🌦️ Build Your Own Open-Source Weather API Using Python, FastAPI, and Open-Meteo in Windows 11

 

No API Keys. 100% Free. Runs on Windows, Linux, or macOS.

If you’ve ever wanted to build a weather service that you fully control—without relying on paid providers—this guide is for you. Today we’ll build a fully open-source weather API using:

✔ Python
✔ FastAPI
✔ Uvicorn
✔ Open-Meteo (free, no API key)

This weather server runs locally on any computer and can be extended to power automation tools, chatbots, dashboards, or IoT projects.

Let’s get started!

---

🚀 Why Open-Source Instead of Claude/Proprietary MCP?

In the MCP ecosystem, servers are often built to integrate with specific applications like Claude Desktop. But not everyone wants to depend on closed-source platforms.

This tutorial shows how to build a fully standalone weather API, powered only by open-source tools:

• FastAPI → modern Python web framework

• Uvicorn → lightning-fast ASGI server

• Open-Meteo API → real-time global weather data

• httpx → async HTTP client

The result?
A clean, extensible, open-source weather microservice you can run anywhere.

---

🖥️ Step 1 — Install Requirements (Windows / macOS / Linux)

Open a terminal or PowerShell window and install dependencies:

pip install fastapi uvicorn httpx

That’s all you need.

---

🏗️ Step 2 — Create the Project File

Create a new directory:

mkdir weather-server
cd weather-server

Then create:

server.py

Paste in the full working code:

from typing import Any
import httpx
from fastapi import FastAPI

# Initialize FastAPI app
app = FastAPI()

# Constants
OPENMETEO_API_BASE = "https://api.open-meteo.com/v1"
USER_AGENT = "weather-mcp/1.0"

# Helper function to interact with the Open-Meteo API
async def make_openmeteo_request(url: str) -> dict[str, Any] | None:
    """Make a request to the Open-Meteo API with proper error handling."""
    headers = {
        "User-Agent": USER_AGENT,
        "Accept": "application/json",
    }
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(url, headers=headers, timeout=30.0)
            response.raise_for_status()
            return response.json()
        except Exception:
            return None

# -----------------------------
# Endpoint - Get Current Weather
# -----------------------------
@app.get("/current-weather")
async def get_current_weather(latitude: float, longitude: float) -> dict:
    """Get current weather data for a specified latitude/longitude."""
    
    url = (
        f"{OPENMETEO_API_BASE}/forecast"
        f"?latitude={latitude}&longitude={longitude}"
        "&current=temperature_2m,is_day,showers,cloud_cover,wind_speed_10m,"
        "wind_direction_10m,pressure_msl,snowfall,precipitation,"
        "relative_humidity_2m,apparent_temperature,rain,weather_code,"
        "surface_pressure,wind_gusts_10m"
    )

    data = await make_openmeteo_request(url)

    if not data:
        return {"error": "Unable to fetch current weather data for this location."}

    return data

# -----------------------------
# Endpoint - Get Weather Forecast
# -----------------------------
@app.get("/forecast")
async def get_forecast(latitude: float, longitude: float, days: int = 3) -> dict:
    """Get forecast for next X days."""
    
    url = (
        f"{OPENMETEO_API_BASE}/forecast"
        f"?latitude={latitude}&longitude={longitude}"
        f"&hourly=temperature_2m,precipitation,cloud_cover"
        f"&forecast_days={days}"
    )

    data = await make_openmeteo_request(url)
    if not data:
        return {"error": "Unable to fetch forecast."}

    return data

# -----------------------------
# Endpoint - Get Location by Name
# -----------------------------
@app.get("/location")
async def get_location(query: str) -> dict:
    """Search for a city/place name and get coordinates."""
    
    url = f"https://geocoding-api.open-meteo.com/v1/search?name={query}&count=5"
    
    data = await make_openmeteo_request(url)
    
    if not data:
        return {"error": "Location not found."}
    
    return data

---

▶️ Step 3 — Run Your Server

Start the server using Uvicorn:

uvicorn server:app --reload

You’ll see:

Uvicorn running on http://127.0.0.1:8000

Open your browser and test:

✔ Current Weather

http://127.0.0.1:8000/current-weather?latitude=40.7128&longitude=-74.0060

✔ Forecast

http://127.0.0.1:8000/forecast?latitude=40.7128&longitude=-74.0060&days=3

✔ Location Search

http://127.0.0.1:8000/location?query=London

---

📘 Step 4 — Use the Interactive Dashboard

FastAPI provides automatic documentation.

