DEV Community: Ahmed Kadiwala

SQL Commands Explained: DDL vs DML vs DQL vs DCL vs TCL

Ahmed Kadiwala — Wed, 01 Oct 2025 06:43:00 +0000

SQL (Structured Query Language) is the backbone of working with databases. But SQL is not just about writing SELECT * FROM table;.

It has multiple categories of commands, each with a specific purpose. If you’ve ever been confused between DDL, DML, DQL, DCL, and TCL, this blog will clear it up for you with simple explanations and examples.

🏗️ 1. DDL (Data Definition Language)

Definition:

DDL commands are used to define and manage the structure of database objects like tables, schemas, and indexes.

Common Commands:

CREATE
ALTER
DROP
TRUNCATE
RENAME

Examples:

-- Create a table
CREATE TABLE Students (
    StudentID INT PRIMARY KEY,
    Name VARCHAR(50),
    Age INT
);

-- Alter table to add a new column
ALTER TABLE Students ADD Email VARCHAR(100);

-- Drop a table
DROP TABLE Students;

-- Truncate a table (removes all data but keeps structure)
TRUNCATE TABLE Students;

-- Rename a table
ALTER TABLE Students RENAME TO Learners;

✏️ 2. DML (Data Manipulation Language)

Definition:

DML commands are used to manipulate the actual data stored inside tables.

Common Commands:

INSERT
UPDATE
DELETE
MERGE

Examples:

-- Insert a new record
INSERT INTO Students (StudentID, Name, Age) VALUES (1, 'Alice', 20);

-- Update existing data
UPDATE Students SET Age = 21 WHERE StudentID = 1;

-- Delete a record
DELETE FROM Students WHERE StudentID = 1;

-- Merge data (commonly used in SQL Server/Oracle)
MERGE INTO Students S
USING NewStudents N
ON (S.StudentID = N.StudentID)
WHEN MATCHED THEN UPDATE SET S.Age = N.Age
WHEN NOT MATCHED THEN INSERT (StudentID, Name, Age) VALUES (N.StudentID, N.Name, N.Age);

🔍 3. DQL (Data Query Language)

Definition:

DQL is focused on querying and retrieving data. The main command is SELECT.

But querying becomes powerful with the use of clauses and operators.

Main Command:

SELECT

Clauses:

WHERE – filter rows
GROUP BY – group rows by column values
HAVING – filter groups
ORDER BY – sort results
LIMIT / TOP – restrict the number of rows

Operators:

LIKE – pattern matching
IN – match any value in a list
BETWEEN – range check
Comparison (=, >, <, >=, <=)
Logical (AND, OR, NOT)

Examples:

-- Simple SELECT
SELECT Name, Age FROM Students;

-- WHERE clause
SELECT * FROM Students WHERE Age > 18;

-- LIKE operator
SELECT * FROM Students WHERE Name LIKE 'A%';

-- IN operator
SELECT * FROM Students WHERE Age IN (18, 20, 22);

-- BETWEEN operator
SELECT * FROM Students WHERE Age BETWEEN 18 AND 25;

-- GROUP BY + HAVING
SELECT Age, COUNT(*) AS Count
FROM Students
GROUP BY Age
HAVING COUNT(*) > 2;

-- ORDER BY
SELECT * FROM Students ORDER BY Age DESC;

-- LIMIT (MySQL/PostgreSQL)
SELECT * FROM Students LIMIT 5;

-- TOP (SQL Server)
SELECT TOP 5 * FROM Students;

🔑 4. DCL (Data Control Language)

Definition:

DCL commands are used to control access to data inside the database.

Common Commands:

GRANT
REVOKE

Examples:

-- Grant SELECT permission
GRANT SELECT ON Students TO user1;

-- Revoke permission
REVOKE SELECT ON Students FROM user1;

🔄 5. TCL (Transaction Control Language)

Definition:

TCL commands handle transactions in databases. They help maintain data consistency by grouping multiple DML operations.

