuality #DevOps #CICD #Security #TakneekiGyanGuru #Tutorial #DevSecOps

✨ Author

Takneeki Gyan Guru — guiding developers through real-world tutorials on AI, ML, Cloud, DevOps, and Application Development.
Follow for more hands-on videos and step-by-step DevOps learning content.

Automating .NET Core Deployment to IIS with Jenkins | CI/CD Pipeline Setup

Takneekigyanguru — Sun, 09 Nov 2025 16:13:44 +0000

⚙️ Automating .NET Core Deployment to IIS with Jenkins | CI/CD Pipeline Setup

Tired of deploying your .NET Core applications manually to IIS?
Let’s fix that! 🚀

In this step-by-step tutorial, you'll learn how to automate .NET Core application deployment to IIS using Jenkins CI/CD pipelines — saving time, improving consistency, and reducing human error.

🎥 Full Video Tutorial:
👉 Automating .NET Core Deployment to IIS with Jenkins | CI/CD Pipeline Setup

🧩 What You’ll Learn

This tutorial covers everything you need to create a complete CI/CD pipeline for .NET Core applications:

Jenkins Pipeline Setup — building a continuous integration workflow for .NET Core

IIS Configuration Automation — automate website setup and deployment

Ansible / Chef / Puppet Integration — manage infrastructure and application provisioning efficiently

Seamless Deployment — push new versions of your app automatically after each successful build

🔧 Prerequisites

Before starting, make sure you have:

Jenkins installed and configured

IIS running on your target Windows Server

.NET Core SDK installed on both Jenkins and deployment machine

Basic familiarity with PowerShell, Ansible, or Chef/Puppet

🏗️ Step-by-Step Outline
1️⃣ Create a Jenkins Pipeline

Define your pipeline stages:

Build the .NET Core project

Test with NUnit or xUnit

Package the application into an artifact

Deploy automatically to IIS

Example snippet:

pipeline {
agent any
stages {
stage('Build') {
steps {
bat 'dotnet build MyWebApp.sln'
}
}
stage('Publish') {
steps {
bat 'dotnet publish -c Release -o publish'
}
}
stage('Deploy to IIS') {
steps {
bat 'powershell.exe -ExecutionPolicy Bypass -File deploy.ps1'
}
}
}
}

2️⃣ Automate IIS Configuration

Use PowerShell or a configuration management tool (like Ansible/Chef/Puppet) to:

Create the IIS website

Set the physical path

Bind the correct port

Deploy the latest build

3️⃣ Integrate Infrastructure as Code

If you want full automation:

Ansible: Use win_iis_website and win_copy modules

Chef/Puppet: Define IIS site as a resource

Trigger these tasks from Jenkins pipeline

🧰 Related Videos

🎬 Create .NET Core Web App | Automate .NET Core Web App Deployment with Jenkins Pipeline

💡 Why Automate?

Manual deployments lead to:
❌ Inconsistent environments
❌ Missed steps under pressure
❌ Difficult rollback

Automating CI/CD ensures:
✅ Consistency
✅ Faster releases
✅ Reliability and visibility

🧠 Ideal For

DevOps engineers automating .NET pipelines

Developers learning CI/CD for Microsoft stack

Anyone setting up hybrid infrastructure deployments

🧾 Tags

DotNet #Jenkins #CICD #DevOps #IIS #Automation #Ansible #Chef #Puppet #TakneekiGyanGuru

✨ Author

Takneeki Gyan Guru — simplifying AI, ML, Cloud, DevOps, and Software Engineering with practical tutorials.
Follow for more step-by-step guides on CI/CD, Automation, and Cloud-native development.

🧠 Build a Document Search with RAG | Hugging Face Transformers + Flan-T5 + NLP Tutorial

Takneekigyanguru — Sun, 09 Nov 2025 16:01:44 +0000

🧠 Build a Document Search with RAG | Hugging Face Transformers + Flan-T5 + NLP Tutorial

Ever wondered how to make your own AI-powered document search system?
In this tutorial, we’ll build one step by step using Retrieval-Augmented Generation (RAG) — the same principle behind modern GenAI systems.

🎥 Watch the full video here:
👉 YouTube: Build a Document Search with RAG | Hugging Face Transformers + Flan-T5 + NLP Tutorial

🚀 What You’ll Learn

This hands-on tutorial shows you how to build an intelligent document search engine using Python, Hugging Face Transformers, Sentence Transformers, and Flan-T5.

We’ll cover:

Chunking documents — split large text files into smaller chunks for efficient processing

Text embeddings — convert chunks into semantic vector representations using Sentence Transformers

Semantic search — use cosine similarity to find the most relevant chunks for a user query

RAG pipeline — combine retrieved text with a language model (Flan-T5) for contextual, natural-language answers

End-to-end architecture — see how chunking, embedding, retrieval, and generation connect into a working RAG system

🧩 Code Overview

You’ll build 4 key components:

Chunker

Splits large text documents into smaller, overlapping segments for better recall.

def chunk_text(text, chunk_size=500, overlap=50):
chunks = []
for i in range(0, len(text), chunk_size - overlap):
chunks.append(text[i:i + chunk_size])
return chunks

Embedder

Encodes chunks into embeddings using Sentence Transformers.

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(chunks)

Query Engine

Uses cosine similarity to retrieve the most relevant chunks.

from sklearn.metrics.pairwise import cosine_similarity
scores = cosine_similarity(query_embedding, embeddings)

RAG Pipeline

Passes retrieved context to Flan-T5 to generate detailed, context-aware answers.

from transformers import pipeline
rag_pipeline = pipeline("text2text-generation", model="google/flan-t5-base")
result = rag_pipeline(f"Answer based on: {context}\nQuestion: {query}")

🧱 Architecture Overview

Retrieval-Augmented Generation (RAG) combines:

Retriever → finds relevant text chunks

Generator → formulates a human-like answer

It allows LLMs to use external knowledge sources without retraining — perfect for dynamic knowledge bases and document search.

💻 Full Source Code

🔗 GitHub Repository: takneekigyanguru/document-search-rag

🧩 Ideal For

Building knowledge base Q&A systems

Searching large technical or business documents

Understanding GenAI + RAG pipelines end-to-end

Preparing for AI/ML or NLP interviews

🔖 Tags

RAG #HuggingFace #FlanT5 #DocumentSearch #Python #MachineLearning #NLP #AI #SemanticSearch #PythonTutorial

✨ Author

Takneeki Gyan Guru — simplifying AI, ML, Cloud, and DevOps concepts through practical tutorials and real-world demos.
Follow me for more guides on AI + Cloud integration and GenAI architecture.