Choosing Our Tools & Tech Stack for SankalandTech.com Analytics

How We Picked the Perfect Tools to Build SankalandTech.com’s Analytics System

Alright, we've had a good chat about why we're building this awesome web analytics system for sankalandtech.com. Now comes the fun part: deciding how we're actually going to put it all together! This means choosing the right tools and technologies, which folks in the tech world often call our "tech stack."

Imagine our web analytics system as a living, breathing machine, with a few key jobs to do. Each job needs its own special tool, a bit like how a chef picks different utensils for different cooking tasks:

• The "Eyes" on the Website: This is the clever bit of code that will watch what every visitor does on sankalandtech.com.

• The "Brain" of the System: This is where all the information collected by the "eyes" first lands. It's like a busy control center, getting everything organized and ready.

• The "Filing Cabinet": This is our super safe place where all the valuable website data will be neatly stored, ready to be looked at whenever we need it.

• The "Dashboard" (Our Insights View): This is the exciting display where all that raw data transforms into easy to understand charts and graphs, showing us what's really happening on our site.


Let's go ahead and pick the best tools for each of these important jobs!

Our Smart Tool Choices
We're aiming for tools that are not just powerful and widely used in the industry, but also fantastic for learning. Remember, our big goal is to build something truly impressive for your portfolio.

1. The "Eyes" on the Website: Tracking with Plain JavaScript

What it does: This is a tiny piece of code that lives right on your sankalandtech.com web pages. The moment someone visits a page, this code springs to life! It quietly gathers details like:

- Exactly which page they're looking at (its web address).
- The precise time they dropped by.
- Anything they click on.
- Some basic info about their web browser and the device they're using (like a phone or a computer).

Our Tool: Plain JavaScript

Why we picked it:

Works Everywhere: JavaScript is the universal language of the web. It runs smoothly in every single web browser, making it the perfect choice for tracking.

Total Control: By building this part ourselves, we get to decide exactly what information we collect and how. For a custom project like this, it's brilliant because it helps us truly understand every little detail of how it works.

No Unnecessary Baggage: We’re keeping it simple and fast by not using any big, complicated extra code.


Diagram showing chosen tools for SankalandTech’s custom web analytics system: JavaScript, Python Flask, SQL Server, and Streamlit.

2. The "Brain" of the System: Processing with Python (Using Flask)

What it does: This is the part that works quietly in the background. After the JavaScript on your website collects some data, it sends that data to this “brain.” Here’s what the brain does:

– Takes in the data safely.
– Checks and cleans it up a bit.
– Gets it ready to store in our database (the “filing cabinet”).

The tool we're using: Python with Flask

Why we chose it:

Python is powerful and easy to use: It’s a popular language for building websites and working with data. You already know it well, which makes things faster and easier.

Flask keeps things simple: Flask is a lightweight tool that helps us build the part of the system that receives the data. It doesn’t come with a lot of extra stuff we don’t need, which makes it less confusing and quicker to work with.

We can build fast: With Python and Flask, we don’t have to spend too much time setting things up. We can get this part running quickly and start seeing results sooner.


3. The "Filing Cabinet": Our Database (SQL Server)

What it does: This is where all the raw data from sankalandtech.com will be safely stored. Think of it as a super organized digital filing cabinet where we keep everything we collect, ready to use whenever we need it.

The tool we're using: Microsoft SQL Server

Why we chose it:

You already know it: You're familiar with SQL Server, so you don’t need to waste time learning something new. You can start designing the database and running queries right away.

Trusted by many companies: SQL Server is a strong and reliable tool used by businesses all over the world to manage and analyze data. It's a great skill to show off in your portfolio.

Free and cloud-ready: Thanks to Microsoft Azure’s free plan for SQL databases, we can put our database online at no cost. That means your project can go live and be shared with others easily.


