DEV Community: Sahinur

RDS vs DynamoDB: How I Think About Choosing an AWS Database

Sahinur — Thu, 17 Sep 2026 18:00:00 +0000

Choosing a database is one of those decisions that can look simple at the beginning of a project.

You need to store some data, so you pick a database and start building.

But once you start thinking about scale, queries, cost, performance, and maintenance, the decision becomes much more interesting.

As a Node.js backend developer, I've mostly worked with MongoDB, but while learning and working with AWS, I've spent more time understanding how AWS database services such as Amazon RDS and Amazon DynamoDB fit into backend architectures.

They solve very different problems.

Here's how I approach the decision.

The data shape comes first

Before choosing a database, I think about the data and, more importantly, how the application will access that data.

For example, imagine an e-commerce application.

We might have:

Users
Products
Orders
Payments
Reviews

An order might contain relationships between multiple entities:

User
  ↓
Orders
  ↓
Products
  ↓
Payments

If the application requires complex relationships, joins, transactions, and flexible SQL queries, a relational database becomes very attractive.

That's where RDS can make sense.

But not every application needs that model.

Imagine an application where the primary access pattern is:

Get user by ID
Get user's notifications
Get latest notifications
Mark notification as read

If these access patterns are predictable and the application needs massive scalability, DynamoDB becomes an interesting option.

The key lesson is:

Don't start with the database. Start with the data and access patterns.

What pulls me toward RDS

Amazon RDS provides managed relational databases such as:

PostgreSQL
MySQL
MariaDB
Oracle
SQL Server

One of the biggest advantages is the relational model.

For applications with relationships between entities, SQL can make the data model and queries much easier to reason about.

For example:

SELECT
    users.name,
    orders.id,
    orders.total
FROM users
JOIN orders
    ON users.id = orders.user_id
WHERE users.id = 1001;

This type of relationship-oriented query is natural in a relational database.

RDS is particularly attractive when I need:

Complex queries
Relationships
Joins
Transactions
SQL
Reporting
Existing relational database expertise

Another advantage is that many backend developers already understand SQL and relational database design.

That can significantly reduce the learning curve.

What pulls me toward DynamoDB

DynamoDB is fundamentally different.

Instead of starting with relationships and joins, I think about access patterns.

For example:

GetUserById
GetOrdersByUser
GetLatestOrders
GetOrderById

The table and indexes are designed around how the application will query the data.

This approach can be extremely powerful for applications that need:

High scalability
Low-latency access
Predictable access patterns
Serverless architectures
High request volumes
Automatic scaling

DynamoDB also removes much of the traditional database server management.

That makes it particularly interesting when building serverless applications with services such as Lambda and API Gateway.

The architectural difference

A traditional RDS-based backend might look like:

Client
   ↓
API
   ↓
Node.js / Express
   ↓
Amazon RDS

The application connects to a relational database and performs SQL queries.

A serverless architecture could look like:

Client
   ↓
API Gateway
   ↓
Lambda
   ↓
DynamoDB

Here, the database fits naturally into an event-driven or serverless architecture.

Neither architecture is automatically better.

The workload determines which one makes more sense.

What about MongoDB?

This is especially relevant to me because my backend experience includes MongoDB.

MongoDB uses a document-oriented model:

{
  name: "Sahinur",
  email: "user@example.com",
  skills: [
    "Node.js",
    "Express",
    "MongoDB"
  ]
}

This can be very convenient for applications where the data naturally fits into documents.

It also feels familiar when working with JavaScript and Node.js.

That experience made me realize something important:

Database choice isn't only about technical specifications.

Developer experience, existing knowledge, application requirements, and team expertise all matter.

How I would make the decision

If I were starting a new AWS backend, I'd ask these questions first.

1. Do I need complex relationships?

If the application heavily depends on relationships and joins, I'd lean toward a relational database such as RDS.

2. Are my access patterns predictable?

If I know exactly how the application will read and write data and need highly scalable low-latency access, DynamoDB becomes more attractive.

3. Do I need SQL?

If the team relies heavily on SQL queries, reporting, and relational data analysis, RDS is likely the easier choice.

4. Is the application serverless?

If I'm building around Lambda and API Gateway, DynamoDB can fit naturally into that architecture.

5. What does the team already know?

A technically powerful database isn't useful if the team doesn't understand how to model and operate it effectively.

A simple comparison

Factor	RDS	DynamoDB
Database model	Relational	NoSQL
Query model	SQL	Key/index based
Joins	Yes	No traditional joins
Transactions	Strong relational transactions	Supported, but different model
Scaling	Managed, but capacity planning still matters	Designed for large-scale automatic scaling
Schema	Structured	Flexible
Best fit	Relational applications	High-scale access-pattern-driven workloads
Serverless fit	Possible	Excellent

The biggest lesson

The biggest lesson I've learned while studying different database architectures is:

Don't choose a database because it's popular. Choose it because its data model matches your application.

A database can be extremely fast and scalable but still be the wrong choice if your application's access patterns don't fit it.

Similarly, choosing a relational database when your application has simple key-value access patterns can introduce unnecessary complexity.

What I'd do before committing

Before choosing a database, I'd write down the application's most important operations:

1. Create user
2. Get user by ID
3. Get user's orders
4. Create order
5. Update order
6. Get latest orders
7. Generate reports

Then I'd evaluate how naturally each database handles those operations.

This exercise can reveal more than a generic database comparison ever will.

Final takeaway

For me, the RDS vs DynamoDB discussion isn't really about which service is better.

It's about matching the database model to the application.

If I need relational data, SQL, joins, and complex queries, RDS is a strong candidate.

If I have predictable access patterns and need highly scalable, low-latency NoSQL access, DynamoDB is worth serious consideration.

And my experience with MongoDB has reinforced the same principle:

The right database is the one that makes your application's most important operations simple, reliable, and scalable.

That's the way I now approach database decisions when designing backend systems on AWS.

How I Would Structure a Node.js Backend on AWS: EC2 vs Lambda

Sahinur — Thu, 10 Sep 2026 18:00:00 +0000

When building a backend on AWS, one of the first decisions is not which framework to use.

It's where and how the backend should run.

As a Node.js developer, I've worked with Express-based APIs and AWS EC2, and one question that keeps coming up is:

Should I run my backend on a traditional server, or move toward a serverless architecture such as API Gateway + Lambda?

There isn't one answer for every application.

The right choice depends on traffic, workload, architecture, team experience, and operational requirements.

Here's how I think about the decision.

The requirement

For a typical Node.js backend, I usually need:

REST APIs
Authentication and authorization
MongoDB or another database
File uploads
Background processing
Logging and monitoring
Reliable deployment
The ability to handle changing traffic

A simple backend might look like:

Client
   ↓
Node.js + Express API
   ↓
MongoDB

When deploying this to AWS, there are several ways to run it.

Options I considered

Option A: EC2 + Node.js + Express

This is the traditional approach.

Client
   ↓
Nginx
   ↓
AWS EC2
   ↓
Node.js + Express
   ↓
MongoDB

The biggest advantage is control.

I can manage:

The operating system
Node.js process
PM2
Nginx
Networking
Installed dependencies
Background processes

It's also a straightforward model for developers who already understand traditional server deployment.

But that control also creates more responsibility.

I need to think about:

Server maintenance
Scaling
Memory usage
Process management
Security updates
Monitoring
High availability

I've seen this firsthand with production Node.js applications: the application itself may be working correctly, but server resources such as memory can become a problem under real traffic.

Option B: API Gateway + Lambda

The serverless approach changes the architecture:

Client
   ↓
API Gateway
   ↓
Lambda
   ↓
Database

Instead of keeping a Node.js server running continuously, individual Lambda functions execute in response to requests.

This can reduce the amount of server infrastructure I need to manage.

It can also make automatic scaling much easier for workloads that aren't constantly running at high utilization.

But serverless introduces its own considerations.

For example:

Cold starts
Execution limits
Function design
Database connection management
Logging
Local development
Vendor-specific architecture

So serverless isn't automatically "better."

