DEV Community: Bastullah Batas

🌐 Web Scraping with Apify — Where There’s Data, There’s Opportunity

Bastullah Batas — Thu, 27 Aug 2026 16:34:12 +0000

🌐 Web Scraping with Apify — Where There’s Data, There’s Opportunity

The web is full of valuable information, but collecting that information efficiently is a challenge.

I’ve recently been exploring Web Scraping with Apify and how it can be connected with n8n to build practical, automated data workflows.

This hands-on learning journey is helping me understand not only how to scrape data, but also how to transform raw web information into structured and useful datasets.

🚀 What I’m Exploring

1. What is Web Scraping?

Web scraping is the process of collecting information from websites in an automated way.

But scraping is not simply about extracting data. It is also important to understand the legal, ethical, and technical boundaries involved in collecting information from the web.

2. Understanding Apify

Apify provides a platform for web scraping, crawling, and browser automation.

One of the most interesting concepts is the Actor.

An Actor can be thought of as a reusable cloud program designed to perform a specific task—such as collecting data from websites, search engines, business directories, or other online sources.

3. Using Ready-Made Actors

Apify provides many ready-made Actors that can significantly reduce development time.

Some useful use cases include:

📍 Google Maps data extraction
💼 LinkedIn-related data collection
🌐 Website crawling
🔎 Search and content extraction

Instead of building everything from scratch, ready-made Actors can help speed up experimentation and prototyping.

4. Connecting Apify with n8n

This is where things become especially interesting.

Scraped data can be passed into n8n, where it can be cleaned, transformed, filtered, stored, or used as an input for another automated workflow.

A typical workflow can look like:

Website → Apify Actor → Structured Data → n8n → Processing → Database / Google Sheets / Other Systems

This creates a powerful combination of data collection + workflow automation.

⚙️ Handling Real-World Challenges

Web scraping becomes much more interesting when working with real websites.

Some of the challenges I’m learning to handle include:

Pagination
Rate limits
Dynamic websites
Anti-bot mechanisms
Data inconsistencies
Missing or incomplete information

Understanding these challenges is essential for building scraping workflows that are reliable and responsible.

🧹 Cleaning & Structuring Data

Raw scraped data is rarely ready to use immediately.

Before using it in an automation workflow, the data may need to be:

Cleaned → Validated → Standardized → Structured

For example, names, phone numbers, URLs, categories, and other fields may need normalization before sending the data to a database or another application.

🧪 Hands-On Practice

As part of this learning process, I’m working on extracting structured data from real-world targets and connecting the results with automation workflows.

The main goal is to understand the complete process:

Collect → Clean → Structure → Automate

💡 Key Takeaway

The most valuable part of web scraping is not simply getting data from a website.

The real opportunity comes from turning that data into something useful.

Raw Web Data → Structured Information → Automated Action

That’s why the combination of Apify + n8n is particularly interesting for building modern data and automation solutions.

I’m continuing to explore how these tools can be used for real-world automation, lead generation, research, data pipelines, and other practical applications.

🔗 Connect With Me

I regularly share my learning journey and automation projects on LinkedIn.

LinkedIn: www.linkedin.com/in/bastullah-batas-54ab23335

🏷️ Tags

webscraping #apify #n8n #automation #data #webautomation #nocode #lowcode #workflowautomation #datascience #devtools #learninginpublic

Build AI Agents and Automate Workflows with n8n: Course Review & Insights

Bastullah Batas — Wed, 26 Aug 2026 12:19:02 +0000

Build AI Agents and Automate Workflows with n8n: Course Review & Insights

I just completed the "Build AI Agents and Automate Workflows with n8n" course on LinkedIn Learning (50 minutes, completed Aug 26, 2026), and I want to break down why this matters for developers and what I learned.