Common Commands:

COMMIT
ROLLBACK
SAVEPOINT
SET TRANSACTION

Examples:

-- Start a transaction
BEGIN;

-- Insert data
INSERT INTO Students (StudentID, Name, Age) VALUES (2, 'Bob', 22);

-- Commit changes
COMMIT;

-- Rollback example
BEGIN;
UPDATE Students SET Age = 25 WHERE StudentID = 2;
ROLLBACK; -- Undo the update

-- Savepoint example
BEGIN;
INSERT INTO Students VALUES (3, 'Charlie', 23);
SAVEPOINT sp1;
UPDATE Students SET Age = 30 WHERE StudentID = 3;
ROLLBACK TO sp1; -- Undo update but keep insert
COMMIT;

📌 Summary: DQL at the Core

DQL = SELECT + Clauses + Operators
It’s what we use the most when working with databases daily.
DDL defines structure, DML manipulates data, DCL controls access, and TCL ensures safe transactions.

📊 Quick Reference Comparison Table

Category	Commands	Clauses (DQL only)	Operators (DQL only)
DDL	CREATE, ALTER, DROP, TRUNCATE, RENAME	-	-
DML	INSERT, UPDATE, DELETE, MERGE	-	-
DQL	SELECT	WHERE, GROUP BY, HAVING, ORDER BY, LIMIT, TOP	LIKE, IN, BETWEEN, =, >, <, AND, OR, NOT
DCL	GRANT, REVOKE	-	-
TCL	COMMIT, ROLLBACK, SAVEPOINT, SET TRANSACTION	-	-

🚀 Takeaway

Think of SQL like this:

DDL builds the house 🏠
DML arranges the furniture 🪑
DQL lets you explore inside 🔍
DCL decides who gets the keys 🔑
TCL ensures everything stays safe and consistent 🔄

Master these categories, and you’ll have full control over any database you work with!

🔒 How Platforms Detect Fake Accounts & Inflated Likes 📊 Instagram, YouTube, Facebook Explained 👀

Ahmed Kadiwala — Sun, 21 Sep 2025 18:06:11 +0000

Ever wondered how platforms like Instagram, Facebook, YouTube, and others can detect suspicious activity or coordinated fake accounts, even when multiple accounts are using different IP addresses? In this post, we’ll explore how platforms detect fake activity, the risks involved, and the myths surrounding famous creators buying likes or subscribers — all from a research and learning perspective.

1. IP Addresses Aren’t Everything

A common misconception is that rotating IPs or using proxies can make fake accounts invisible. While using multiple IPs might temporarily avoid simple blocks, modern platforms have evolved far beyond basic IP-based detection.

They monitor patterns beyond IP addresses, including:

Device fingerprints (browser type, OS, screen size, fonts)
Email patterns (temporary domains, similar structures)
Timing and frequency of actions (burst registrations or likes)
Content similarity (identical posts or comments across accounts)

Even if each account uses a different IP, suspicious patterns can still be flagged. Platforms like Instagram and YouTube have sophisticated detection algorithms that can notice unusual patterns across multiple accounts.

2. Behavioral Analysis: The Core Signal

Behavioral analysis is one of the most effective ways platforms detect coordinated fake activity. Common signals include:

Accounts liking the same post or video rapidly
Posting identical or highly similar content
Repetitive commenting patterns
Following or interacting with the same set of accounts consistently

These patterns trigger automated algorithms that assign a risk score, and in some cases, suspicious clusters are reviewed by human moderators.

3. Device & Browser Fingerprinting

Even with rotating IPs, platforms can track subtle device-level signals:

Browser type and version
Operating system
Screen resolution
Installed fonts and plugins
Cookie and session data

Matching fingerprints across multiple accounts can reveal links between accounts, making coordinated fake activity detectable.

4. Long-Term Risk

Most platforms maintain logs and historical data indefinitely. Suspicious patterns may not be obvious immediately but can emerge over time, leading to:

Bulk account bans
Removal of fraudulent likes, comments, or followers
Suspension or permanent deletion of accounts

The larger the scale of coordinated activity, the higher the likelihood of detection.