Option C: ECS / Fargate

Another option is containerizing the Node.js application:

Client
   ↓
Load Balancer
   ↓
ECS / Fargate
   ↓
Node.js + Express
   ↓
MongoDB

This gives me many of the benefits of containers without requiring me to manage the underlying EC2 instances directly.

For larger applications or teams already using Docker, this can be a very attractive middle ground.

The architecture I'd choose

For a small-to-medium Node.js API with predictable traffic, EC2 can still be a practical choice.

A basic architecture could be:

                 ┌──────────────┐
                 │    Client    │
                 └──────┬───────┘
                        │
                        ▼
                 ┌──────────────┐
                 │    Nginx     │
                 └──────┬───────┘
                        │
                        ▼
              ┌──────────────────┐
              │     AWS EC2      │
              │                  │
              │ Node.js +        │
              │ Express + PM2    │
              └────────┬─────────┘
                       │
                       ▼
                 ┌──────────────┐
                 │   MongoDB    │
                 └──────────────┘

This architecture is simple, familiar, and gives me direct control over the application environment.

But I wouldn't automatically use it for every new project.

If the application has highly variable traffic, independent functions, or a strong serverless requirement, I'd seriously consider API Gateway + Lambda.

The trade-offs

The interesting part isn't choosing the "most modern" AWS service.

It's understanding what you're giving up and what you're gaining.

Factor	EC2	Lambda
Server management	Higher	Lower
Scaling	Requires configuration	Automatic
Long-running processes	Good	Limited
Cold starts	No	Possible
Infrastructure control	High	Lower
Deployment model	Server/application	Function-based
Operational complexity	Higher	Different, not zero
Best fit	Persistent services	Event/API-driven workloads

This is why I don't think "serverless means no problems."

It means you're moving some operational responsibilities away from servers and taking on a different set of architectural considerations.

A lesson from production

One thing that changed how I think about architecture is realizing that application behavior matters just as much as infrastructure choice.

For example, a Node.js API can run perfectly on a powerful EC2 instance and still have problems if an endpoint loads thousands of MongoDB documents into memory.

Likewise, moving that endpoint to Lambda wouldn't automatically fix inefficient data processing.

The architecture can help, but good application design still matters.

What I'd change next time

If I were starting a new AWS backend today, I'd make the architecture decision based on the workload instead of choosing a service simply because it's popular.

I'd ask:

How predictable is the traffic?
Does the application need long-running processes?
How much infrastructure do we want to manage?
What are the database access patterns?
How important is automatic scaling?
What are the latency requirements?
Does the team already have experience with containers or serverless?

Only after answering those questions would I choose between EC2, ECS/Fargate, and Lambda.

Final takeaway

There is no single AWS architecture that is perfect for every Node.js application.

EC2, ECS/Fargate, and Lambda solve different problems.

For me, the important lesson is to start with the application's requirements and workload, then choose the AWS service that fits.

The goal isn't to use the most services.

The goal is to build a system that is reliable, maintainable, scalable, and appropriate for the actual workload.

And that's one of the things I'm continuing to learn as I work deeper with AWS and backend engineering.

A Node.js Backend Memory Issue That Only Showed Up in Production

Sahinur — Wed, 02 Sep 2026 04:27:15 +0000

One of the most frustrating backend problems is an issue that works perfectly in development and staging, but starts failing when real production traffic arrives.

I recently worked with a Node.js + Express + MongoDB backend running on an AWS EC2 instance, where we started seeing unexpected memory usage in production.

The application was working normally at first. Then, as traffic increased, the server's memory usage kept climbing until the Node.js process eventually crashed.

Here's how I approached the problem.

The setup

The backend was built with:

Node.js
Express.js
MongoDB
AWS EC2
PM2 for process management

The basic architecture looked like this:

Client
   ↓
Nginx
   ↓
Node.js + Express
   ↓
MongoDB

The application handled API requests normally during development and staging.

The EC2 instance also had enough resources for the expected workload, so initially there was no obvious reason for the server to run out of memory.

What went wrong

After the application had been running under production traffic for some time, memory usage started increasing.

We noticed the EC2 instance's memory utilization gradually climbing instead of returning to its normal level after requests were completed.

Eventually, the Node.js process became unstable and crashed.

A representative error looked like this:

<--- Last few GCs --->

FATAL ERROR: Reached heap limit
Allocation failed - JavaScript heap out of memory

Node.js v20.x

At first, increasing the server resources seemed like the easiest solution.

But that would only hide the actual problem.

The important question was:

Why was the application continuously consuming more memory?

Why staging didn't catch it

This was one of the interesting parts of the problem.

The application worked correctly in staging because the environment was significantly different from production.

The main differences were:

Lower traffic
Fewer concurrent requests
Smaller datasets
Shorter test duration
Less realistic production usage patterns

A memory problem doesn't necessarily appear immediately.

An application can use a little more memory after every request without causing any visible problem during a short test.

For example:

Request 1  →  500 MB
Request 100 → 550 MB
Request 1,000 → 700 MB
Request 10,000 → 1.2 GB

Eventually, the server reaches its available memory limit.

This made the problem difficult to reproduce in staging.

How I investigated the problem

The first thing I did was check the EC2 instance's resource usage.

I used system-level monitoring to check memory consumption:

free -h

I also checked running processes:

top

and inspected the Node.js process managed by PM2:

pm2 status
pm2 monit

The important observation was that CPU usage wasn't consistently high.

Memory usage, however, continued increasing.

That changed the direction of the investigation.

Instead of asking:

"Why is the API slow?"

I started asking:

"What is keeping objects in memory longer than expected?"

Looking at the application

I started reviewing endpoints that processed large amounts of data.

One suspicious pattern was fetching a large dataset from MongoDB and keeping the entire result in memory before processing it.

For example:

const users = await User.find({
  status: "active"
});

const result = users.map(user => {
  // process user
});

return res.json(result);

This can become expensive when the collection grows.

If MongoDB returns thousands or tens of thousands of documents, Node.js has to hold those JavaScript objects in memory.

The problem becomes worse if multiple requests are doing the same thing concurrently.

The fix

The first improvement was to avoid loading unnecessary records into memory.

Instead of retrieving everything, we introduced pagination:

const page = Number(req.query.page) || 1;
const limit = 50;

const users = await User.find({
  status: "active"
})
  .skip((page - 1) * limit)
  .limit(limit)
  .lean();

Now the application only processes a limited number of documents per request.

We also reviewed queries to make sure we weren't returning fields that the API didn't actually need.

For example:

const users = await User.find({
  status: "active"
})
  .select("name email status")
  .limit(50)
  .lean();

This reduced unnecessary data being loaded into the Node.js process.

Monitoring after the fix

After making the changes, I continued monitoring the application rather than assuming the problem was solved.

The important thing was to observe memory usage over a longer period under realistic traffic.

The expected pattern was:

Memory
  │
  │       ╭──╮      ╭──╮
  │    ╭──╯  ╰──╮ ╭─╯  ╰──
  │────╯────────╰─╯──────────
  │
  └────────────────────────── Time

Instead of continuously climbing, memory usage should stabilize within a reasonable range.

I also kept PM2 monitoring in place so that unexpected process behavior could be detected quickly.

What I learned

This incident taught me an important lesson:

A backend can work perfectly in development and still have serious resource problems in production.

For Node.js applications, I now pay much more attention to:

Large MongoDB queries
Pagination
Selecting only required fields
Processing large datasets
Concurrent requests
Memory usage over time
Production-like load testing

I also learned that simply increasing the EC2 instance size isn't always the correct solution.

More RAM can postpone the crash, but it doesn't necessarily fix the underlying problem.

What I'd do differently

If I were setting up the same system again, I'd introduce memory monitoring and load testing much earlier.

I'd also test endpoints with production-sized datasets instead of relying only on small staging datasets.

The biggest takeaway for me was simple:

Don't just test whether your API works. Test how it behaves after running for a long time under realistic load.

That's where some of the most interesting backend problems reveal themselves.