Quick Context

Certificate Verification: 22c40b9551ef8da9c867ea048c175e53d9c149ac0df81f90b5eeeafd76c163ba7

Instructor: Shea Hanson, Head of Learning Content Strategy at LinkedIn Learning

Skills Covered:

AI Agents
AI Productivity
Generative AI

Why n8n? (And Why Now?)

If you haven't heard of n8n yet, imagine Zapier's open-source, self-hosted cousin that gives you full control. Here's the comparison:

Feature	n8n	Zapier	Make
Self-hosted	✅ Yes	❌ Cloud only	✅ Yes
Open source	✅ Yes	❌ Proprietary	❌ Proprietary
Integrations	400+	7000+	1000+
Custom code	✅ JavaScript	⚠️ Limited	✅ Yes
Pricing model	Free/self-hosted	Pay per task	Pay per operation
Learning curve	Moderate	Easy	Moderate

The key difference: With n8n, you own your workflows. No vendor lock-in. Self-host if you want full control.

What I Learned: The Three Pillars

1. AI Agents: The Intelligence Layer

AI agents are the game-changer here. They're not just "if-then" logic—they're autonomous systems that:

Understand context - Process information like humans do
Make decisions - Evaluate multiple options before acting
Handle ambiguity - Deal with edge cases intelligently
Learn and adapt - Improve from interactions

Real example:
Instead of: "If email contains 'refund' → send to support@"

You get: "Read email, understand the actual problem, route to appropriate department (support/billing/product), generate a contextual response"

This is a massive leap forward.

2. Workflow Automation: The Efficiency Layer

This is where n8n shines. You can build workflows that:

Trigger (Webhook/Schedule/Event)
    ↓
Data Processing (Extract, Transform, Validate)
    ↓
AI Processing (Generate, Summarize, Decide)
    ↓
Integration (Send to Slack, Email, Database, etc)
    ↓
Logging (Track what happened)

Key insight: n8n uses a visual node-based interface. No code required to build basic workflows, but you can drop into JavaScript whenever you need power.

3. Generative AI Integration: The Superpower

This is the magic ingredient. Combining LLMs (like Claude, GPT, etc.) with workflow automation unlocks:

Intelligent content generation - Write summaries, emails, reports automatically
Smart decision-making - Workflows that actually understand what they're doing
Context awareness - Process information in natural language
Scale without overhead - Do work of 10 people with 1 AI agent

Practical Workflows I'm Building

1. Smart Customer Support Pipeline

GitHub Issue Created
    ↓
AI reads issue + context
    ↓
AI determines: (Bug? Feature? Support question?)
    ↓
Route + Auto-draft response
    ↓
Notify team
    ↓
Track in database

Time saved: ~5 mins per issue × 50 issues/month = 4+ hours saved

2. Content Distribution Automation

Blog Post Published
    ↓
AI generates: Twitter thread, LinkedIn post, Email summary
    ↓
Post to 5 platforms simultaneously
    ↓
Track engagement
    ↓
Report metrics

Time saved: ~1 hour per blog post

3. Data Processing Pipeline

New data in Webhook
    ↓
Validate + Clean
    ↓
AI: Extract insights, categorize, flag anomalies
    ↓
Store in Database
    ↓
Update Dashboard

Benefit: Real-time processing, zero manual work

4. Email Intelligence

Email Received
    ↓
AI: Is this important? What's the action item?
    ↓
Smart filter + Prioritization
    ↓
Auto-draft response suggestions
    ↓
Notify on critical emails

Getting Started: The Technical Side

Installation (Self-hosted)

# Using npm
npm install -g n8n
n8n start

# Using Docker (Recommended)
docker run -it --rm --name n8n -p 5678:5678 n8nio/n8n

Then visit: http://localhost:5678

Basic Workflow Structure

Every n8n workflow has:

Trigger - What starts the workflow (webhook, schedule, event)
Nodes - Actions (read, write, process, send)
Connections - How data flows between nodes
Output - What happens when it completes

Integrating AI (Example with Claude)

