Abhishek

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Abhishek

Abhishek

@LoyalDataEng

Learning. Building. Scaling. 🚀 Data Engineer sharing projects, tips & real-world data engineering journeys.

เข้าร่วม Temmuz 2026
130 กำลังติดตาม122 ผู้ติดตาม
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Abhishek
Abhishek@LoyalDataEng·
🚀 Starting Tomorrow: 30 Days of Data Engineering I'll be sharing everything I learn about: 🐍 Python for Data Engineers 🗄️ SQL for Data Engineers What to expect every day: ✅ Topic-by-topic learning ✅ Easy-to-understand notes ✅ Interview questions I've personally faced during Data Engineering interviews ✅ Tips, tricks & practical examples If you're preparing for Data Engineering or planning to switch into this field, this series is for you. I'm new here, so your support, follows, reposts, and feedback would mean a lot. Let's learn and grow together! 🤝 #DataEngineering #Python #SQL #AI #BigData #DataEngineeringRoadmap #LearningInPublic #TechTwitter #InterviewPreparation #DataEngineer
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Sravya Reddy
Sravya Reddy@CodeWithSravya·
Be honest: Who is better at teaching DSA?
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Abhishek
Abhishek@LoyalDataEng·
🐍 Day 2/30 – Variables & Data Types in Python Every Python program starts with one thing: Variables. If you don't understand variables and data types, writing Python code becomes much harder. In today's lesson, we'll cover: 📌 What is a Variable? 📌 Variable Naming Rules 📌 Dynamic Typing in Python 📌 Built-in Data Types 📌 Numbers (int, float, complex) 📌 Strings 📌 Boolean 📌 Type Checking ("type()") 📌 Type Conversion 📌 Real-world Examples 📌 Interview Questions 📌 Quick Revision Cheat Sheet Everything is explained with diagrams, examples, and beginner-friendly visuals. 📖 Part 1: Fundamentals (This Post) 🔥 Part 2: Deep Dive • Memory Allocation • Mutable vs Immutable Objects • Variable References • Object Identity ("id()") • Type Casting Internals • Common Mistakes • Best Practices • Interview Questions • Pro Tips 💾 Save this thread—you'll use these concepts throughout your Python journey. Tomorrow: Operators in Python ⚡ Follow @LoyalDataEng for daily Python & Data Engineering content. #Python #PythonProgramming #DataEngineering #LearnPython #Coding #Programming #DataEngineer #TechTwitter #100DaysOfCode #AI #MachineLearning
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Abhishek@LoyalDataEng

🚀 Day 1/30 – Python for Data Engineering 🐍 The journey officially begins! 🎉 Today's topics: 📌 What is Python? 📌 Why Python for Data Engineering? 📌 Python in the Data Engineering ecosystem 📌 Python execution flow & interpreter 📌 Program structure 📌 Your first Python program 📌 Python syntax rules 📌 Variables & basic data types 📌 User input 📌 Comments & indentation 📌 Common mistakes beginners make 📌 Python best practices 📌 Features of Python 📌 File types (.py, .pyc) 📌 Quick recap & pro tips 🔥 Bonus (In-Depth Sheet): ✅ Python execution explained ✅ Important concepts every beginner should know ✅ Sample code walkthrough ✅ Interview questions ✅ 5 beginner coding problems with solutions: • Sum of Digits • Reverse a Number • Armstrong Number • Palindrome Number • Factorial 💡 My goal is simple: Build a complete, beginner-friendly 30 Days of Python for Data Engineering series with visual notes, practical examples, coding challenges, and interview preparation. 📌 Save this thread and follow @LoyalDataEng so you don't miss Day 2! #Python #DataEngineering #PythonProgramming #DataEngineer #LearnPython #Coding #TechTwitter #100DaysOfCode #BigData #PySpark #SQL #Programming #AI #MachineLearning