Book Review: "Software Development From A to Z" — The Book I Wish I'd Read Before My First Dev Job

Sahinur — Thu, 20 Aug 2026 09:27:19 +0000

Most programming books teach you one thing deeply: a language, a framework, a database. Very few explain how all of those things fit together inside an actual team, or why the product manager keeps asking you for estimates, or what the designer is doing for three weeks before you get a single mockup.

Software Development From A to Z by Olga Filipova and Rui Vilão (Apress, 2018) tries to fill exactly that gap. I finished it recently, and here's my take.

What the book is about

The premise is simple: follow one small team building one product, an online learning platform, from "hey, I have an idea" all the way to maintaining it in production. Each chapter is narrated from the seat of a different role:

Having an idea (and turning it into an MVP)
Roles, responsibilities, Scrum and Kanban
The product manager: requirements and deadlines
The designer: user-centered design
The backend engineer
The frontend engineer
The QA engineer
DevOps: going live
Maintaining and improving what you shipped

It's around 300 pages, and it moves fast.

What I liked

The running example actually runs. A lot of "big picture" books stay abstract. This one doesn't. You watch the requirements table get written, the personas get invented, the Spring Boot backend get scaffolded, the Vue/Nuxt frontend get wired up, and the whole thing get deployed to Heroku through Travis CI. Seeing the same feature (user registration, for example) pass through the hands of five different roles is the single most valuable thing in the book.

It's honest about messy reality. There's a whole section admitting that role titles mean different things at different companies and that it's normal to be confused. The Scrum-vs-Kanban comparison doesn't pick a winner; it rates each on a strictness scale and then shows the hybrid the authors actually use. That felt refreshingly practical.

The interviews. Several chapters include short interviews with a real product manager, designer, and DevOps engineer. The "biggest screw-up / biggest success" questions in particular give the book a human texture that pure tutorials lack.

The MVP chapter. If you've never internalized the difference between delivering a wheel and delivering a skateboard, chapter 1 will fix that. It's the clearest explanation of "minimum viable" I've read in a book aimed at developers.

What I didn't love

Depth is uneven. The backend and frontend chapters go fairly deep into code, while the DevOps chapter stays more conceptual. If you're reading for the technical content, you'll find the engineering chapters meatier than the rest. If you're reading for the big picture, the code may feel like a detour.

The stack is dated. Java/Spring Boot and Vue 2 with Nuxt are still perfectly reasonable choices, but Travis CI and Heroku's free tier are not what they were in 2018. Read the tooling chapters for the ideas (CI on every push, automated deploys from the main branch, platform-as-a-service tradeoffs), not the specific buttons to click.

It's an overview, by design. No chapter will make you a designer or a DevOps engineer. That's fine, but set expectations accordingly: this is the map, not the territory.

Who should read it

Bootcamp grads and juniors about to join their first team. This will decode half the meetings you'll sit in.
Career changers (QA, support, design, product) who want to understand what engineers actually do all day.
Solo founders planning to build an MVP and wondering which hats they'll need to wear.
Experienced devs who want something to hand to non-technical colleagues. It's approachable without being condescending.

Skip it if you already have a few years on cross-functional teams; you'll nod along but learn little.

Verdict

4 / 5. Not a reference book, and not the place to learn any single technology. But as a guided tour through how software gets made, and especially through how the people who make it depend on each other, it's one of the better ones out there. I'd put it on the same shelf as The Phoenix Project, just less narrative and more hands-on.

Have you read it? What's your go-to "how teams actually work" book for new developers? Drop it in the comments.

Cloud Computing for Beginners: A Simple Guide for Students & New Developers ☁️

Sahinur — Mon, 12 Jan 2026 12:17:59 +0000

Cloud computing is no longer just a buzzword — it’s a core skill for modern developers. Whether you’re a student, a beginner, or someone curious about how applications scale globally, understanding cloud basics can open many doors.

In this article, I’ll explain cloud computing in simple terms, without heavy jargon, and help you take your first step into the cloud.

What Is Cloud Computing? (In Simple Words)

Cloud computing means using computing resources over the internet instead of your own computer or physical servers.

Instead of:

Buying servers
Maintaining hardware
Managing storage manually

You rent:

Servers
Storage
Databases
Networking

👉 All provided by cloud platforms like Microsoft Azure, AWS, and Google Cloud.

Think of the cloud like renting electricity instead of building your own power plant.

Why Should Beginners Learn Cloud Computing?

Here’s why cloud skills matter, even if you’re just starting:

🚀 High Industry Demand

Most modern applications — from startups to enterprises — run on cloud infrastructure.

💼 Career Opportunities

Cloud knowledge is useful for roles like:

Software Engineer
DevOps Engineer
Cloud Engineer

📈 Easy Scalability

Cloud platforms allow applications to scale from 10 users to millions without rebuilding everything.

Core Cloud Concepts You Should Know

1️⃣ Compute

Compute services run your applications and code.

Examples:

Azure Virtual Machines
Azure App Service

2️⃣ Storage

Storage services keep files, images, backups, and logs.

Examples:

Azure Blob Storage

3️⃣ Networking

Networking connects cloud resources securely.

Examples:

Virtual Networks
Load Balancers

4️⃣ Databases

Managed databases remove the pain of setup and maintenance.

Examples:

Azure SQL Database

Cloud Service Models Explained

Understanding these models is very important.

🧱 IaaS – Infrastructure as a Service

You manage:

Operating system
Application software

Cloud provider manages:

Physical hardware

Example:

Virtual Machines

🧩 PaaS – Platform as a Service

You focus on:

Writing code

Cloud provider manages:

Servers
OS
Scaling

Example:

Azure App Service

☁️ SaaS – Software as a Service

You simply use the software.

Examples:

Microsoft 365
Gmail

Why Microsoft Learn Is Perfect for Beginners

Learning cloud from documentation alone can be overwhelming.

That’s why Microsoft Learn is a great starting point:

✅ Free
✅ Beginner-friendly
✅ Hands-on learning
✅ No credit card required

You learn by doing, not just reading.

🔗 Start learning here:

https://learn.microsoft.com?wt.mc_id=studentamb_502522

A Simple Beginner Learning Path

If you’re starting today, follow this order:

Cloud fundamentals
Azure basics
Compute & storage concepts
Deploy a simple application

Consistency matters more than speed.

Final Thoughts

You don’t need to be an expert to start learning cloud computing.

All you need:

Curiosity
Consistency
The right learning resources

Every cloud expert was once a beginner.

Start small, stay consistent, and keep building ☁️🚀

About the Author

Sahinur Islam

Full-Stack Developer | Cloud & DevOps Enthusiast

Microsoft Learn Student Ambassador – Registered Member

When Should You Use AWS Lambda? A Beginner's Perspective

Sahinur — Sun, 11 Jan 2026 11:32:51 +0000

If you're new to cloud computing, you've probably heard the buzz around AWS Lambda and serverless architecture. But here's the million-dollar question: When should you actually use it?

In this article, I'll break down AWS Lambda in simple terms, explain when it's the perfect choice, and when you might want to look elsewhere. Let's dive in!

🤔 What is AWS Lambda, Anyway?

AWS Lambda is Amazon's serverless compute service. In simple terms:

You write code, upload it to Lambda, and AWS runs it for you — no servers to manage, no infrastructure to worry about.

Lambda only runs your code when something triggers it (like an API request, a file upload, or a database change). When there's nothing to do, Lambda sits idle — and you pay nothing.

Key characteristics:

Event-driven: Runs only when triggered
Auto-scaling: Handles 1 request or 10,000 automatically
Pay-per-use: Charged only for execution time (in milliseconds)
Zero server management: AWS handles everything

✅ When Should You Use AWS Lambda?

Here are the scenarios where Lambda truly shines:

1. API Backends & Microservices

Building a REST API? Lambda + API Gateway is a killer combo.