4. The Dashboard (Our Insights View): Visualizing with Python (Streamlit)

What it does: This is one of the most exciting parts! It’s where all the raw data we collected turns into clear, helpful charts and summaries. The dashboard shows us exactly how people are using sankalandtech.com — like what pages they visit, what they click, and how long they stay.

The tool we're using: Python with Streamlit (plus Matplotlib and Seaborn to make the charts look great!)

Why we chose it:

Built with just Python: Streamlit is awesome because we can make fully working dashboards using only Python code. No need to deal with tricky HTML, CSS, or JavaScript. That saves us time and keeps things simple.

Quick to build and update: With Streamlit, we can create dashboards really fast. It’s great for testing different ways to show our data and seeing results right away.

Made for data projects: Streamlit is super popular with data science folks. When we use it along with Matplotlib and Seaborn (two great Python tools for creating charts), we can show off our insights in a way that looks clean and professional.

Easy to share online: It’s really simple to publish a Streamlit app on the web, so we can easily share a live working version of our dashboard with recruiters, friends, or teammates.


Our Main Data Source: The Awesome sankalandtech.com Website!

The real heart of this project is all the data we get when people use sankalandtech.com. Every time someone visits a page, clicks on something or reads a tutorial, that info comes straight into our system.

With all the tools picked out, we now have a clear plan to build every part of our web analytics setup.


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Website Analytics Project: Phases and Action Steps

  • Home
  • 🟢 Live App: Web Analytics Simulator
  • Phase 0: Project Setup & Problem Definition
  • 0.1 Define Project Goals & Challenges
  • 0.2 Select Tools, Tech Stack & Data Sources
  • 0.3 Software Requirements & Installation
  • 0.4 Folder Structure & GitHub Repo
  • 0.5 Testing Project Locally
  • Phase 1: Planning for Analytics
  • 1.1 Website Analytics Project Overview
  • 1.2 Define KPIs, Bounce Rate, Engagement
  • 1.3 Identify Target Users & Pain Points
  • Phase 2: Data Collection
  • 2.1 Setup Google Analytics 4 (GA4)
  • 2.2 Export GA4 Data to BigQuery/CSV
  • 2.3 Design SQL Schema for Web Analytics
  • Phase 3: Data Cleaning & Feature Engineering
  • 3.1 Clean Website Data with Python & Pandas
  • 3.2 Create Custom Metrics (Session, Bounce, etc.)
  • Phase 4: Exploratory Data Analysis (EDA)
  • 4.1 Analyze Website Traffic Trends
  • 4.2 Behavior by Device, Source, Location
  • 4.3 Top Pages & High Bounce Pages
  • 4.4 Diagnose Low Traffic & User Drop
  • Phase 5: Business Insights
  • 5.1 Funnel Analysis & Drop-Off Points
  • 5.2 New vs Returning Users
  • 5.3 Time Spent & Scroll Depth
  • Phase 6: SQL for Business
  • 6.1 SQL for Business Insights
  • 6.2 Combine Web Data Using SQL
  • 6.3 Find Problematic Pages Using SQL
  • Phase 7: Machine Learning
  • 7.1 Segment Users with Clustering
  • 7.2 Predict Bounce Rate with ML
  • 7.3 Recommend Pages or Content
  • Phase 8: Dashboards & Visualization
  • 8.1 Dashboard with Streamlit
  • 8.2 Visualize KPIs with Python
  • 8.3 Page-Level Metrics & Drop Heatmaps
  • Phase 9: Final Analytics Story
  • 9.1 Summary Report & Findings
  • Phase 10: Hosting & Portfolio Presentation
  • 10.1 Host Website Project Online
  • 10.2 Add to GitHub with ReadMe
  • 10.3 Pitch Project in Interview
  • Other Topics
  • SQL Interview Questions
  • SQL Case Study: Account Management
  • Python Interview Questions
  • Why C Language

Get in touch

  • tech2dsm@gmail.com

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