Spark Isn’t Magic: What Twenty Years of Data Engineering Taught Me About Distributed Processing

Every few years, a technology emerges that fundamentally changes how we think about data processing. MapReduce did it in 2004. Apache Spark did it in 2014. And after spending two decades building data pipelines across enterprises of every size, I’ve learned that the difference between a successful Spark implementation and a failed one rarely comes […]

Read more →

Data Pipelines for LLM Training: Building Production ETL Systems

Building production ETL pipelines for LLM training is complex. After building pipelines processing 100TB+ of data, I’ve learned what works. Here’s the complete guide to building production data pipelines for LLM training. Figure 1: LLM Training Data Pipeline Architecture Why Production ETL Matters for LLM Training LLM training requires massive amounts of clean, processed data: […]

Read more →

Your Copilot Is Watching: The Real Story Behind AI Coding Assistants in 2025

Something shifted in how we write code over the past two years. It wasn’t a single announcement or product launch—it was the gradual realization that the cursor blinking in your IDE now has a silent partner. GitHub Copilot crossed 1.8 million paid subscribers in 2024. Cursor raised $60 million at a $400 million valuation. Amazon […]

Read more →

Testing AI-Powered Frontends: Strategies for LLM Integration Testing

Testing AI-Powered Frontends: Strategies for LLM Integration Testing Expert Guide to Testing AI Applications with Confidence I’ve tested AI applications that handle streaming responses, complex state, and real-time interactions. Testing AI frontends is different from traditional web apps—you’re dealing with non-deterministic outputs, streaming data, and asynchronous operations. But with the right strategies, you can test […]

Read more →