// Simple Lambda function for an API endpoint
exports.handler = async (event) => {
    const userId = event.pathParameters.id;

    // Fetch user from database
    const user = await getUser(userId);

    return {
        statusCode: 200,
        body: JSON.stringify(user)
    };
};

Perfect for:

Mobile app backends
Web application APIs
Microservices architecture

2. File Processing

When users upload files to S3, Lambda can automatically process them.

Use cases:

Image resizing/compression
Video transcoding
PDF generation
CSV/Excel data processing

# Triggered when file is uploaded to S3
def lambda_handler(event, context):
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    # Process the uploaded file
    process_file(bucket, key)

    return {'statusCode': 200}

3. Scheduled Tasks (Cron Jobs)

Need to run something every hour? Every day at midnight? Lambda + EventBridge is your friend.

Examples:

Daily database cleanup
Sending scheduled reports
Periodic data synchronization
Health checks

# Using AWS SAM template
Events:
  ScheduledEvent:
    Type: Schedule
    Properties:
      Schedule: rate(1 hour)  # Runs every hour

4. Real-time Data Processing

Process streaming data from Kinesis or DynamoDB Streams.

Use cases:

IoT sensor data processing
Real-time analytics
Log aggregation
Change data capture (CDC)

5. Webhooks & Event Handlers

Respond to external events instantly.

Examples:

Payment gateway callbacks (Stripe, PayPal)
GitHub/GitLab webhooks for CI/CD
Slack bot commands
Third-party API integrations

6. Chatbots & Voice Assistants

Lambda powers many conversational interfaces.

Integrations:

Amazon Lex (chatbots)
Alexa Skills
Telegram/Discord bots

7. Authentication & Authorization

Custom auth logic for your applications.

Use cases:

JWT token validation
Custom authorizers for API Gateway
User signup/signin flows
OAuth callback handlers

❌ When Should You NOT Use AWS Lambda?

Lambda isn't a silver bullet. Here's when you should consider alternatives:

1. Long-Running Processes

Lambda has a 15-minute timeout limit. If your task takes longer, Lambda isn't the right choice.

Better alternatives:

AWS Fargate
AWS Batch
EC2 instances

2. Consistent High Traffic

If your application runs 24/7 with consistent high traffic, Lambda might be more expensive than EC2 or containers.

Rule of thumb: Calculate your costs. Lambda charges per millisecond, while EC2 is hourly. For always-on workloads, EC2 or Fargate often wins.

3. Stateful Applications

Lambda functions are stateless. Each invocation is independent with no shared memory.

Not ideal for:

WebSocket connections (though API Gateway helps)
Session-based applications
Applications requiring persistent connections

4. Latency-Critical Applications

Cold starts can add 100ms to several seconds of latency when Lambda spins up a new container.

Mitigations:

Provisioned Concurrency (costs extra)
Keep functions warm with scheduled pings
Use smaller deployment packages

5. Heavy Compute Workloads

Lambda maxes out at 10GB RAM and 6 vCPUs. For ML training, video rendering, or heavy computation, look elsewhere.

Better alternatives:

EC2 with GPU instances
AWS SageMaker
AWS Batch

6. Complex Monolithic Applications

Lambda works best with small, focused functions. Migrating a large monolithic app to Lambda is painful and often counterproductive.

📊 Quick Decision Matrix

Scenario	Use Lambda?	Alternative
REST API with variable traffic	✅ Yes	-
Image processing on upload	✅ Yes	-
Cron jobs (< 15 min)	✅ Yes	-
Real-time webhooks	✅ Yes	-
24/7 high-traffic web server	❌ No	EC2, Fargate
ML model training	❌ No	SageMaker, EC2
Long-running batch jobs	❌ No	AWS Batch
WebSocket game server	❌ No	EC2, Fargate
Sub-10ms latency required	⚠️ Maybe	EC2

💰 Understanding Lambda Pricing

Lambda's pricing is simple but can surprise you:

You pay for:

Number of requests: $0.20 per 1 million requests
Duration: Based on memory allocated × execution time

Free tier (every month):

1 million requests
400,000 GB-seconds of compute time

Example calculation:

Function specs:
- Memory: 512MB
- Avg execution time: 200ms
- Monthly invocations: 5 million

Cost calculation:
- Requests: (5M - 1M free) × $0.20/1M = $0.80
- Compute: 5M × 0.2s × 0.5GB = 500,000 GB-s
- GB-s cost: (500,000 - 400,000 free) × $0.0000166667 = $1.67

Total: ~$2.47/month

🚀 Getting Started: Your First Lambda Function

Ready to try Lambda? Here's a quick start:

Step 1: Create the Function

// index.js
exports.handler = async (event) => {
    const name = event.queryStringParameters?.name || 'World';

    return {
        statusCode: 200,
        headers: {
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({
            message: `Hello, ${name}!`,
            timestamp: new Date().toISOString()
        })
    };
};

Step 2: Deploy Using AWS CLI

# Zip your code
zip function.zip index.js

# Create the function
aws lambda create-function \
    --function-name HelloWorld \
    --runtime nodejs20.x \
    --handler index.handler \
    --role arn:aws:iam::YOUR_ACCOUNT:role/lambda-role \
    --zip-file fileb://function.zip

Step 3: Test It

aws lambda invoke \
    --function-name HelloWorld \
    --payload '{"queryStringParameters": {"name": "Developer"}}' \
    response.json

cat response.json
# Output: {"message":"Hello, Developer!","timestamp":"2024-01-15T10:30:00.000Z"}

🎯 Best Practices for Beginners

Keep functions small and focused — One function, one job
Minimize cold starts — Use smaller packages, avoid VPC unless necessary
Set appropriate timeouts — Don't use 15 minutes if 10 seconds is enough
Use environment variables — Never hardcode secrets
Monitor with CloudWatch — Set up alarms for errors and duration
Use Layers for dependencies — Share common code across functions

🏁 Conclusion

AWS Lambda is a powerful tool, but it's not for everything. Use it when:

✅ Your workload is event-driven
✅ Traffic is variable or unpredictable
✅ Tasks complete in under 15 minutes
✅ You want zero infrastructure management

Avoid it when:

❌ You need consistent, high-performance compute
❌ Tasks run longer than 15 minutes
❌ Sub-millisecond latency is critical
❌ You're running a stateful monolith

Start small, experiment, and you'll quickly discover where Lambda fits in your architecture. The serverless journey is worth it!

📚 Resources

What's your experience with AWS Lambda? Drop a comment below! 👇

If you found this helpful, please ❤️ and follow for more cloud content!

aws #lambda #serverless #beginners #cloud

AWS IAM basics explained with real examples

Sahinur — Sun, 11 Jan 2026 10:57:07 +0000

When you first start working with Amazon Web Services, one of the most critical services you'll encounter is Identity and Access Management (IAM). Think of IAM as the security guard of your AWS account—it controls who can enter, what rooms they can access, and what actions they can perform.

In this guide, we'll break down IAM concepts with practical examples that you can immediately apply to your projects.

What is AWS IAM?

AWS Identity and Access Management (IAM) is a free service that allows you to securely manage access to AWS resources. With IAM, you can control:

Who can access your resources (authentication)
What they can do with those resources (authorization)

Every interaction with AWS—whether through the console, CLI, or SDK—goes through IAM for permission checks.

Core IAM Components

1. Users

An IAM user represents a person or application that interacts with AWS. Each user has unique credentials.

Real Example: Creating a Developer User

Imagine you're onboarding a new developer named Sarah to your team. She needs access to deploy applications to EC2 and view logs in CloudWatch.

{
  "UserName": "sarah-developer",
  "Tags": [
    {
      "Key": "Department",
      "Value": "Engineering"
    },
    {
      "Key": "Role",
      "Value": "Backend Developer"
    }
  ]
}

Best Practice: Never use the root account for daily tasks. Create individual IAM users for each team member.

2. Groups

Groups are collections of users. Instead of attaching policies to each user individually, you attach them to a group, and all members inherit those permissions.