Open:

http://127.0.0.1:8000/docs

You now have:

• A UI for testing all endpoints

• Auto-generated schema

• Live API explorer

No additional setup. It just works.

---

🧩 Step 5 — Why This Matters

This small project demonstrates how you can:

• Build useful microservices with only open-source tools

• Consume real-time weather data without API keys

• Extend the API into dashboards, chatbots, IoT devices, and more

• Deploy the service anywhere (Docker, VPS, Raspberry Pi, etc.)

It’s clean, lightweight, and completely free to use.

---

🎉 Final Thoughts

With Python, FastAPI, and Open-Meteo, you now have a fully functioning open-source weather server that you can run locally or deploy to the cloud.

This is a perfect foundation for:

• AI integrations

• Automation scripts

• Custom user interfaces

• Smart home systems

• Education and learning projects

---

📚 Need Online, Retail, or Corporate Training?

If you want to master Python, APIs, cloud, AI, full-stack development, DevOps, or corporate-level tech skills, learn with the best trainers available.

Get professional online training, retail programs, or corporate upskilling from experts:

👉 www.eduarn.com

Your learning, your success — powered by world-class trainers.

AI-Powered Kubernetes, Podman & Container Training 2026–2027 | Eduarn Online & Corporate Learning

 As we move into 2026 and 2027, the integration of Artificial Intelligence (AI) and container technologies is redefining how enterprises build, deploy, and scale applications. Modern businesses in retail, tech, and corporate sectors are increasingly relying on Containers, Kubernetes, and Podman to automate workflows, reduce infrastructure costs, and achieve faster delivery cycles.

At Eduarn, we combine AI-powered personalized learning with world-class online and corporate training to prepare individuals and teams for the next generation of container orchestration and automation.

---

The Rise of AI in Container and Kubernetes Management

The future of cloud computing lies in AI-driven automation. In 2026–2027, tools like Kubernetes and Podman are expected to leverage artificial intelligence for smarter resource allocation, predictive scaling, and self-healing container clusters.
AI algorithms now monitor container performance, detect bottlenecks, and automatically adjust workloads to maintain optimal efficiency.

By learning Containers, Kubernetes, and Podman today, professionals can position themselves for a future where AI and containerization work hand-in-hand to power autonomous DevOps ecosystems.

---

Why Every Business Needs Container Training

Whether you’re in retail, finance, or enterprise IT, containerization is the foundation of modern cloud architecture.

• Containers provide lightweight, portable environments that accelerate deployment.

• Kubernetes orchestrates large-scale container clusters with AI-based scaling and monitoring.

• Podman, an advanced container engine, ensures secure, daemonless, and rootless container operations.

For corporate teams, mastering these tools means improved deployment reliability, security, and cost savings.
For retail and e-commerce businesses, containers help deliver faster web services, AI-based personalization, and smooth scaling during high-traffic seasons.

Eduarn’s AI-enhanced training platform ensures learners not only understand these technologies but also learn to integrate machine learning models into Kubernetes and Podman environments — the future of cloud DevOps.

---

Eduarn — The #1 AI-Powered Online and Corporate Training Platform

At Eduarn, we lead the transformation of tech education by providing AI-assisted training in Containers, Kubernetes, and Podman.
Our programs use intelligent adaptive learning algorithms to adjust content based on each learner’s pace and goals — making it the most personalized and effective training experience in 2026–2027.

