Writing | Dineshkarthik RaveendranPractical notes on data engineering, platform strategy, and technology judgment.https://dineshkarthik.me/blog2026-06-17T00:00:00+00:00Dineshkarthik Raveendranhello@dineshkarthik.mehttps://dineshkarthik.me/Vibe coding vs agentic engineering: what the new SDLC means for leadershttps://dineshkarthik.me/blogs/vibe-coding-vs-agentic-engineering2026-06-17T00:00:00+00:002026-06-17T00:00:00+00:00AI makes generation cheap; verification, judgment, and direction are the new craft. A leader's synthesis of Google's new SDLC whitepaper on vibe coding and agentic engineering.AI-native data teams: what drives success and what leads to failurehttps://dineshkarthik.me/blogs/ai-native-data-teams-success-failure2026-05-26T00:00:00+00:002026-05-26T00:00:00+00:00Most teams call themselves AI-native without changing how data, governance, or delivery actually work. Here's what actually separates the teams that deliver durable value from those stuck in demo mode.What executives actually need from a data leaderhttps://dineshkarthik.me/blogs/what-executives-actually-need-from-a-data-leader2026-05-08T00:00:00+00:002026-05-08T00:00:00+00:00Executives don't need more dashboards or a longer tool shopping list. They need a data leader who creates trust, improves decisions, and builds scalable organizational leverage.Where AI fits in the modern data stackhttps://dineshkarthik.me/blogs/where-ai-fits-in-the-modern-data-stack2026-01-14T00:00:00+00:002026-04-29T00:00:00+00:00AI doesn't sit outside your data stack. It works best where trust, governance, and reliable foundations already exist. Here's how to think about AI as an extension of modern data capabilities.How to reduce data platform complexity without slowing teams downhttps://dineshkarthik.me/blogs/reduce-data-platform-complexity2025-11-28T00:00:00+00:002026-04-28T00:00:00+00:00A good data platform does not eliminate complexity entirely. It contains complexity in the right places and removes it from everyday delivery.Why most data teams are busy but not effectivehttps://dineshkarthik.me/blogs/why-most-data-teams-are-busy-but-not-effective2025-09-15T00:00:00+00:002026-04-28T00:00:00+00:00Data teams often appear highly productive—shipping dashboards, fixing pipelines, responding to requests—yet struggle to move the business forward. The problem isn't effort; it's a system designed for activity rather than leverage.What I look for in a modern data platformhttps://dineshkarthik.me/blogs/what-i-look-for-in-a-modern-data-platform2025-07-20T00:00:00+00:002026-04-27T00:00:00+00:00A practical framework for evaluating data platforms based on reliability, cost, developer experience, governance, and speed to insight.Data Warehouse, Data Lake, or Lakehouse? A pragmatic guidehttps://dineshkarthik.me/blogs/data-warehouse-data-lake-or-lakehouse2025-06-15T00:00:00+00:002026-04-26T00:00:00+00:00A practical decision framework for choosing data architecture based on workloads, governance needs, and team maturity.Become a Data Engineer: Master the Fundamentalshttps://dineshkarthik.me/blogs/master-the-fundamentals2025-01-25T00:00:00+00:002026-04-26T00:00:00+00:00A guide for building the core technical judgment behind data engineering, from systems thinking to the practical foundations that make teams effective.Taming the Data Pipeline Beast: Best Practices for Robust Data Engineeringhttps://dineshkarthik.me/blogs/taming-data-pipeline-beast2024-05-29T00:00:00+00:002026-04-26T00:00:00+00:00Design patterns for data pipelines that are observable, maintainable, resilient, and ready for real organizational scale.Surfing the Technological Tidal Waves: Embracing the Hype and Conquering FOMOhttps://dineshkarthik.me/blogs/surfing-technological-tidal-waves2023-07-15T00:00:00+00:002026-04-26T00:00:00+00:00A measured approach to new technology waves, experimentation, and separating durable value from noise.