Real Example: Team-Based Access Structure

├── Developers (Group)
│   ├── sarah-developer
│   ├── john-developer
│   └── Policy: DeveloperAccess
│
├── DevOps (Group)
│   ├── mike-devops
│   └── Policy: DevOpsFullAccess
│
└── Finance (Group)
    ├── lisa-finance
    └── Policy: BillingReadOnly

When Sarah moves from Development to DevOps, you simply remove her from the Developers group and add her to DevOps. No policy changes needed.

3. Roles

Roles are similar to users but are meant to be assumed temporarily by users, applications, or AWS services. They don't have permanent credentials.

Real Example: EC2 Instance Accessing S3

Your application running on EC2 needs to upload images to an S3 bucket. Instead of storing AWS credentials in your code (a security risk), you create a role:

{
  "RoleName": "EC2-S3-Upload-Role",
  "AssumeRolePolicyDocument": {
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Principal": {
          "Service": "ec2.amazonaws.com"
        },
        "Action": "sts:AssumeRole"
      }
    ]
  }
}

The EC2 instance automatically gets temporary credentials to access S3—no hardcoded keys required.

4. Policies

Policies are JSON documents that define permissions. They specify what actions are allowed or denied on which resources.

Policy Structure:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow | Deny",
      "Action": "service:action",
      "Resource": "arn:aws:service:region:account:resource",
      "Condition": { }
    }
  ]
}

Real-World Policy Examples

Example 1: S3 Read-Only Access to a Specific Bucket

A mobile app needs to read images from a bucket called my-app-images:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::my-app-images",
        "arn:aws:s3:::my-app-images/*"
      ]
    }
  ]
}

Example 2: Developer Access with Restrictions

Developers can manage EC2 instances but cannot terminate production servers:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "ec2:*",
      "Resource": "*"
    },
    {
      "Effect": "Deny",
      "Action": "ec2:TerminateInstances",
      "Resource": "*",
      "Condition": {
        "StringEquals": {
          "ec2:ResourceTag/Environment": "production"
        }
      }
    }
  ]
}

Example 3: Time-Based Access

Contractors can only access resources during business hours:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "s3:*",
      "Resource": "*",
      "Condition": {
        "DateGreaterThan": {
          "aws:CurrentTime": "2025-01-01T09:00:00Z"
        },
        "DateLessThan": {
          "aws:CurrentTime": "2025-12-31T18:00:00Z"
        }
      }
    }
  ]
}

Example 4: Lambda Function Accessing DynamoDB

A Lambda function that reads and writes to a specific DynamoDB table:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "dynamodb:GetItem",
        "dynamodb:PutItem",
        "dynamodb:UpdateItem",
        "dynamodb:Query"
      ],
      "Resource": "arn:aws:dynamodb:ap-south-1:123456789012:table/UserProfiles"
    },
    {
      "Effect": "Allow",
      "Action": [
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "arn:aws:logs:*:*:*"
    }
  ]
}

Understanding Policy Evaluation

When a request is made, AWS evaluates policies in this order:

Explicit Deny — If any policy explicitly denies the action, access is denied (highest priority)
Explicit Allow — If a policy allows the action and nothing denies it, access is granted
Implicit Deny — If no policy mentions the action, access is denied by default

Request → Check Deny → Check Allow → Default Deny
              ↓             ↓              ↓
           DENIED       ALLOWED        DENIED

Types of IAM Policies

Policy Type	Description	Use Case
AWS Managed	Created and maintained by AWS	Quick setup with common permissions
Customer Managed	Created by you	Custom permissions for your organization
Inline	Embedded directly in a user, group, or role	Strict one-to-one relationship

Recommendation: Use customer managed policies for reusability and easier maintenance.

Security Best Practices

1. Enable Multi-Factor Authentication (MFA)

Always enable MFA for the root account and privileged users.

2. Follow the Principle of Least Privilege

Grant only the permissions required to perform a task—nothing more.

// ❌ Bad: Too permissive
{
  "Effect": "Allow",
  "Action": "*",
  "Resource": "*"
}

// ✅ Good: Specific and limited
{
  "Effect": "Allow",
  "Action": "s3:GetObject",
  "Resource": "arn:aws:s3:::my-bucket/public/*"
}

3. Use Roles Instead of Access Keys

For applications running on AWS services, always use IAM roles instead of embedding access keys.

4. Rotate Credentials Regularly

Set up automatic rotation for access keys and review unused credentials.

5. Use IAM Access Analyzer

This tool helps identify resources shared with external entities and validates policies.

Common IAM Scenarios

Scenario 1: Cross-Account Access

Your production and development accounts are separate. Developers need to access production S3 for debugging.

Solution: Create a role in production that developers can assume from the development account.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "AWS": "arn:aws:iam::DEV_ACCOUNT_ID:root"
      },
      "Action": "sts:AssumeRole",
      "Condition": {
        "Bool": {
          "aws:MultiFactorAuthPresent": "true"
        }
      }
    }
  ]
}

Scenario 2: Service-Linked Roles

Some AWS services create roles automatically to perform actions on your behalf. For example, when you use Elastic Load Balancing, AWS creates AWSServiceRoleForElasticLoadBalancing.

Scenario 3: Federated Access

Your company uses Google Workspace or Microsoft Azure AD. Instead of creating IAM users, you can federate identity:

Corporate Identity Provider → AWS STS → Temporary Credentials → AWS Access

Debugging IAM Issues

When access is denied, check these areas:

Policy Simulator — Test policies before applying them
CloudTrail Logs — See exactly what was denied and why
IAM Access Advisor — View which services a user actually accessed

Common Error:

An error occurred (AccessDenied) when calling the PutObject operation: Access Denied

Debugging Steps:

Verify the policy is attached to the user/role
Check for explicit deny statements
Ensure the resource ARN matches exactly
Verify the correct region is specified

Quick Reference: Common IAM Actions

Service	Common Actions
S3	`s3:GetObject`, `s3:PutObject`, `s3:DeleteObject`, `s3:ListBucket`
EC2	`ec2:RunInstances`, `ec2:StopInstances`, `ec2:TerminateInstances`
Lambda	`lambda:InvokeFunction`, `lambda:CreateFunction`, `lambda:UpdateFunctionCode`
DynamoDB	`dynamodb:GetItem`, `dynamodb:PutItem`, `dynamodb:Query`, `dynamodb:Scan`
RDS	`rds:CreateDBInstance`, `rds:DeleteDBInstance`, `rds:DescribeDBInstances`

Conclusion

AWS IAM is the foundation of security in the cloud. By understanding users, groups, roles, and policies, you can build secure applications that follow the principle of least privilege.

Start with these steps:

Create individual users for your team members
Organize users into groups based on job functions
Use roles for applications and cross-account access
Write specific policies that grant only necessary permissions
Enable MFA and regularly audit your IAM configuration

Security isn't a one-time setup—it's an ongoing practice. Regularly review your IAM policies and adjust as your application evolves.

Happy building on AWS!

Complete Guide: Deploying Node.js Application on Ubuntu VPS

Sahinur — Sun, 11 Jan 2026 10:50:28 +0000

A step-by-step guide to deploy your Node.js application with MongoDB, Nginx reverse proxy, and SSL certificate on a DigitalOcean Droplet or any Ubuntu VPS.

Prerequisites

A DigitalOcean Droplet (or any Ubuntu VPS)
A domain name (optional, but required for SSL)
SSH access to your server
Your Node.js application hosted on GitHub

Step 1: Connect to Your Server

SSH into your droplet using the root user:

ssh root@YOUR_DROPLET_IP

Replace YOUR_DROPLET_IP with your actual server IP address.

Step 2: Update System Packages

Always start by updating and upgrading your system packages:

sudo apt update && sudo apt upgrade -y

Step 3: Install Required Packages

Install Node.js, npm, and Nginx:

sudo apt install -y nodejs npm nginx

Step 4: Install Node Version Manager (NVM)

NVM allows you to manage multiple Node.js versions easily.

Download and install NVM:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.1/install.sh | bash

Load NVM into current session:

source ~/.bashrc

Install and configure Node.js 20.10.0:

nvm install 20.10.0
nvm use 20.10.0
nvm alias default 20.10.0

Verify installation:

node --version
npm --version

Step 5: Install MongoDB

MongoDB is a popular NoSQL database for Node.js applications.