We offer:

• 🎯 Online Training: Self-paced AI-personalized courses in containers and cloud-native technologies.

• 🧑‍💼 Corporate Training: Customized learning paths for enterprise teams with AI-based performance tracking.

• 🏪 Retail & SME Learning Programs: Affordable solutions for startups and retailers adopting DevOps practices.

Eduarn’s interactive labs, real-world AI container projects, and live mentorship ensure learners build both technical mastery and strategic understanding.

---

Podman and Kubernetes — Smarter, Secure, and AI-Optimized

Podman continues to gain popularity as the secure, rootless alternative to Docker, perfect for AI workloads and hybrid environments.
Kubernetes, meanwhile, has evolved into a self-managing orchestration platform where AI agents can make data-driven scaling and fault-recovery decisions.

Through Eduarn’s AI-guided training, professionals gain hands-on experience deploying machine learning pipelines, containerized AI models, and Podman-based microservices managed via Kubernetes.

---

Eduarn LMS — Free for Learners, Best and Affordable for Coaches

Eduarn isn’t just a training provider — it’s powered by one of the most advanced AI-enabled Learning Management Systems (LMS) in the world.

• 🧑‍🎓 For Learners (Free): Enjoy 100% free access to our LMS with smart course recommendations, AI tutors, and personalized dashboards.

• 🧑‍🏫 For Coaches: Eduarn offers the best and most affordable LMS platform to create, manage, and monetize courses with AI analytics and engagement tools. 

With Eduarn’s AI-integrated LMS, both learners and educators can thrive in an intelligent, data-driven learning environment built for the future of tech.

---

Conclusion

In 2026–2027, the fusion of AI, Containers, Kubernetes, and Podman will define the next generation of DevOps and cloud computing.
Whether you’re an individual seeking career growth or an enterprise investing in skill transformation, Eduarn provides the most advanced, affordable, and personalized learning experience available today.

Start your AI-driven container training journey now at www.eduarn.com — the best platform for online, retail, and corporate training.

Eduarn: The best LMS, free for learners, and the most affordable for coaches — empowering the next generation of AI and cloud professionals.

 

https://www.eduarn.com/?courseoid=5&ref=1#course 

https://www.eduarn.com/?courseoid=15&ref=1#course 

Eduarn – Best LMS/TMS in India | Affordable, Easy-to-Setup, White-Label Training Platform

 

Looking for the best LMS (Learning Management System) or TMS (Training Management System) that is easy to set up, affordable, and perfect for corporate, online, or retail training?

Meet Eduarn — your all-in-one white-label LMS platform designed for organizations, institutes, and individual trainers who want to deliver professional online learning and training at the lowest price.

---

🌐 Quick Setup – Get Started Instantly

Eduarn is one of the easiest LMS platforms to set up. In just a few steps, you can start delivering engaging courses to your learners:

1. 🏢 Organization Setup – Create and customize your portal with your brand logo and colors (complete white-labeling support).

2. 👩‍💼 Role Management – Assign roles like Admin, Trainer, HR, or Student with full control and permissions.

3. 📚 Course Creation – Upload unlimited courses, add your own videos, or host live classes through Zoom, Google Meet, or Teams.

4. 🗓️ Course Offerings – Schedule classes, set prices, coupons, and manage batches easily.

5. 🧠 Tests & Quizzes – Evaluate learners using smart test modules with instant grading.

6. 💬 Feedback & Surveys – Gather student feedback to improve your content and delivery.

7. 💳 Finance & Reports – Generate payment, enrollment, and completion reports with a single click.

8. 🎓 Learning Progress Tracking – Allow learners to pause, resume, and track course progress anytime, anywhere.

---

💼 Ideal for All Types of Training

Eduarn is the best LMS for:
✅ Corporate Training – Manage employee learning and skill development easily.
✅ Online Coaching – Perfect for individual trainers or academies running online classes.
✅ Retail Training – Train store staff or sales teams efficiently across locations.
✅ Institutes & Schools – Organize digital classrooms with live and recorded content.