5. The Myth vs Reality: Buying Likes and Subscribers

You may have heard that famous YouTubers or influencers, such as T-Series or PewDiePie, buy likes, views, or subscribers. Here’s what research and platform enforcement suggest:

Large creators occasionally use marketing and promotional tools to boost visibility, but buying fake likes/subscribers is risky and against platform rules.
Platforms actively audit sudden spikes in activity. Artificially inflated metrics are often removed, and repeat offenders may face penalties.
While there are underground services that offer fake followers or likes, large platforms continuously update detection algorithms to identify and neutralize them.
Most successful creators rely on organic growth, collaborations, and promotions, not fake activity, because it’s sustainable and avoids account risk.

✅ Takeaway: Buying fake likes/subscribers is risky, and platforms like YouTube, Instagram, and Facebook are actively detecting it. The most reliable growth comes from legitimate engagement.

6. Ethical Learning Approach

If your goal is to study fake account patterns, there’s a safe and legal approach:

Create a local or sandbox environment
Simulate accounts and coordinated actions in a controlled setting
Analyze patterns and detection strategies without affecting real users

This teaches the same concepts used in fraud detection, data science, and cybersecurity research — safely and responsibly.

Final Thoughts

Even with techniques like IP rotation, coordinated fake accounts are highly detectable. Platforms use a combination of device fingerprinting, behavioral analytics, and historical data to maintain fairness and integrity.

Understanding these detection mechanisms gives valuable insight for anyone interested in security, data science, or platform moderation, while keeping experiments ethical and legal.

🤖 From Machine Learning to AI Chatbots: The Complete Beginner’s Guide

Ahmed Kadiwala — Sun, 21 Sep 2025 16:24:32 +0000

Artificial Intelligence (AI) is everywhere — from chatbots like ChatGPT to self-driving cars and voice assistants.

But for a beginner, terms like Machine Learning, Deep Learning, CNN, RNN, and Reinforcement Learning can be confusing.

Don’t worry! In this blog, we’ll break down everything step by step with real-life examples so you can understand how these concepts come together to create powerful AI systems.

🧠 Machine Learning (ML) vs Deep Learning (DL)

What is Machine Learning?

Machine Learning is teaching a computer to learn from examples rather than following strict rules.

Example:

If you want a computer to detect whether an email is spam:

You give it lots of emails labeled “spam” or “not spam”
It learns patterns from the data
Predicts whether new emails are spam or not

What is Deep Learning?

Deep Learning is a special branch of ML that uses Neural Networks inspired by the human brain.

It works best with huge amounts of data and automatically learns complex features.

Example:

Face recognition on phones
Self-driving cars detecting traffic signals
Voice assistants like Siri or Alexa

ML vs DL: Key Differences

Feature	Machine Learning (ML)	Deep Learning (DL)
Data Needed	Works with small/medium data	Needs lots of data (Big Data)
Feature Extraction	Manual (you decide what to look at)	Automatic (neural networks learn)
Computation	Runs on CPUs	Needs GPUs (more power)
Examples	Spam detection, price prediction	Self-driving cars, voice recognition

📊 Types of Learning in Machine Learning

Machine Learning has three main learning types:

1. Supervised Learning

You give the computer input data + correct answers (labels)
It learns from these labeled examples

Example:

Predicting house prices based on size, location, and age
Email spam detection

2. Unsupervised Learning

No answers are given; the computer finds patterns or groups on its own

Example:

Grouping customers by shopping habits for targeted ads
Clustering similar news articles together

3. Reinforcement Learning

The computer learns by trial and error using rewards and penalties

Example:

Self-driving cars: Reward for staying on the road, penalty for crashes
Game AIs like AlphaZero learning chess strategies