Import the public key:

curl -fsSL https://www.mongodb.org/static/pgp/server-7.0.asc | sudo gpg -o /usr/share/keyrings/mongodb-server-7.0.gpg --dearmor

Create the list file for Ubuntu 22.04:

echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-7.0.gpg ] https://repo.mongodb.org/apt/ubuntu jammy/mongodb-org/7.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-7.0.list

Update packages and install MongoDB:

sudo apt update
sudo apt install -y mongodb-org

Start and enable MongoDB:

sudo systemctl start mongod
sudo systemctl enable mongod
sudo systemctl status mongod

For complete MongoDB installation instructions, refer to the official documentation.

Step 6: Setup SSH Key for GitHub

Generate an SSH key to clone your private repositories:

ssh-keygen -t rsa -b 4096 -C "your-email@example.com"

Press Enter to accept default file location and optionally set a passphrase.

Display your public key:

cat ~/.ssh/id_rsa.pub

Add the key to GitHub:

Copy the entire output
Go to GitHub → Settings → SSH and GPG keys
Click "New SSH key"
Paste your key and save

Step 7: Clone Your Repository

Navigate to your preferred directory and clone your repository:

cd /var/www
git clone git@github.com:username/repository.git
cd repository

Install dependencies:

npm install

Step 8: Configure Nginx as Reverse Proxy

Nginx will handle incoming HTTP requests and forward them to your Node.js application.

Create a new Nginx configuration file:

sudo nano /etc/nginx/sites-available/your-app-name

Add the following configuration:

server {
    listen 80;
    server_name YOUR_DOMAIN_OR_IP;

    location / {
        proxy_pass http://localhost:3000;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection 'upgrade';
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
        proxy_cache_bypass $http_upgrade;
    }
}

Replace YOUR_DOMAIN_OR_IP with your domain name or server IP address.

Enable the configuration:

sudo ln -s /etc/nginx/sites-available/your-app-name /etc/nginx/sites-enabled/

Test Nginx configuration:

sudo nginx -t

Reload Nginx:

sudo systemctl reload nginx

Step 9: Managing Your App with Tmux

Tmux keeps your application running even after you disconnect from SSH.

Tmux Commands Reference:

Command	Description
`tmux new -s session_name`	Create a new session
`tmux attach -t session_name`	Attach to existing session
`tmux ls`	List all sessions
`tmux kill-session -t session_name`	Kill a session
`tmux rename-session -t old_name new_name`	Rename a session
`Ctrl+B` then `D`	Detach from current session

Start your application:

tmux new -s myapp
cd /var/www/repository
npm start

Press Ctrl+B then D to detach and leave it running.

Step 10: Configure Firewall (UFW)

Enable and configure the firewall:

sudo ufw enable
sudo ufw allow 'Nginx Full'
sudo ufw allow OpenSSH
sudo ufw status

Step 11: Install SSL Certificate (HTTPS)

Secure your application with a free SSL certificate from Let's Encrypt.

Install Certbot:

sudo apt install certbot python3-certbot-nginx

Obtain SSL certificate:

sudo certbot --nginx -d example.com -d www.example.com

Replace example.com with your actual domain name.

Certbot will automatically configure Nginx for HTTPS and set up auto-renewal.

Test auto-renewal:

sudo certbot renew --dry-run

Quick Reference Commands

Server Management:

# Restart Nginx
sudo systemctl restart nginx

# Check Nginx status
sudo systemctl status nginx

# View Nginx error logs
sudo tail -f /var/log/nginx/error.log

# Restart MongoDB
sudo systemctl restart mongod

# Check MongoDB status
sudo systemctl status mongod

Application Management:

# Pull latest changes
cd /var/www/repository
git pull origin main

# Install new dependencies
npm install

# Restart application (inside tmux session)
tmux attach -t myapp
# Press Ctrl+C to stop, then npm start

Alternative: Using PM2 (Recommended for Production)

PM2 is a production process manager for Node.js applications.

Install PM2:

npm install -g pm2

Start your application:

pm2 start app.js --name "your-app-name"

PM2 Commands:

pm2 list              # List all processes
pm2 restart app-name  # Restart application
pm2 stop app-name     # Stop application
pm2 logs              # View logs
pm2 startup           # Configure PM2 to start on boot
pm2 save              # Save current process list

Troubleshooting

Application not accessible:

Check if the app is running: tmux attach -t myapp
Verify Nginx config: sudo nginx -t
Check firewall: sudo ufw status
Review Nginx logs: sudo tail -f /var/log/nginx/error.log

MongoDB connection issues:

Check if MongoDB is running: sudo systemctl status mongod
Review MongoDB logs: sudo tail -f /var/log/mongodb/mongod.log

SSL certificate issues:

Ensure domain DNS points to your server IP
Check Certbot logs: sudo certbot certificates
Renew manually: sudo certbot renew

Conclusion

Your Node.js application is now deployed with:

✅ Latest Node.js version via NVM
✅ MongoDB database
✅ Nginx reverse proxy
✅ SSL/HTTPS encryption
✅ Firewall protection
✅ Process management with Tmux or PM2

For production applications, consider implementing additional security measures, setting up automated backups, and configuring monitoring tools.

Last updated: January 2026

Getting Started with AWS in 2026 – A Practical Beginner's Guide 🚀

Sahinur — Sun, 11 Jan 2026 06:14:20 +0000

Getting Started with AWS in 2026 – The Practical Way for Developers

Hey DEV community! 👋

If you're a developer looking to level up in 2026, AWS (Amazon Web Services) is still the #1 cloud platform — powering everything from startups to giants like Netflix, Airbnb, and Spotify.

But AWS can feel overwhelming with 200+ services. Where do you even start?

This guide is for absolute beginners who want a clear, practical path — not just theory. We'll cover account creation (with security!), the most important services for developers, the famous Free Tier, and your first experiments.

Let's dive in! ☁️

Why Learn AWS in 2026? (Quick Reality Check)

Pay-as-you-go → No huge upfront costs — pay only for what you use
Massive scalability → Go from 1 to 1 million users overnight
Global reach → Deploy apps in regions close to your users
Free Tier → Still very generous in 2026 (12 months of limited free usage on many services)
Huge job market → AWS skills = better opportunities in backend, DevOps, full-stack, AI/ML
Generative AI boom → Services like Amazon Bedrock make it easier than ever to build AI apps

Step 1: Create Your AWS Account (Do It Right!)

Go to https://aws.amazon.com/ and click Create an AWS Account
Use a real email (you'll need it for recovery)
Choose the basic support plan (free)
Enter your payment info → AWS requires a card even for Free Tier (they charge $1 then refund it for verification)
Very important — Verify your phone number

Security First (Don't Skip This in 2026!)

After signup, immediately:

Enable MFA on your root account (use authenticator app like Google Authenticator)
Never use root account for daily work → Create an IAM user with admin rights instead
Follow least-privilege principle from day 1

Pro tip: Use AWS Organizations later if you plan to create multiple accounts (dev/test/prod).

Step 2: Understand the AWS Free Tier (What’s Actually Free?)

AWS Free Tier in 2026 still gives you:

750 hours/month of t3.micro or t2.micro EC2 instances (~1 year)
5 GB of Amazon S3 standard storage
750 hours/month of RDS (db.t3.micro or similar)
1 million Lambda requests/month
400,000 GB-seconds of Lambda compute
Many other services free forever (DynamoDB, SNS, etc.)

Warning: Always delete resources after experimenting — forgotten EC2/RDS instances can cost money after free tier expires!