// Inside a "Code" node in n8n

const response = await $node.$params.anthropic.sendMessage({
  model: "claude-opus-4-6",
  max_tokens: 1024,
  messages: [
    { 
      role: "user", 
      content: `Process this customer email: ${$node.data.input.emailText}`
    }
  ]
});

return {
  aiResponse: response.content[0].text,
  timestamp: new Date()
};

Key Takeaways

For Developers

✅ Learn n8n if you want to automate repetitive tasks
✅ Combine with AI for intelligent automation
✅ Self-host for full control over sensitive workflows
✅ Use the free tier to experiment (it's generous!)

For Teams

✅ Stop wasting engineer time on manual tasks
✅ Build workflows non-technical people can modify
✅ Reduce operational overhead significantly
✅ Scale without hiring more people

For Businesses

✅ Automation ROI is immediate and measurable
✅ Lower cost than hiring (especially for recurring tasks)
✅ Fewer human errors
✅ 24/7 operation

The Bigger Picture

We're at an inflection point where:

Automation is no longer optional - It's table stakes
AI + Automation is the winning combo - Intelligent workflows > dumb automation
No-code/low-code is production-ready - You don't need a full engineering team for basic workflows
The future of work is different - More strategy, less busywork

If you're not thinking about automation in 2026, you're leaving money and time on the table.

Resources

Official n8n Docs: https://n8n.io/docs
n8n Community: https://community.n8n.io
YouTube Tutorials: Search "n8n tutorial" (plenty of good stuff)
LinkedIn Learning Course: The one I just completed (50 mins, highly recommend)

Next Steps for Me

[ ] Build customer support automation workflow
[ ] Deploy content distribution pipeline
[ ] Integrate with my personal projects
[ ] Document workflows on GitHub
[ ] Experiment with advanced AI integration

Questions for You

What repetitive task are you doing that could be automated? Drop it in the comments—let's talk about solutions.

Have you used n8n? What did you build? I'd love to hear about your workflows.

Interested in AI + Automation? Follow for more technical breakdowns and tutorials coming soon.

Originally completed: August 26, 2026 at 09:32 AM UTC

Feel free to connect with me on LinkedIn or Twitter if you want to chat about automation, AI, or workflow engineering!

Getting Started with Workflow Automation
AI Integration Patterns in 2026
Building Scalable Automation Systems

Happy automating! 🚀
Linkedin : www.linkedin.com/in/bastullah-batas-54ab23335

Title: How I Built an Automated Data-Cleaning Pipeline with n8n (So I Never Touch Messy Spreadsheets Again)

Bastullah Batas — Tue, 25 Aug 2026 11:27:30 +0000

If you've ever collected customer data through a Google Form, you already know what happens next: the spreadsheet turns into chaos. Names in different cases, duplicate emails, phone numbers missing digits, dates written five different ways in the same column.

I got tired of manually cleaning this every week, so I built an automated pipeline in n8n that fixes all of it the moment a form is submitted.

The Problem

A raw form submissions sheet usually looks something like this:

Full Name	Email	Phone	City	Registration Date
John doe	j0hn@gmail.com	1712345678	Dhaka	01/05/2025
JOHN DOE	j0hn@gmail.com	1712345678	Chittagong	May 1, 2025
tanvir ahmed	tanvir@yahoo.com	171122	Dhaka	2025/06/12

Same person entered twice with different casing. A copy-pasted email attached to the wrong name. A phone number missing half its digits. Three different date formats in three rows. Multiply this by a few hundred submissions and manual cleanup becomes a part-time job.

The Solution: An n8n Pipeline

Here's the flow:

flowchart LR
    A[Form Submission Trigger] --> B[Messy Data]
    B --> C[Filter Code]
    C --> D[Clean File]
    C --> E[Summary Report Filter Code]
    E --> F[Summary Report]

A form submission triggers the workflow, pulls in the raw row, runs it through a Code node that handles all the normalization logic, then splits into two outputs — a clean master file and an auto-generated summary report.