---

⚙️ Power-Packed Features

• 🌟 Easy-to-setup LMS – No technical skills required

• 💸 Cheapest and most affordable LMS in India, and world-wide 

• 🧭 Intuitive dashboard for admins and trainers

• 🎥 Unlimited video uploads

• 🧾 Detailed finance and course performance reports

• 📱 Mobile-friendly and cloud-based

• 🏷️ 100% White-label platform – run under your own brand

• 🔒 Secure and scalable

---

💰 Pricing & Offer

Get access to Eduarn LMS/TMS for just
🎯 ₹12,000 per year

Includes:
✅ Unlimited course setups
✅ Unlimited users
✅ Full white-label branding
✅ Complete reporting and analytics

📅 Limited Period Offer – Valid until 31st December 2025!

---

🏆 Why Eduarn Is the Best LMS for You

Because Eduarn combines power, simplicity, and affordability — giving you the best LMS experience without the high cost. Whether you’re a corporate, educational institute, or solo trainer, Eduarn helps you train smarter and grow faster.

👉 Visit www.eduarn.com and book your demo today!

 

www.eduarn.com GUI:

 

Course and Course Offering Setups: 

  

 

 

 

 

 

 

 

🚀 Why Apache Spark (or PySpark) is Essential for AI, Data Engineering, and Machine Learning

 In today’s data-driven world, organizations are generating more information than ever before. The challenge isn’t just collecting data — it’s transforming that data into insights that drive smarter business decisions. That’s where Apache Spark and PySpark come in.

---

🔥 What is Apache Spark?

Apache Spark is an open-source, distributed computing system designed for processing large-scale data quickly and efficiently. Unlike traditional data processing frameworks that rely heavily on disk-based operations, Spark performs in-memory computations, making it incredibly fast for large datasets.

PySpark is Spark’s Python API — allowing developers, data engineers, and data scientists to harness Spark’s power using the simplicity and versatility of Python.

 

---

🧩 Why Spark Matters for AI, Data Engineering, and ML

1. Speed and Scalability

Spark can process terabytes or even petabytes of data across distributed clusters, all while maintaining impressive speed. This scalability is crucial when training machine learning models or running ETL (Extract, Transform, Load) pipelines on massive datasets.

2. Unified Framework

Spark supports multiple workloads under one ecosystem — including data preparation, ETL, streaming analytics, and machine learning. This unified approach simplifies workflows for data engineers and data scientists who often need to move seamlessly between data transformation and model training.

3. PySpark for Machine Learning (MLlib)

Spark’s built-in MLlib library provides a robust set of scalable machine learning algorithms — from classification and regression to clustering and recommendation systems. With PySpark, AI practitioners can integrate MLlib with Python’s powerful ecosystem (NumPy, Pandas, TensorFlow, etc.) for end-to-end ML workflows.

4. Real-Time Data Processing

Modern AI systems thrive on real-time insights. Spark Streaming enables continuous data ingestion and analysis — perfect for use cases like fraud detection, predictive maintenance, and real-time recommendations.

5. Big Data + AI Integration

For data engineers, Spark is the backbone of most Big Data pipelines. For AI teams, it’s the bridge between raw data and intelligent insights. Together, Spark and PySpark make it possible to train AI models on massive, distributed datasets — something traditional single-node systems struggle with.

---

⚙️ Use Cases Across Industries

• Finance: Fraud detection and risk analysis using real-time Spark streaming.

• Retail: Personalized recommendations powered by PySpark MLlib.

• Healthcare: Large-scale predictive analytics on medical and genomic data.

• Manufacturing: Real-time IoT data processing for predictive maintenance.

---

🎯 Final Thoughts

In the world of AI, Data Engineering, and Machine Learning, efficiency and scalability are everything. Apache Spark — especially through PySpark — empowers teams to process, analyze, and model data at scale, turning complex data challenges into actionable intelligence.