Step 3: Core AWS Services Every Developer Should Know (2026 Edition)

Here are the must-know services for most developers:

Compute

Amazon EC2 → Virtual servers (your Linux/Windows machines in the cloud)
AWS Lambda → Serverless — run code without managing servers
Elastic Beanstalk → Easy deployment (upload your app and AWS handles the rest)

Storage

Amazon S3 → Object storage (think unlimited Dropbox for apps)
Amazon EBS → Block storage (like SSD for your EC2 instances)

Databases

Amazon RDS → Managed SQL databases (MySQL, PostgreSQL, etc.)
Amazon DynamoDB → NoSQL — super fast and scalable

Networking & Security

Amazon VPC → Your private network in AWS
AWS IAM → Control who can do what (super important!)
AWS CloudTrail → Audit logs (who did what)

Developer Tools

AWS CodeCommit → Git repositories
AWS CodePipeline → CI/CD
AWS CodeBuild → Build your code

Bonus (Hot in 2026): Generative AI

Amazon Bedrock → Build AI apps with foundation models (Claude, Llama, Stable Diffusion, etc.) — no need to manage your own GPUs

Step 4: Your First Hands-On Challenge (Do This Today!)

Log into the AWS Management Console
Go to EC2 → Launch Instance
- Choose Amazon Linux 2023 (free tier eligible)
- t3.micro instance
- Create a new key pair (save the .pem file!)
- Allow SSH traffic (port 22)
Connect via SSH (use your key)
Run sudo yum update -y and play around
Terminate the instance when done (very important!)

Bonus: Upload a file to S3 using the console — create bucket → upload → make it public → view in browser.

Common Beginner Mistakes to Avoid

Using root account for everything → Security risk!
Forgetting to terminate instances → Surprise bill
Making buckets public without thinking → Data leaks
Not using tags on resources → Hard to manage later

Next Steps After This Guide

Complete AWS Cloud Practitioner Essentials (free on AWS Skill Builder)
Try AWS Cloud Quest — gamified learning (free badges!)
Build a small project: Static website on S3 + CloudFront
Aim for AWS Certified Cloud Practitioner → Great first cert
Explore AWS Amplify if you're a frontend/full-stack developer

AWS is a journey, not a race. Start small, break things (safely), and have fun!

What’s your first AWS project going to be? Drop a comment below — I’d love to hear! 💬

Happy clouding! ☁️✨

AWS #CloudComputing #Beginners #DevOps

🚀 20 Must-Know GitHub Repositories for Developers!

Sahinur — Sat, 08 Mar 2025 09:59:57 +0000

💡 Introduction
GitHub is a treasure trove of free resources, tools, and learning materials for developers. Whether you’re a beginner or an experienced coder, these repositories will help you level up your skills in web development, AI, system design, and coding interviews.

Let’s explore 20 must-know GitHub repositories that can help you learn, code, and build better projects!

🔥 Top 20 GitHub Repositories Every Developer Should Know
1️⃣ Free Programming Books 📚
📌 https://github.com/EbookFoundation/free-programming-books
A legendary collection of free programming books covering AI, web development, Python, JavaScript, and more!

2️⃣ Developer Roadmap 🛤️
📌 https://github.com/kamranahmedse/developer-roadmap
A structured roadmap for learning Frontend, Backend, DevOps, and more with the latest technologies.

3️⃣ Build Your Own X 🔨
📌 https://github.com/codecrafters-io/build-your-own-x
A fantastic repository that teaches you how to build browsers, databases, OS, blockchain, and more from scratch!

4️⃣ 30 Seconds of Code ⚡
📌 https://github.com/30-seconds/30-seconds-of-code
Short, powerful JavaScript, Python, and React snippets to speed up your development.

5️⃣ Awesome GitHub Repos 🌟
📌 https://github.com/sindresorhus/awesome
A curated list of amazing GitHub repositories covering various programming topics.

6️⃣ JavaScript Algorithms and Data Structures 🧠
📌 https://github.com/trekhleb/javascript-algorithms
A collection of JavaScript-based algorithms and data structures with explanations.

7️⃣ The Algorithms 📊
📌 https://github.com/TheAlgorithms/Python
A massive collection of algorithm implementations in Python.

8️⃣ System Design Primer 🏗️
📌 https://github.com/donnemartin/system-design-primer
Learn system design concepts with real-world case studies and interview prep materials.

9️⃣ CS50x: Harvard’s Computer Science Course 💻
📌 https://github.com/cs50
Harvard’s official CS50 computer science course, available for free!

🔟 Frontend Developer Interview Questions 🎤
📌 https://github.com/h5bp/Front-end-Developer-Interview-Questions
A collection of real frontend developer interview questions to prepare for job interviews.

1️⃣1️⃣ Practical Machine Learning 📈
📌 https://github.com/ageron/handson-ml
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow.

1️⃣2️⃣ Free Code Camp Curriculum 🎓
📌 https://github.com/freeCodeCamp/freeCodeCamp
A free interactive coding platform with real-world projects.

1️⃣3️⃣ LeetCode Solutions 🏆
📌 https://github.com/azl397985856/leetcode
A collection of LeetCode solutions to prepare for FAANG interviews.

1️⃣4️⃣ Node.js Best Practices 🚀
📌 https://github.com/goldbergyoni/nodebestpractices
Best practices for building scalable and efficient Node.js applications.

1️⃣5️⃣ Coding Interview University 🎯
📌 https://github.com/jwasham/coding-interview-university
A complete self-study guide to crack FAANG interviews.

1️⃣6️⃣ Project-Based Learning 🔨
📌 https://github.com/practical-tutorials/project-based-learning
A collection of hands-on projects to improve your coding skills.

1️⃣7️⃣ The Art of Command Line 🖥️
📌 https://github.com/jlevy/the-art-of-command-line
Master the Linux command line with this detailed guide.

1️⃣8️⃣ Web Developer Resources 🌎
📌 https://github.com/bradtraversy/webdev-resources
A curated list of frontend & backend development resources.

1️⃣9️⃣ Public APIs 📡
📌 https://github.com/public-apis/public-apis
A list of free APIs for web development, data, and AI applications.

2️⃣0️⃣ Design Resources for Developers 🎨
📌 https://github.com/bradtraversy/design-resources-for-developers
Free design tools, UI kits, and CSS frameworks for developers.

🎯 Conclusion
🚀 Staying up-to-date with the latest resources and tools is key to growing as a developer. These GitHub repositories offer educational materials, coding challenges, and project-based learning opportunities to help you excel in your career.

Whether you’re preparing for interviews, learning new skills, or looking for project ideas, these repositories will boost your programming journey!

📌 Which GitHub repo is your favorite? Let me know in the comments! 👇

🔥 Don't forget to star your favorite repositories on GitHub and share this post with other developers!

Introducing Exciting New Git Features: A Boost for Your Development Workflow

Sahinur — Mon, 20 May 2024 09:28:25 +0000

Hello Dev community! 🌟

As developers, we continually seek tools and features that streamline our workflows and make coding more efficient and enjoyable. Git, the widely-used version control system, has recently introduced some new features that promise to enhance our development experience even further. In this blog post, we’ll explore these new Git features and how they can improve your workflow. Let’s dive in!

1. Sparse Checkout

Sparse checkout is a game-changer for large repositories. It allows you to check out only a subset of the repository, reducing the load on your local environment and improving performance.

How to Use Sparse Checkout

Initialize Sparse-Checkout Mode:

   git sparse-checkout init

Define the Sparse-Checkout Patterns: Specify the directories or files you want to include.

   git sparse-checkout set <directory-or-file>

Benefits

Reduced Disk Usage: Only necessary files are checked out, saving space.
Improved Performance: Faster operations due to fewer files.
Focused Development: Work on specific parts of the repository without distractions.

2. Built-in File System Monitor (FSMonitor)

FSMonitor is a built-in file system monitor that speeds up Git commands by keeping track of file changes in real-time, eliminating the need to scan the entire working directory.

How to Enable FSMonitor

Enable FSMonitor:

   git config core.fsmonitor true

Benefits

Faster Commands: Commands like git status execute significantly faster.
Real-Time Updates: Immediate awareness of file changes.

3. Git Maintenance

Git Maintenance automates routine maintenance tasks, such as repacking and cleaning up unnecessary files, to keep your repository performant and healthy.