What the Cleaning Logic Actually Handles

Names → consistent title-case formatting
Emails → deduplicated and validated against the right record
Phone numbers → normalized into one consistent format (in my case, Bangladeshi numbers into 01XXXXXXXXX)
Dates → every format converted into YYYY-MM-DD
Customer ID → auto-generated and assigned per record

The trickiest part was date and phone parsing — real-world form input is way messier than test data. A rough example of the kind of normalization logic involved:

function normalizePhone(raw) {
  const digits = raw.replace(/\D/g, '');
  if (digits.length === 10 && !digits.startsWith('0')) {
    return '0' + digits;
  }
  return digits;
}

(Simplified for illustration — the actual node handles a few more edge cases, like missing digits and stray country-code prefixes.)

The Result

Customer ID	Full Name	Email	Phone	City	Registration Date
CUST-0001	John Doe	john@gmail.com	01712345678	Dhaka	2025-01-05
CUST-0002	Sarah Khan	sarah@gmail.com	01987654321	Chittagong	2025-05-03
CUST-0004	Tanvir Ahmed	tanvir@yahoo.com	01711223344	Dhaka	2025-06-12

Same data, but now every row is consistent, deduplicated, and immediately usable — in a CRM, a mailing list, or anywhere else it needs to go.

Stack

n8n for orchestration
Google Forms & Sheets for data collection
Excel as the cleaned output

Why This Is Worth Automating

No more weekly manual cleanup
Fewer errors from duplicate or mistyped records
A summary report that's ready the second new data comes in
Scales identically whether you get 10 submissions a week or 10,000

If you're building something similar, or you've handled messier real-world data than this, I'd genuinely like to hear how you approached deduplication or date parsing — always looking to improve the edge-case handling.

Contact With me Linkedin : www.linkedin.com/in/bastullah-batas-54ab23335

Building a Real-Time Data Cleaning Pipeline with n8n, Google Sheets, and JavaScript

Bastullah Batas — Sat, 22 Aug 2026 12:02:52 +0000

The Problem: Every Team Has a "Messy Spreadsheet"

If you've ever inherited a spreadsheet that's been manually updated by multiple people over months, you know the pain. Here's what I was staring at recently:

📞 Phone numbers in 5+ different formats — 1711019283, +8801711019283, +880-1678-231920
📧 Emails with typos in the domain — gmail.co, gmial.com instead of gmail.com
🔤 Status fields with inconsistent casing — active, ACTIVE, Active, pending, INACTIVE
📅 Join dates in at least 4 different formats — YYYY-MM-DD, DD/MM/YYYY, MM/DD/YYYY, all mixed in the same column

None of this is unusual — it's what happens when data entry isn't enforced at the source. But it wrecks every filter, every report, and every automation built on top of it.

Rather than clean it manually (again), I decided to automate the entire process.

The Approach

I built a real-time transformation pipeline using three pieces:

Layer	Tool	Role
Trigger	n8n (Google Sheets Trigger)	Detects new/edited rows
Transform	JavaScript (Code node)	Cleans & standardizes data
Write-back	n8n (Append/Update node)	Writes cleaned data to sheet

Step 1 — Trigger on Change

The workflow starts with a Google Sheets Trigger node set to anyUpdate, so it fires the moment a row is added or edited — no polling delay, no manual "run" button.

Step 2 — Transform with JavaScript

This is where the actual cleanup logic lives. A few examples of the kind of normalization rules involved:

// Normalize phone numbers to a single consistent format
function normalizePhone(raw) {
  const digits = raw.replace(/[^\d]/g, '');
  const local = digits.startsWith('880') ? digits.slice(3) : digits;
  return `+880${local}`;
}

// Fix common email domain typos
function fixEmailDomain(email) {
  return email
    .replace(/gmial\.com$/i, 'gmail.com')
    .replace(/gmail\.co$/i, 'gmail.com');
}

// Standardize status casing
function normalizeStatus(status) {
  return status.trim().toLowerCase();
}

Each field gets passed through its own validation/normalization function, and the row is reassembled with a Cleaned_Status flag so downstream steps (and humans) can tell what's already been processed.

