Lokesh deesh V’s Post

🚀 Day 5/100 — Working with Persistent Storage 🧠 “Persistence transforms execution into continuity.” Systems become meaningful when they retain and retrieve information reliably. Today, I learned how Python interacts with files to store and retrieve persistent data. ⚙️ 🔧 Today’s focus areas: 📂 File Reading — Accessing stored data 📝 File Writing — Persisting new information 🔄 File Modes — Managing read and write operations 🎯 Data Persistence — Ensuring continuity across executions 🎯 The objective was to enable programs to maintain state beyond runtime. ✅ Day 5 complete: Persistent data handling established. ▶️ Day 6: Strengthening reliability through exception handling. Step by step. The system evolves. 🏗️ #Python #BackendDevelopment #100DaysOfCode #SoftwareEngineering

🐍 When it comes to building reliable, scalable data solutions, Python is our main programming language. It’s the tool that powers it all, from data pipelines and ETL to automation, testing, and orchestration. Need to work with large-scale distributed data? We’ve got you covered with #PySpark, combining Python’s flexibility with the power of Apache Spark. At DataEngi, we use #Python to: ✅ Build production-grade pipelines ✅ Process big data with PySpark ✅ Automate workflows and testing ✅ Integrate with tools like Airflow, Dagster, and dbt Let’s put Python to work for your data. 🔗 Learn more about our #Python development services: https://buff.ly/jzDRzMC #DataEngineering #ETL #BigData #SaaS

One thing I’ve been learning while building backend applications is that writing an API that works is only the first step. What really matters over time is maintainability. Some practices that have made a big difference in my projects: • Clear separation between routing, business logic, and database access • Consistent data validation using schemas • Proper error handling • Meaningful naming and clean structure These practices make APIs easier to test, maintain, and scale as projects grow. Good backend systems are not just functional — they are designed to be maintainable. #BackendDevelopment #Python #FastAPI #SoftwareEngineering #CleanCode #APIDesign

𝗘𝘃𝗲𝗿 𝘁𝗿𝗶𝗲𝗱 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗮 𝗹𝗮𝗿𝗴𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲𝗯𝗮𝘀𝗲 𝘆𝗼𝘂 𝗱𝗶𝗱𝗻’𝘁 𝘄𝗿𝗶𝘁𝗲? I recently built a 𝗥𝗔𝗚-𝗕𝗮𝘀𝗲𝗱 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲 𝗤𝗻𝗔 𝗦𝘆𝘀𝘁𝗲𝗺 that analyzes Python repositories and makes them searchable through natural-language queries. What started as a simple AST parser quickly turned into a systems challenge:  • handling large repositories  • avoiding repeated or circular retrieval of the same code  • tracking long-running indexing jobs asynchronously  • and exposing progress without blocking the user I focused on designing the core backend architecture: async indexing, job tracking via UUIDs, controlled retrieval, and query orchestration. 𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸 𝗮𝗻𝗱 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀:  • Python AST for structural parsing  • FastAPI for async APIs and background tasks  • Weaviate for semantic retrieval  • LangGraph for query reasoning control  • Docker for local infrastructure This project pushed me to think beyond “features” and focus on robust backend design, retrieval quality, and developer experience. Sharing a short UI demo below: GitHub Repo: https://lnkd.in/gVCUaCYM #BackendEngineering #SystemDesign #DeveloperTools #Python

Python-based toolkit for automated data quality checks. It helps identify missing data, invalid types, duplicates, outliers, and logical inconsistencies so datasets are cleaner, more reliable, and analysis-ready. A small step toward better data, stronger insights, and smarter decisions. #Python #DataQuality #DataAnalytics #DataScience #Automation #MachineLearning #DataEngineering

SQL Mastery: Complete. ✅ Python: Loading... 🐍 I just wrapped up my deep dive into SQL! From mastering complex JOINs and Subqueries to fine-tuning data logic with Window Functions and CTE optimizations, I’m officially comfortable structuring and querying data at scale. My goal is: I’m building a strong technical foundation to help international teams manage and automate their data workflows. Next stop: Python for Data. I’m self-studying this path because I’m obsessed with how we can use code to automate and scale data systems. If you’re an Engineer or a fellow learner, I’d love to connect! #SQL #DataEngineering #SelfTaught #Python #LearningInPublic #RemoteWork #TechJourney #BuildingInPublic

Data lineage traces your data's journey from source to destination. Where did this number come from? What would break if I changed this table? Who's using this data? Good lineage answers these questions. Bad lineage makes you grep through code. Tools like dbt give you SQL lineage for free. Orchestrators like Dagster give you Python lineage. The challenge is connecting them. https://lnkd.in/ea9NGZt6

Day 46 of my Data Engineering journey 🚀 Today I learned about scheduling and automation with Python an important step toward building real data pipelines. 📘 What I learned today (Automation in Python): • Why automation is essential in data engineering • Running scripts automatically instead of manually • Using Python’s schedule library • Understanding cron jobs for scheduled tasks • Automating repetitive data workflows • Building scripts that run daily or hourly • Thinking about reliability in automated jobs • Moving from scripts → pipelines In real data systems, data pipelines run automatically. No one manually runs scripts every day. Automation is what turns code into a real data pipeline. Why I’m learning in public: • To stay consistent • To build accountability • To improve daily Day 46 done ✅ Next up: connecting Python with databases 💪 #DataEngineering #Python #Automation #LearningInPublic #BigData #CareerGrowth #Consistency

Day 101: Automation vs Over-Engineering Not every workflow needs Airflow, Spark, and microservices. Sometimes a scheduled Python script delivers more value. Simplicity scales better than complexity. Tip: Choose the smallest solution that solves today’s problem. Have you ever over-engineered a data solution? #DataEngineeringLife #Automation #PracticalArchitecture

Most developers learn syntax. Very few build production-ready systems. Today I worked on: • Async API development using FastAPI • SQL database integration • Clean architecture using Spec-Driven Development I’m focused on building scalable, AI-ready backend systems with Python. Consistency > Motivation. What are you building this week? #Python #FastAPI #BackendDevelopment #AIEngineering #FullStack