If you’re looking to upskill in Apache Spark or PySpark and learn how to apply it to real-world AI and Data Engineering problems, check out www.eduarn.com — offering online, retail, and corporate training programs to accelerate your data career.

🚀 Simplify IT Automation with AWX (Automation Controller): End-to-End Guide and Demo

 In today’s fast-paced DevOps world, automation has become the backbone of efficient IT operations. Teams are constantly looking for tools that simplify repetitive tasks, improve collaboration, and increase productivity across infrastructure environments. One of the most powerful and open-source automation management tools available today is AWX (Automation Controller).

If you’ve ever struggled with managing complex command-line operations or found it difficult to collaborate on automation tasks in team environments, AWX can be a game changer. It offers an intuitive web-based interface, API access, and role-based controls to help DevOps and SRE teams streamline their workflows effortlessly.

---

💡 What is AWX?

AWX is the open-source version of Ansible Tower, designed to provide a visual and centralized platform for managing Ansible playbooks, inventories, and workflows. Built by Red Hat, AWX acts as a control plane for your automation environment—allowing you to execute playbooks, manage permissions, schedule jobs, and monitor execution results all from a web browser.

In simple terms, AWX makes Ansible accessible to everyone—even those who may not be comfortable working exclusively with the command line.

 

---

🔧 Why Use AWX (Automation Controller)?

• Centralized Management: Manage all your playbooks, credentials, and inventories in one place.

• Team Collaboration: Assign roles and permissions for different users and teams.

• Web UI & API: Run jobs and monitor progress from any device.

• Scalability: Ideal for both small deployments and enterprise-scale automation.

• Open Source: Completely free under the Apache License 2.0.

While AWX is community-supported, organizations seeking enterprise-grade support and features can explore the Red Hat Ansible Automation Platform, which is the commercial version of AWX.

---

🎓 What You’ll Learn in This 20-Minute Demo

Our latest video, “End-to-End AWX / Ansible Tower / Automation Controller by Example in 20 Minutes,” provides a hands-on walkthrough of how to set up and use AWX effectively.

Watch it here:
👉 AWX Demo Video on YouTube

This session covers:

• AWX / Tower Prerequisites

• Login and Setup

• Creating Organizations and Users

• Managing Credentials and Inventories

• Adding Projects and Templates

• Launching Jobs and Workflows

Each section is explained with real-world examples and clear demonstrations, making it easy for beginners and professionals alike to follow along.

---

🧠 Key AWX Concepts Explained

• Organizations: Logical groups that define who can access what resources.

• Users: Individuals with assigned permissions and roles.

• Credentials: Authentication details (passwords, SSH keys) needed to run Ansible playbooks.

• Inventory: A collection of hosts or systems managed by AWX.

• Projects: Collections of playbooks that can be sourced from Git or other repositories.

• Templates: Predefined job configurations that allow you to execute playbooks with a single click.

• Workflows: Chains of job templates that enable complex automation pipelines.

By mastering these core components, you can orchestrate complex automation scenarios with minimal effort.

---

🌍 Why AWX and Ansible Tower Matter

In the modern cloud and DevOps landscape, automation isn’t just a convenience—it’s a necessity. AWX and Ansible Tower empower teams to focus on innovation rather than repetitive manual tasks. Whether you’re deploying applications, configuring infrastructure, or maintaining cloud environments, these tools offer a centralized, transparent, and secure way to manage automation at scale.

From startups to large enterprises, AWX is helping organizations accelerate digital transformation, reduce human error, and improve operational consistency across environments.

---

🎥 Ready to Learn by Example?

Don’t miss the full step-by-step demo video that walks you through everything from installation to job execution—all in just 20 minutes!
👉 Watch the Full AWX / Ansible Tower Demo

---

🏁 Final Thoughts

AWX bridges the gap between command-line Ansible and enterprise automation platforms. It gives teams the flexibility of open-source tools while providing a user-friendly interface for managing complex automation environments. Whether you’re an individual learning DevOps automation or part of a corporate IT team, mastering AWX is a great step forward in your automation journey.