Step 3 — Write Back Automatically

The cleaned row is pushed back into the sheet using an Append or Update Row node, matching on a unique ID so existing rows get updated in place instead of duplicated.

Google Sheets Trigger  →  JavaScript Transformation  →  Append/Update in Sheet
     (anyUpdate)              (clean & validate)          (appendOrUpdate)

The Results

✅ Zero manual cleanup after setup — the pipeline runs on its own
✅ Real-time processing — rows are cleaned within seconds of being entered
✅ Consistent formatting across phone numbers, emails, statuses, and dates
✅ Hours saved weekly that used to go into manually fixing spreadsheet entries

Why This Matters

It's easy to underestimate how much time "small" data inconsistencies cost. A malformed phone number or an inconsistent status value doesn't seem like a big deal — until it silently breaks a filter, skews a report, or fails a downstream integration.

Low-code tools like n8n make it realistic to solve this kind of problem without standing up a full backend service. The trigger → transform → write-back pattern shown here can be adapted to almost any messy-data scenario: CRM exports, form submissions, survey responses, you name it.

Takeaways for Your Own Projects

If you're dealing with something similar, a few principles that helped:

Normalize at the field level — write one small function per field type (phone, email, date, etc.) rather than one giant cleaning function.
Flag what's been processed — a Cleaned_Status column avoids re-processing rows and makes debugging easier.
Trigger on change, not on schedule — real-time beats batch when the goal is "always clean," not "clean once a day."

Have you built something similar, or dealt with messy data at scale? Would love to hear how you approached it in the comments 👇

Connect with me:

💼 LinkedIn: www.linkedin.com/in/bastullah-batas-54ab23335
✖️ X (Twitter): @batas999

Building an AI-Powered Weather Agent using n8n, Groq LLM & Telegram

Bastullah Batas — Fri, 21 Aug 2026 15:53:51 +0000

Automating everyday tasks with AI Agents has never been easier. Recently, I built an end-to-end intelligent weather assistant using n8n, Groq AI (LLM), and Telegram.

In this article, I'll break down the workflow architecture, how the AI agents process intent, and how you can replicate this project.

🌐 Project Overview
Instead of simple keyword matching, this bot processes natural language queries from Telegram, extracts context using Groq AI, fetches live weather data via HTTP nodes, and formats the output into readable responses.

🏗️ Architecture & Workflow Breakdown
Here is how data flows through the n8n pipeline:

Telegram Trigger: Listens for incoming user messages in natural language (e.g., "Is it going to rain in Dhaka today?").

AI Agent (Groq Chat Model): Parses the user prompt, identifies the location, and extracts parameters.

HTTP Request Nodes: Makes dynamic API calls to fetch real-time weather metrics from an external Weather API.

Merge Node (chooseBranch): Manages data routing and joins parallel processing branches.

Secondary AI Agent: Formats raw API JSON data into a clear, user-friendly markdown response.

Telegram Send Message Node: Delivers the formatted answer back to the user.

🛠️ Tech Stack
Workflow Engine: n8n (Cloud / Self-hosted)

LLM Model: Groq Chat Model

Data Sources: External Weather API via HTTP Request Node

Interface: Telegram Bot API

🚀 Getting Started
If you want to try this out yourself:

Clone the repository from GitHub: Your Repository Link

Import the workflow.json into your n8n instance.

Set up credentials for Telegram Bot, Groq API, and your Weather API Key.

Activate the workflow and test it out on Telegram!