How to Configure Git Maintenance

Configure Maintenance Tasks:

   git maintenance start

Schedule Specific Tasks:

   git maintenance run --task=<task-name>

Benefits

Automated Upkeep: Set and forget maintenance tasks.
Enhanced Performance: Regular maintenance ensures optimal repository performance.
Reduced Manual Intervention: Focus on coding while Git handles the housekeeping.

4. Background Pre-fetching

Git now supports background pre-fetching of commits, allowing you to stay up-to-date with remote branches without blocking your workflow.

How to Enable Background Pre-fetching

Configure Pre-fetching:

   git config fetch.auto true

Benefits

Non-blocking Updates: Fetch updates in the background while you work.
Stay Synchronized: Always have the latest changes from remote branches.

5. Improved Merge Conflict Resolution

Git’s new merge conflict resolution features provide a more intuitive and user-friendly way to handle conflicts, making it easier to integrate changes from different branches.

How to Use the Improved Merge Tools

Invoke the Merge Tool:

   git mergetool

Benefits

Simplified Conflict Resolution: Enhanced tools and interfaces to resolve conflicts efficiently.
Better Visual Guidance: Improved visualization of conflicts and changes.

Conclusion

These new Git features are designed to make your development workflow faster, more efficient, and more enjoyable. Whether you’re dealing with large repositories, frequent file changes, or complex merge conflicts, these enhancements have got you covered.

Give these features a try and see how they can transform your Git experience. Happy coding, and let’s keep pushing the boundaries of what we can achieve with Git!

Feel free to share your thoughts and experiences with these new features in the comments below. 🚀

Thank you for reading! If you enjoyed this post, don’t forget to give it a ❤️ and follow for more updates on the latest in software development. Happy coding! 👩‍💻👨‍💻

Unlock Your Code's Potential: 5 Powerful GitHub Copilot Alternatives

Sahinur — Sun, 17 Dec 2023 03:48:42 +0000

GitHub Copilot, developed by Microsoft, is an innovative AI-driven collaboration tool designed to enhance developers' coding efficiency and accuracy. By leveraging its contextual awareness of the file being worked on and its connections to related files, Copilot offers autocomplete-style suggestions that streamline the coding process. Although Copilot is a prominent AI tool, there are also other alternatives available. This article will introduce you to five additional options to consider.
Getting started with GitHub Copilot is effortless. You can utilize this powerful tool by either typing the code you need or describing your desired functionality in natural language within your text editor. Copilot will then carefully analyze the context and provide relevant suggestions to aid you in your coding process.
Moreover, GitHub Copilot conveniently integrates with various popular IDEs such as Visual Studio Code, Visual Studio, NeoVim, and the JetBrains suite. To begin utilizing Copilot, simply install the extension in your preferred IDE and start benefiting from its capabilities.
While GitHub Copilot is a useful tool for developers, there are also several alternatives available that offer similar code completion features. Let’s now consider five of those, so you can decide what to use.

1. Codeium

Codeium is an AI-powered programming tool that seamlessly integrates with popular code editors and IDEs like Visual Studio Code, PyCharm, JetBrains, IntelliJ, Vim, Android Studio, and more. Its primary purpose is to empower developers to expedite code development through its advanced code suggestion feature. Moreover, it offers extensive support for multiple programming languages, including Python and JavaScript.
When you start typing a code fragment in Codeium, it instantly generates multiple code suggestions. If one of the suggestions aligns with your desired outcome, simply press the Tab button to automatically insert it into your code block. In case the suggested code appears different from what you intend, Codeium offers an impressive feature that enables you to cycle through various code suggestions. By hovering over the beginning of the suggested code block, you can access a Next and Previous toggle, allowing you to explore different suggestions and select the one that best fits your specific requirements.

The pros of Codeium include the following:

Multiple programming language support
Fast suggestions and a provision to cycle between alternate coding patterns

2. CodeGeex AI

CodeGeex AI is an AI-based multilingual development tool with code suggestions, auto-complete features, and code translations. It is an AI assistant that works alongside you to ensure you ship codes easier and faster. It is not restricted by programming languages, as it can convert your code into a different programming language.

Pros of CodeGeex AI:

CodeGeex is powered by large code repositories of up to 20 programming languages.
It also provides developers with a translation feature to convert code written in one programming language, to another.

3. Code Whisperer

Code Whisperer, developed by AWS, is a powerful tool designed to enhance developers' coding experiences with a range of features. This comprehensive tool includes AI-powered autocomplete, code documentation, and refactoring capabilities, providing developers with a robust coding environment.
With Code Whisperer, developers can seamlessly integrate Amazon Web Services (AWS) functionalities into their code. This includes leveraging AWS services like S3 buckets, containers, and more. The tool offers code suggestions and auto-completion tailored specifically for working with AWS, enabling developers to write code more efficiently and accurately when utilizing AWS resources.

Pros of Code Whisperer:

Code Whisperer comes with an AWS toolkit extension, which in addition to Code Whisperer’s coding assistant feature, also provides you with easy access to view, modify and deploy AWS resources.
Code Whisperer provides support for a wide range of programming languages.

Cons of Code Whisperer:

Generally, it is a great alternative; it has no cons.

4. Tabnine

Tabnine revolutionizes the coding experience with its cutting-edge AI autocomplete functionality. As one of the early pioneers in the market, Tabnine's code completion tool offers a seamless integration with your preferred code editor. It empowers you to effortlessly complete code in any programming language, library, or framework of your choice.
What sets Tabnine apart from other AI models is its unique approach to data privacy. It exclusively trains its algorithms using open-source and permissive code, ensuring that your proprietary code remains confidential and secure.
By leveraging artificial intelligence, Tabnine analyzes the code you've written and intelligently suggests relevant words or phrases to assist you in your coding process. It seamlessly integrates with popular code editors such as Visual Studio Code, Sublime Text, and IntelliJ, allowing you to enjoy its exceptional capabilities across different environments. Moreover, Tabnine is highly adaptable and can be personalized to match your specific coding style.
Depending on your requirements and desired features, you have the option to choose between a free version or a paid subscription of Tabnine.

Pros of Tabnine :

Tabnine can predict your next line of code and give you suggestions based on it.
It comes with not only auto-complete features but also improved code quality because of its AI power.
It covers almost all programming languages, from the most popular ones by coders to the more used ones in the tech industry.

Cons of Tabnine :

Sometimes, it doesn’t give the exact suggestions.
Beginners in coding might find it not easy to use at first.
Based on its AI learning from code written, it might sometimes mix up one code for the other.

But in all, it is very awesome and useful. It has its cons due to its AI assistant having to learn from the code being written in the editor. Everyone learns, even AI,, so let’s not fault it.

5. Blackbox

Blackbox is an exceptional Visual Studio Code extension that provides advanced code completion and suggestions. What sets it apart is its remarkable ability to transform your coding questions into actual code within your editor. This unique feature allows you to seamlessly convert your queries into executable code snippets.

What makes Blackbox even more impressive is its wide compatibility with multiple programming languages. It intelligently adapts to the syntax and structure of each supported language, ensuring accurate and reliable code generation.

With Blackbox, you can confidently ask coding questions directly within your editor and witness them effortlessly transformed into functional code. It empowers developers to streamline their coding process and enhance productivity, all within the familiar environment of Visual Studio Code.

Pros of BlackBox:

When coding, if you need a specific coding function and you don’t know how to implement it, ask Blackbox, and its AI will get the implementation of the coding function for you.
It works with all programming languages.
It has a feature that allows you to copy code from videos you watch, which is useful while learning.

Cons of Blackbox:

Like every other alternative to GitHub Copilot, you don’t expect every suggestion to be what you wanted.

Conclusion

In their quest for alternatives to GitHub Copilot, developers can explore a range of code completion tools that offer valuable features and capabilities. It's important to note that most of these tools harness the power of AI, but it's essential to manage expectations as they may not always produce the exact desired outcome. Nevertheless, these tools prove to be valuable companions during the coding process, offering assistance and enhancing productivity