---

📘 Learn and Grow with Eduarn

Looking to enhance your technical skills or provide corporate training for your team?
Explore online retail and corporate training programs tailored for IT professionals at 👉 www.eduarn.com

📊 Grafana vs. Prometheus: 5 Key Features and 4 Crucial Differences | DevOps Monitoring Tools 🚀

 In the fast-evolving world of DevOps and Cloud Monitoring, two giants stand tall — Grafana and Prometheus. Whether you’re a beginner stepping into the DevOps universe or a seasoned engineer optimizing complex Kubernetes clusters, understanding Grafana vs. Prometheus is essential. These tools form the beating heart of modern observability stacks, empowering teams to visualize, monitor, and react to data in real time.

👉 If you’re serious about mastering DevOps monitoring, don’t miss this exclusive video training session:
🎥 Watch now: 

 

And for those ready to go deeper, join professional online training programs for individuals, retail learners, and corporate teams at 🌐 www.eduarn.com — your one-stop destination for DevOps excellence.

---

🚀 What is Prometheus?

Prometheus is an open-source monitoring system developed originally by SoundCloud. It’s built for reliability, scalability, and performance, collecting metrics as time-series data. Every data point is timestamped, allowing precise monitoring and alerting across infrastructure and applications.

It doesn’t just store numbers — it gives you the power of PromQL (Prometheus Query Language) to slice, dice, and analyze data at lightning speed. Prometheus integrates seamlessly with Kubernetes, Docker, and hundreds of exporters, making it a go-to solution for cloud-native observability.

---

🌈 What is Grafana?

Grafana, on the other hand, is the ultimate visualization and dashboarding tool. While Prometheus is the powerhouse behind data collection, Grafana turns that data into stunning, interactive dashboards that speak volumes.

Think of Prometheus as the brain and Grafana as the face of your monitoring system. With Grafana, you can visualize metrics from multiple data sources — Prometheus, InfluxDB, ElasticSearch, AWS CloudWatch, and more — all on one screen.

---

🌟 5 Key Features That Make Grafana and Prometheus Unbeatable

1. Seamless Integration

Prometheus and Grafana work hand-in-hand. Prometheus collects and stores the data; Grafana visualizes it. Together, they form a full-stack monitoring solution loved by DevOps teams worldwide.

2. Powerful Query Capabilities

Prometheus’s PromQL gives you deep analytical power. You can perform advanced mathematical operations, create complex metrics, and detect anomalies in seconds.

3. Customizable Dashboards

Grafana’s drag-and-drop dashboard builder allows you to create personalized views for developers, network admins, and business teams alike. It’s open, flexible, and breathtakingly beautiful.

4. Alerting & Notification System

Prometheus can trigger alerts based on thresholds, and Grafana can visualize those alerts with integrated tools like Slack, PagerDuty, or Microsoft Teams, keeping your response time minimal.

5. Scalability & Flexibility

From small startups to enterprise-grade systems, both Grafana and Prometheus scale effortlessly. They fit anywhere — on-premises, in the cloud, or in hybrid infrastructures.

---

⚔️ 4 Crucial Differences Between Grafana and Prometheus

Feature	Prometheus	Grafana
Purpose	Data collection and alerting	Data visualization and analytics
Data Storage	Time-series database	No storage (relies on external data sources)
Query Language	PromQL	Grafana Query Editor (varies by source)
Primary Use	Monitoring & alerting engine	Visualization & dashboarding platform

In short: Prometheus gathers intelligence, Grafana tells the story.

---

💡 Why Learn Grafana and Prometheus?

In today’s DevOps-driven digital world, knowing how to configure Prometheus exporters, set up Grafana dashboards, and build alert pipelines is a career accelerator. Companies across the globe — from startups to tech giants — rely on these tools to keep their systems reliable, available, and high-performing.