Quick Summary Table

Type of Learning	Data Given	Goal	Example
Supervised	Input + Answer	Predict new answers	Spam detection, price prediction
Unsupervised	Only Input	Find hidden patterns	Customer segmentation
Reinforcement	Trial + Reward	Learn best actions	Self-driving cars, Game AI

🖼️ CNN vs RNN: Two Types of Neural Networks

Since Deep Learning uses Neural Networks, let’s look at two famous ones:

1. CNN (Convolutional Neural Network)

Designed for images and spatial data
Learns patterns like edges, shapes, and objects from images

Examples:

Face recognition
Medical imaging (detecting diseases)
Self-driving cars detecting stop signs

2. RNN (Recurrent Neural Network)

Designed for sequential data where order matters
Remembers past information for context

Examples:

Text generation (chatbots, story writing)
Language translation
Stock price prediction

CNN vs RNN Table

Feature	CNN	RNN
Data Type	Images, spatial data	Sequential data (text, time-series)
Memory	No memory of past inputs	Remembers previous inputs
Use Cases	Image recognition, object detection	Text, speech, language processing
Processing	Parallel (fast)	Sequential (slower)

🔗 How Everything Connects: From ML to AI Chatbots

Let’s see how these pieces fit together to build something like an AI chatbot:

Machine Learning:
- Basic ML models can handle simple chat rules like FAQs
Deep Learning with RNN/Transformers:
- RNNs (and advanced versions like Transformers) process conversations because context matters in chat
Reinforcement Learning:
- Modern chatbots like ChatGPT use reinforcement learning to improve responses through feedback
CNN in Chatbots:
- CNNs are used if chatbots also analyze images (e.g., a bot that understands memes)

🏁 Conclusion

Machine Learning teaches computers to learn from data
Deep Learning uses neural networks for powerful AI tasks
Supervised, Unsupervised, and Reinforcement Learning define how the model learns
CNNs handle images, RNNs handle sequences like text
Together, they power real-world applications like chatbots, self-driving cars, and virtual assistants

The best part? You can start small — learn supervised learning first, try simple ML projects, then explore deep learning to build cool AI applications! 🚀

🚀 From Python to Portfolio: How I Vibecoded My First Website Without Knowing JavaScript

Ahmed Kadiwala — Mon, 15 Sep 2025 06:59:33 +0000

Building a portfolio website is every developer's rite of passage. It's not just about showing your skills — it's about experimenting, breaking things, fixing them, and learning along the way. Recently, I built and hosted my portfolio website on Firebase Hosting, and in the process, I discovered what I like to call Vibecoding.

👉 Live link: https://portfolio-website-99a6d.web.app/

⚠️ Note: The site is still a work-in-progress and not yet mobile-optimized. Responsiveness is coming soon!

🎵 What is Vibecoding?

Vibecoding is my way of describing how I coded this project:

Sitting with my laptop, headphones on, playlist running, and just vibing with the flow of code.
It's not about perfect planning; it's about getting into the rhythm, fixing issues as they come, and letting creativity drive the process.
Some developers call it "flow state," but I like "vibecoding" because it feels more fun and personal.

When you vibecode, you don't wait for the perfect roadmap. You start building, deploy often, learn from errors, and let the vibe guide you.

⚡ The Bolt.new Experience

Here's the surprising part: I don't even know JavaScript, TypeScript, or Java. My background is in Python, SQL, and C++ — yet I still managed to build this portfolio.

How? Thanks to Bolt.new, a tool that made creating this React + Vite project a lot easier. Bolt allowed me to skip a lot of boilerplate setup and directly start experimenting with frontend code.

This shows that you don't always need to master every language to ship something useful. Sometimes, the right tools bridge the gap.

🚀 Hosting Portfolio on Firebase

1. Building the Project

Since I used React + Vite, the production build step was simple:

npm run build

This creates a dist folder with optimized files ready for deployment.