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Abhishek
Abhishek@LoyalDataEng·
@PopBase Atleast there is some peace today. We don't want these fifa fan on my Timeline everytimes. They are very irritating to watch.
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Pop Base
Pop Base@PopBase·
For the first time this World Cup, there are no matches today.
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Abhishek
Abhishek@LoyalDataEng·
@sama I think our job is going to over.. What u say Sam bro? These models will replace us eventually.
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Sam Altman
Sam Altman@sama·
GPT-5.6 sol launches thursday! happy building
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Abhishek
Abhishek@LoyalDataEng·
⚡ Why Learn Apache Spark? Traditional tools struggle with massive datasets. Apache Spark enables fast, distributed data processing at scale. It's widely used for ETL pipelines, batch processing, data transformations, and big data analytics. If you want to become a Data Engineer, Spark is a must-have skillset. Resource 1 - Spark by Manish Kumar( This is one stop solution for Spark who knows Hindi) youtube.com/playlist?list=… Resource 2- Spark series by Ansh lamba 1. Apache spark youtu.be/FNJze2Ea780?si… 2.Pyspark youtu.be/94w6hPk7nkM?si… 3. pyspark Interview Questions youtu.be/fOCiis31Ng4?si… 4. Pyspark Real time scenarios youtu.be/Vch6iAnB1eY?si… 5.Another real time scenarios Questions youtu.be/-Gvnm9eS8v8?si…
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Abhishek
Abhishek@LoyalDataEng·
📊 Why Learn Data Fundamentals First? Before building data pipelines, understand what data is, how it's stored, and how it flows. Learn about structured vs. unstructured data, databases, data warehouses, data lakes, file formats, and data modeling. Strong data fundamentals make learning Data Engineering much easier. 🚀 Resource For Big data Fundamentals By @AnshLambaJSR -youtu.be/H5SHmiKTFsM?si… Resource for Data engineering Fundamentals By @parmardarshil07 - youtu.be/ZRz-7E-7X7c?si… youtu.be/hf2go3E2m8g?si… Another Alternative Resources for Data engineering Fundamentals By ansh Lamba-
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Abhishek
Abhishek@LoyalDataEng·
Everyone talks about becoming a Data Engineer. Very few show you how. Starting today, I'm building the most practical Data Engineering Roadmap using 100% free resources. No fluff. No paid courses. Just a clear path from beginner to job-ready. Follow along if you're serious about Data Engineering. #DataEngineering #Python #SQL #BigData #DataScience
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agentX
agentX@nikks_techie·
Today AI/ML gyan: Distributed Training Distributed Training is the process of training a machine learning model using multiple computers or multiple GPUs at the same time instead of just one. This helps train large models much faster because the workload is shared across multiple machines. Example: House Price Prediction Suppose you’re building an AI model to predict house prices using 100 million house records. If you train the model on a single computer, it may take 12 hours to finish because one machine has to process all the data. Instead, you use 4 computers to train the model together. The dataset is divided into four parts, and each computer trains on its portion at the same time. After each computer finishes its work, they share their results and combine what they’ve learned into a single model. Now, instead of taking 12 hours, the training completes in about 3 hours. The final model is the same, but it was trained much faster because multiple machines worked together. This process of using several computers or GPUs to train one machine learning model is called Distributed Training. Real-World Analogy Imagine you have to move 1,000 boxes from one warehouse to another. If one person moves all the boxes, it could take an entire day. Instead, you ask 10 people to help. Each person carries 100 boxes at the same time, so the work finishes much faster. Distributed training works the same way. Instead of one computer doing all the work, multiple computers share the workload to complete training more quickly. One-Line Summary Distributed Training is the process of training a machine learning model across multiple computers or GPUs simultaneously to reduce training time and handle very large datasets or models.
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Abhishek
Abhishek@LoyalDataEng·
@striver_79 Al's real impact isnot code generation. It's the shift toward validation, design, and higher-order engineering decisions and intent. The engineers who master that shift will define the next era.
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Striver | Building takeUforward
What has AI changed when it comes to coding? - does known things at a faster pace - shortened timelines - got us free from ratification of a lot of useless things - downsized team size - made learning easier for junior engineers Not AGI, but good that as engineers we have a lot more time to spend on design, scale and other things..
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Abhishek
Abhishek@LoyalDataEng·
@SalaarStanhk How much payout u get generally?? If ur engagement is like this
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John Wick
John Wick@SalaarStanhk·
Red almost all over my analytics portfolio over the week But i think it is pretty good what do you think? 🤔 What should i focus on?? I hope this week will be better for all of us ❤️
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John Wick@SalaarStanhk

The Structure is going very good 💯 Expected like 12k imps yesterday but got 14k I suppose it is going good 🤔 let's see what can i improve on, how are your stats? as tomorrow complete 1 week I will upload my vf week stats tomorrow i hope it will help someone

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Mishii 🌻
Mishii 🌻@mishi_not·
How many verified followers do you have ?
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Arunima Ganguly 🇮🇳
Arunima Ganguly 🇮🇳@ArunimaGanguly2·
payout from x for july 1st week credited to my bank account. which supercar should i book now? 😃
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Arunima Ganguly 🇮🇳
Arunima Ganguly 🇮🇳@ArunimaGanguly2·
sell some stocks and bought ice cream tonight :) minted for just 0.05 SOL even the character on the tv wanted a scoop.
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Abhishek
Abhishek@LoyalDataEng·
@AKirtesh It's amazing brother 👏 How much consistency and effort it take? In how much months this no reach? Could u tell so that we can also go there
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Kirtesh
Kirtesh@AKirtesh·
X payout breakdown: $608.08 I spend about 4 hours/day on X Output (28 days): - 413K impressions - 35.2K engagements - 13.6K replies - 17.5K likes - 516 reposts - 267 bookmarks - 67 shares Per day - $21.72 earned - 14.75K impressions - 1.26K engagements - 250+ replies My tactic: create engaging posts, then reply to every single comment. 60% of my replies are to comments on my own posts.
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Abhishek
Abhishek@LoyalDataEng·
@Srishhh9 Consistency matters only? Right? Eventually it will reach to audience?
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Abhishek
Abhishek@LoyalDataEng·
@aman_pb06 How u have done?. How much time it has taken???
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Aman.pb🚀
Aman.pb🚀@aman_pb06·
I'm now eligible to receive payout! 5M impressions, 500 verified followers I have done it all in a span of 7 months If I can you can do it too 🫵🫵
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Aslam
Aslam@aslambeg84·
370 → 444 didn't expect this much support. can we hit 500 next? if you're in tech, say hello 👋
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Kartik
Kartik@KartikSharmaa_0·
Day 1 of Premium ✅ Let's Connect, Let's Grow Together
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