If you’re aiming for DevOps, SRE, or Cloud Monitoring roles, these skills aren’t optional — they’re essential.

---

🎓 Start Learning Today

🚀 Watch our expert-led video: https://youtu.be/sZx-Lowp3Hg
🌐 Join online training at: www.eduarn.com

At Eduarn, we specialize in online DevOps training for retail learners, professionals, and corporate teams. From beginner-level monitoring to advanced observability pipelines, our hands-on programs make you job-ready and industry-certified.

Don’t just monitor — master the art of observability with Grafana and Prometheus today!

Cracking TCS as a Fresher: Interview Process + How Eduarn Training & Mentorship Can Help

 

Graduating is a big milestone — but the next big test is often the corporate interview. TCS (Tata Consultancy Services) is a top goal for many freshers, and rightly so. But its process is competitive, multi‑phased, and demands not just technical skills but the right mindset. Here Are 5 Things You Must Know Before Your Job” emphasizes, mindset and resilience often distinguish those who succeed from those who falter.

In this post, I’ll walk you through the TCS fresher interview journey — with real examples — and then show how Eduarn’s online training & mentorship can give you an edge in each stage.

---

TCS Fresher Interview Process (Step by Step)

Below is a typical path many freshers go through when applying to TCS:

1. Eligibility / Screening

Before even sitting for tests, you must satisfy eligibility criteria:

• Full‑time degree (BE, BTech, MCA, etc.)

• Minimum academic percentages (often ~60% in 10th, 12th, graduation)

• No active backlogs

• Limited gaps (usually ≤ 2 years)

Failing here—or having discrepancies in your documents—can disqualify you early.

2. TCS NQT / Aptitude & Coding Test

TCS often uses the TCS National Qualifier Test (NQT) or a similar aptitude + coding screening. This stage typically includes:

• Quantitative aptitude (probability, permutations, time‑work, arithmetic)

• Verbal/English (comprehension, grammar)

• Logical reasoning

• Coding / programming section (for roles where coding is expected)

If you clear this, you move to interviews.

3. Technical / Core Round

In the technical interview, panels test your fundamentals:

• Data structures, algorithms, complexity

• Conceptual questions in database, OS, networking

• Deep dive into your projects (what you built, challenges faced, tradeoffs)

• Coding tasks (reverse strings, check palindrome, small optimizations)

For instance, some candidates recall being asked to reverse words in a string, or to build a small algorithm and explain its time complexity.

4. Managerial / Decision-making Round (sometimes merged)

This round probes your thinking, situational judgments, and soft skills:

• How you handle conflicts or pressure

• Scenarios like “if requirement changes” or “team deadline shifts”

• Why you chose certain technical decisions in your project

Sometimes this is merged with the technical round.

5. HR / Behavioral Round

Here, the focus is on you as a person:

• “Tell me about yourself”

• “Why TCS?”

• Strengths, weaknesses

• Are you open to relocation?

• Gaps in education or other anomalies

This round is shorter (10–20 mins) but critical for buy‑in from HR.

6. Offer, Background Check & Onboarding

If you pass all rounds, you receive an offer. Then there’s background verification of your academics, identity, and any prior work (if applicable). Finally, there’s the joining process and training (which sometimes gets delayed depending on business needs).

---

Where Many Freshers Trip Up — Real Pitfalls

• Overconfidence / Underpreparation: Thinking one test or interview will sail through without rigorous prep

• Vague project descriptions: Panel will dig – be ready to talk about your exact role, metrics, algorithms

• Document discrepancies or gap issues: Even small gaps or mismatches kill your candidature

• Poor communication: Language, articulation, confidence all matter

• Lack of patience: Many expect offers immediately; sometimes there are delays

As Neeshi Kumar’s article suggests, fresh graduates often overestimate how quickly things will move. Having resilience and a willingness to learn from rejection is key.

