Hi, I am Nilesh
I am a data professional, passionate about solving problems with data & experience.
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Context Engineering in AI: Designing Intelligent Information Environments
Artificial intelligence models, especially large language models (LLMs), do not operate in a vacuum. Their performance depends heavily on the context—the data, instructions, and constraints that shape their reasoning. Context engineering is the discipline of designing this information environment so that AI systems produce reliable, accurate, and aligned outcomes.
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The Blueprint's Journey: A History of System Design and Its Critical Need
The story of system design is one of escalating complexity and scale. What began as organizing tasks for a single machine has evolved into orchestrating a global network of computers.
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Python for Data Engineering: The Swiss Army Knife of the Data World
When I first started working in data engineering, I assumed it was all about big fancy tools—Hadoop, Spark, Kafka, and massive SQL queries. But soon, I noticed something interesting: every senior engineer on the team always had a little Python script running somewhere.
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Understanding Apache Spark Architecture: A Simple Guide
A few years back, I was working on a project where we had to process millions of records every night. Our old system was slow—it took hours to run. Then we discovered Apache Spark, and suddenly those hours turned into minutes.
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Scaling in System Design: From Tea Stall to Global Café
When I was a kid, there was a tea stall near my school. Every morning, ten to fifteen people gathered there, sipping hot chai before rushing to work. The stall had one kettle, one helper, and one wooden bench. Life was smooth.
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From Buckets to Pipelines: The Art of Data Engineering
A few months ago, a friend asked me: “Why do companies even need data engineers? Isn’t it just about storing data somewhere and letting analysts use it?”
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The Art of Building Systems: Core Principles of System Design
A few years ago, I was working with a small team on a product that was growing faster than we expected. In the early days, things were simple: one server, one database, and a handful of happy users. Life was good. But as the number of users grew, our system started to feel like an old scooter carrying too many passengers. It wobbled, it slowed down, and sometimes, it just broke down in the middle of the road.