2. Firebase Setup

1. Install Firebase CLI globally:

npm install -g firebase-tools

2. Login to Firebase:

firebase login

3. Initialize hosting in the project directory:

firebase init hosting

Select your Firebase project
Set dist as the public directory
Choose yes for single-page app config

3. Deploying to Live

firebase deploy

And boom 💥 — my portfolio was live at

👉 https://portfolio-website-99a6d.web.app/

🛠️ Common Errors & Fixes

Along the way, I faced multiple errors. Here's how I solved them:

❌ Error: No currently active project

Error: No currently active project.

✅ Fix: Run

firebase use --add

and select your Firebase project.

❌ Error: Firestore API disabled (403)

Cloud Firestore API has not been used in project ...

✅ Fix: Go to

👉 Enable Firestore API

and enable the API, then redeploy.

❌ Deprecation Warnings (like `node-domexception`)

These are safe to ignore in most cases. Just make sure you're on a recent Node.js version.

📊 Monitoring the Website

Deployment isn't the end. I also explored monitoring options:

Firebase Performance Monitoring – to track response times and usage.
UptimeRobot – free tool to ping my website every 5 minutes.
Sentry – optional for tracking runtime errors.

This ensures that if something breaks, I know before users do.

📈 SEO Considerations

Since this is a portfolio, discoverability matters. Some steps I applied:

Added meta tags (title, description, keywords) inside index.html
Deployed on Firebase with HTTPS (Google ranks secure sites higher)
Plan to add a sitemap.xml and robots.txt for better crawling

Right now, my site isn't mobile-oriented, so SEO isn't perfect — but once responsiveness is fixed, search engines will rank it better.

💡 Lessons from Vibecoding + Firebase

Start messy, refine later. Vibecoding helps you begin without overthinking.
Tools > knowledge sometimes. Bolt.new helped me code in a stack I didn't even know.
Deployment is half the battle. Use Firebase for quick hosting wins.
Errors are part of the process. Every warning taught me something new.
Monitoring matters. A live site without uptime monitoring is risky.
SEO is ongoing. Don't ignore metadata and responsiveness.

🎯 Final Thoughts

Building this portfolio wasn't about perfection — it was about momentum. Vibecoding gave me the mindset to push forward, Bolt.new gave me the starting boost, Firebase gave me the platform to deploy fast, and monitoring gave me peace of mind.

The journey continues: I still need to make my portfolio mobile-friendly and add CI/CD pipelines so updates go live automatically after each GitHub push.

But hey, that's the fun of vibecoding — the vibe evolves with you. ✨

👨‍💻 If you're a developer building your portfolio, I highly recommend vibecoding your way into Firebase Hosting. Deploy fast, fix as you go, and keep vibing.

Never Hit the ChatGPT Limit Again: 5 Hacks for Long Coding Conversations 🚀

Ahmed Kadiwala — Mon, 08 Sep 2025 14:32:00 +0000

Ever had ChatGPT forget half your project mid-way? 😅 You start coding, things are going great… and suddenly, ChatGPT stops remembering what you did earlier. Annoying, right?

The good news: there’s a super simple way to keep your coding conversations going smoothly—even for big projects—without losing any progress. 🚀

🤔 Why This Happens

Every AI model, including ChatGPT, has a context window 🧠.

Think of it like short-term memory. Once you cross that limit, the model starts forgetting older parts of the conversation to make space for the new ones.

Some AI tools like Claude try to compress older messages automatically. But with ChatGPT, you need to handle this yourself if you want to keep the conversation alive.

🛠️ The 5-Step Hack to Continue Long Coding Conversations

Here’s the workflow I use to make sure ChatGPT never loses track of my projects:

1️⃣ Start with a Project Summary

At the start of a new chat, give ChatGPT a quick one-paragraph overview of your project. For example:

📝 “We’re building a Django backend with user authentication, REST APIs, Docker deployment, Bandit security checks, and test cases. The code should be modular, clean, and production-ready.”

This keeps things light but gives enough context for ChatGPT to help effectively.

2️⃣ Keep Big Code Files Outside ChatGPT

📂 Don’t paste your entire codebase every time. Store it on GitHub or locally.