---

How Eduarn’s Online Training & Mentorship Can Boost Your Chances

To truly stand out, you need more than self-study. Eduarn is a training & learning platform that offers instructor-led classes, mentorship, hands-on labs, and project work. Eduarn

Here are ways Eduarn can help you prepare specifically for TCS or any tech company interview:

A. Structured Learning with Mentors

• Eduarn offers live online classes as well as self-paced / flexi learning options with recorded sessions, courseware, and labs. Eduarn

• They also provide one-to-one AI training / mentorship where a dedicated mentor helps you with clarifications, code review, and feedback. Eduarn

This means you’re never stuck alone — you can ask real questions, get feedback on code, and refine your approach.

B. Project-Based Training You Can Showcase

• In their AI / data science tracks, you build real-world projects (for example, an end-to-end spam classifier) and deploy them. Eduarn

• These deployed projects make for excellent portfolio pieces during interviews. Panelists often ask: “Show me your work, your GitHub, your live demos” — this gives you that ammunition.

C. Domain / Skill Upgradation

• Eduarn offers domain courses like Python & Data Science / AI with intensive instructor-led training. Eduarn

• They also have cloud / Azure / AWS / DevOps tracks (e.g. Azure certification training) which help you expand your skills beyond just basic coding. Eduarn

• Weekend courses (e.g. AWS weekend for women) help you pick up cloud skills in parallel. Eduarn

These extra skill sets can help you answer advanced questions or show that you’re self-driven.

D. Interview & Resume Readiness Support

Many training platforms (including Eduarn) often bundle career support, such as:

• Mock interviews, coding challenge practice

• Resume review and LinkedIn profile polishing

• Guidance on how to present your projects and explain your logic

This helps you align your technical learning with what TCS (or any IT firm) expects in interviews.

E. Flexible / Affordable Training

• Eduarn’s “one-to-one” mentorship model is pitched as affordable compared to expensive bootcamps. Eduarn

• Their flexible scheduling means you can learn while still managing final year, projects, or other commitments. Eduarn

This helps you balance both learning and applying to interviews without burning out.

---

Integrating Eduarn With Your TCS Interview Prep: Sample Plan

Here’s a sample 8‑week plan that blends TCS interview prep with Eduarn’s offerings:

Week	Focus	Eduarn Component	Your Tasks
1	Basics, Aptitude, Logical Reasoning	Use Eduarn’s foundational materials / mentorship support	Solve 30 aptitude questions/day, track weak areas
2	Coding fundamentals (Python, arrays, strings)	Take Eduarn live sessions, ask doubts to mentors	Write small programs daily, maintain GitHub
3	Data structures & algorithm basics	Eduarn’s class + mentor support	Implement linked lists, stacks, queues, etc.
4	Project work & portfolio building	Use Eduarn projects (e.g. spam classifier) Eduarn	Push project to GitHub, deploy it
5	TCS-specific pattern practice	Mentor review of mock tests	Take past TCS NQT mocks, time yourself
6	Technical interview simulations	Mock interviews via Eduarn or external	Record and analyze your performance
7	Managerial / situational / HR prep	Mentor roleplay sessions	Prepare 20 common HR questions and practice
8	Final polishing & document readiness	Mentor feedback, resume / GitHub review	Ensure documents, CV, projects are final

By the end, you'll have:

• A strong grip on aptitude, coding, data structures

• At least one live project to present

• Mentored feedback, mock interview experience

• A polished resume and confidence to face HR rounds

---

Conclusion

TCS’s fresher hiring process is rigorous. It filters through many stages: eligibility, aptitude, technical rounds, managerial / situational, and HR. The competition is fierce, and many fresh graduates fail not because they lack ability, but because of preparation gaps or a weak mindset.

Adding Eduarn’s training & mentorship into your preparation gives you several advantages: structured learning, project exposure, mentor feedback, interview readiness, and skill upgradation beyond just coding. Combined with persistence, this can make the difference between being ignored and being selected.

Note: these are common interview process, we cant guaranty, and no job offering.  

 Top demanded: