Tanvir Ahmed’s Post

Switching from Python to Java: Coming from a Python-heavy background, working with Java has been a real shift in perspective. In Python, a lot is taken care of for you through powerful high-level abstractions. You can move quickly, write less code, and focus on solving problems. But Java? It makes you slow down in a good way. You start paying attention to details you might have overlooked before: type definitions, structure, and the mechanics behind what your code is actually doing. It demands more explicitness, more discipline, and a deeper level of understanding. And that’s the beauty of it. Different languages, different strengths, but stepping outside your comfort zone is where real growth happens. https://lnkd.in/deNbabM5 #Java #Python #SoftwareEngineering #CodingJourney #LearningToCode

Learn Python Programming Using Java Skills — Data Validation with PySpark & Pytest A practical, beginner-friendly guide for Java developers stepping into Python and data engineering. Real code. Real comparisons. #Python #Java #PySpark #Pytest #DataEngineering #BeginnerPython

🚀 Python Learning Journey – Day 3 📅 13/03/2026 Continuing my Python learning journey at Global Quest Technologies, today’s session focused on exploring Python implementations and understanding how it compares with Java. 🔍 Topics Covered: 🔹 Flavours of Python 🔹 Differences between Python and Java 💡 Key Takeaways: ✔️ Learned about different Python implementations: • CPython – Default and most widely used • Jython – Runs on the Java Virtual Machine (JVM) • IronPython – Works with the .NET framework • PyPy – Focused on performance using JIT compilation. ✔️ Understood key differences between Python and Java : 🔹 Python is interpreted and dynamically typed, while Java is compiled and statically typed 🔹 Python emphasizes simplicity and readability, whereas Java focuses on structure and performance 🔹 Python uses indentation, while Java uses braces {} 🔹 Java generally offers faster execution, while Python provides ease of development and debugging. This session helped me gain clarity on how Python works across different environments and how it stands in comparison with Java in real-world applications. Grateful for the continuous guidance from Global Quest Technologies and G.R NARENDRA REDDY Sir. Looking forward to applying these concepts and exploring more advanced topics ahead! #Python #LearningJourney #CorePython #Programming #Java #TechSkills #Upskilling #FullStackDevelopment 🚀

Your Kafka clusters are running, producers are producing, and consumers are consuming. Everything seems to be working fine until your Python service needs to talk to your Java service and suddenly, you begin to see type mismatches between what Python service sends to Kafka and what your Java service can consume.. explore schema registry for enforcing types https://lnkd.in/eRJbSCe6

Hit 5⭐ in Java & Python on HackerRank. Key takeaways from the journey: • Improved problem-solving and logical thinking • Focused on writing clean and efficient code • Got hands-on with arrays, strings, and recursion • Learned the importance of time & space complexity • Practiced consistency and pattern recognition Next step: deeper DSA and applying these skills to real-world projects. #Java #Python #DSA #ProblemSolving #CodingJourney #HackerRank

In Java, private means private. In Python, it means: “I trust you not to look.” I was exploring encapsulation and discovered something interesting. In Java, access is enforced. In Python, it’s… negotiated. A double underscore (__attr) doesn’t truly hide anything. It just renames it. Which means: You *can* still access it — if you know how. That realization changed how I think about class design. Java protects the code. Python trusts the developer. Two different philosophies. Which one do you prefer? 👇 Curious to hear your perspective #Python #Java #OOP #SoftwareEngineering #LearningInPublic

Stop choosing between Java and Python. In 2026, the market demands "Dual-Stack" proficiency. The roadmap to becoming a high-performance architect has changed. With Java 26 arriving and AI integration becoming the standard, mastering both Java and Python is no longer optional - it's a competitive advantage. At MyExamCloud, we use our proven PPA (Plan, Practice, Achieve) methodology to ensure you don't just study-you certify. Link in comments. #Java26 #PythonCertification #SoftwareArchitecture #MyExamCloud #CareerGrowth

Explore why Python is the ultimate choice for data science, engineering, and analytics compared to Java. Learn about its versatility, how it borrows the best features from other languages, and why it's the most efficient tool for building data pipelines and processing complex file formats. #python #java #dataengineering #datascience #dataanalytics #programminglanguages #datapipelines #softwaredevelopment

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering

Did you know Python is actually older than Java? Yes, read that again. Python’s clean, indentation-driven syntax makes it feel like a modern language, but its first public release was all the way back in 1991. Java, on the other hand, didn’t see its first public release until 1995. Modern isn’t always new, sometimes it’s the ideas that were just ahead of their time. #Java #Python