Then just say:

“Refer to the models.py file from earlier. Now write views.py for the same project.”

This saves chat space and keeps the focus on the part you’re working on.

3️⃣ Split Big Code into Smaller Pieces

🧩 Large code requests often get cut off. Break them into chunks like:

📄 models.py
📄 views.py
📄 urls.py
📄 tests.py

Generate one at a time. Later, you can combine everything into a full project.

4️⃣ Save a Summary Before Ending a Session

Before closing ChatGPT, ask it for a short summary 🗒️ of what you did.

Next time you continue, paste this summary and say:

“Here’s what we did last time. Let’s continue from here.”

Boom—context restored instantly.

5️⃣ Use Local Editors for Real Coding

For actual development, use VS Code or PyCharm 💻.

Call in ChatGPT only for specific tasks like writing a feature, fixing a bug 🐞, or optimizing code performance.

🎯 Conclusion

ChatGPT is a fantastic coding assistant, but long conversations can hit limits fast. 🚧

By using project summaries, splitting code, and saving progress, you can keep building big projects without losing context.

Next time ChatGPT forgets something, don’t panic—just restart with a summary and keep coding like nothing happened. 🙌

DEV Community: Ahmed Kadiwala

SQL Commands Explained: DDL vs DML vs DQL vs DCL vs TCL

🏗️ 1. DDL (Data Definition Language)

✏️ 2. DML (Data Manipulation Language)

🔍 3. DQL (Data Query Language)

🔑 4. DCL (Data Control Language)

🔄 5. TCL (Transaction Control Language)

📌 Summary: DQL at the Core

📊 Quick Reference Comparison Table

🚀 Takeaway

🔒 How Platforms Detect Fake Accounts & Inflated Likes 📊 Instagram, YouTube, Facebook Explained 👀

1. IP Addresses Aren’t Everything

2. Behavioral Analysis: The Core Signal

3. Device & Browser Fingerprinting

4. Long-Term Risk

5. The Myth vs Reality: Buying Likes and Subscribers

6. Ethical Learning Approach

Final Thoughts

🤖 From Machine Learning to AI Chatbots: The Complete Beginner’s Guide

Artificial Intelligence (AI) is everywhere — from chatbots like ChatGPT to self-driving cars and voice assistants.

🧠 Machine Learning (ML) vs Deep Learning (DL)

What is Machine Learning?

What is Deep Learning?

ML vs DL: Key Differences

📊 Types of Learning in Machine Learning

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

Quick Summary Table

🖼️ CNN vs RNN: Two Types of Neural Networks

1. CNN (Convolutional Neural Network)

2. RNN (Recurrent Neural Network)

CNN vs RNN Table

🔗 How Everything Connects: From ML to AI Chatbots

🏁 Conclusion

🚀 From Python to Portfolio: How I Vibecoded My First Website Without Knowing JavaScript

🎵 What is Vibecoding?

⚡ The Bolt.new Experience

🚀 Hosting Portfolio on Firebase

1. Building the Project

2. Firebase Setup

3. Deploying to Live

🛠️ Common Errors & Fixes

❌ Error: No currently active project

❌ Error: Firestore API disabled (403)

❌ Deprecation Warnings (like node-domexception)

📊 Monitoring the Website

📈 SEO Considerations

💡 Lessons from Vibecoding + Firebase

🎯 Final Thoughts

Never Hit the ChatGPT Limit Again: 5 Hacks for Long Coding Conversations 🚀

🤔 Why This Happens

🛠️ The 5-Step Hack to Continue Long Coding Conversations

1️⃣ Start with a Project Summary

2️⃣ Keep Big Code Files Outside ChatGPT

3️⃣ Split Big Code into Smaller Pieces

4️⃣ Save a Summary Before Ending a Session

5️⃣ Use Local Editors for Real Coding

🎯 Conclusion

❌ Deprecation Warnings (like `node-domexception`)