💬 Final Thoughts & Links
Building multi-agent workflows with low-code platforms like n8n combined with fast LLMs like Groq unlocks endless automation potential.

📫 Let's Connect on LinkedIn: www.linkedin.com/in/bastullah-batas-54ab23335

Feel free to drop any questions or feedback in the comments below!

Building Automations with n8n & Make – Let’s Connect!

Bastullah Batas — Fri, 21 Aug 2026 14:11:22 +0000

Act as a developer-advocate and technical content writer for dev.to. Write an engaging, value-first dev.to post introducing my expertise in AI and No-Code/Low-Code automation.

Here are the details to include:

Core Focus: Automation, AI workflows, and system integrations using n8n and Make (Integromat).
Key Services/Skills:
- Custom n8n workflows (self-hosted & cloud, API integrations, webhooks).
- Make (Integromat) multi-step scenarios & data syncing.
- Integrating LLMs/AI (OpenAI, Claude) into automation pipelines.
- Converting repetitive tasks into scalable background processes.
Call to Action: Invite developers, founders, and teams to connect, collaborate, or reach out for freelancing/consultation if they need help with any automation projects.

Tone & Style Guidelines:

Technical, helpful, direct, and developer-friendly (avoid overly salesy or marketing jargon).
Use Markdown formatting: clean headings, bullet points, and code blocks if relevant.
Include 3 catchy title options.
Include recommended dev.to tags (e.g., #automation, #n8n, #make, #ai).
Include brief placeholder suggestions for cover images or workflow diagrams to increase engagement.

[Optional extra info: My background/experience is n8n and make.com , and my contact/portfolio link is www.linkedin.com/in/bastullah-batas-54ab23335

DEV Community: Bastullah Batas

🌐 Web Scraping with Apify — Where There’s Data, There’s Opportunity

🌐 Web Scraping with Apify — Where There’s Data, There’s Opportunity

🚀 What I’m Exploring

1. What is Web Scraping?

2. Understanding Apify

3. Using Ready-Made Actors

4. Connecting Apify with n8n

⚙️ Handling Real-World Challenges

🧹 Cleaning & Structuring Data

🧪 Hands-On Practice

💡 Key Takeaway

🔗 Connect With Me

🏷️ Tags

webscraping #apify #n8n #automation #data #webautomation #nocode #lowcode #workflowautomation #datascience #devtools #learninginpublic

Build AI Agents and Automate Workflows with n8n: Course Review & Insights

Build AI Agents and Automate Workflows with n8n: Course Review & Insights

Quick Context

Why n8n? (And Why Now?)

What I Learned: The Three Pillars

1. AI Agents: The Intelligence Layer

2. Workflow Automation: The Efficiency Layer

3. Generative AI Integration: The Superpower

Practical Workflows I'm Building

1. Smart Customer Support Pipeline

2. Content Distribution Automation

3. Data Processing Pipeline

4. Email Intelligence

Getting Started: The Technical Side

Installation (Self-hosted)

Basic Workflow Structure

Integrating AI (Example with Claude)

Key Takeaways

For Developers

For Teams

For Businesses

The Bigger Picture

Resources

Next Steps for Me

Questions for You

Related Articles

Title: How I Built an Automated Data-Cleaning Pipeline with n8n (So I Never Touch Messy Spreadsheets Again)

The Problem

The Solution: An n8n Pipeline

What the Cleaning Logic Actually Handles

The Result

Stack

Why This Is Worth Automating

Building a Real-Time Data Cleaning Pipeline with n8n, Google Sheets, and JavaScript

The Problem: Every Team Has a "Messy Spreadsheet"

The Approach

Step 1 — Trigger on Change

Step 2 — Transform with JavaScript

Step 3 — Write Back Automatically

The Results

Why This Matters

Takeaways for Your Own Projects

Building an AI-Powered Weather Agent using n8n, Groq LLM & Telegram

Building Automations with n8n & Make – Let’s Connect!