Ideas & Expert Perspectives
Practical thinking on AI, data engineering, and modern data architecture — written by practitioners, not theorists.
Posts
Agentic AI: The Next Leap in Enterprise Data Pipelines
Autonomous AI agents are moving beyond chatbots and into the core of enterprise workflows — orchestrating entire data pipelines with minimal human intervention. Here is what that shift means for your architecture.
February 18, 2026
ReadWhy Real-Time Feature Stores Are Becoming Non-Negotiable for ML Teams
Training a great model is only half the battle. The gap between offline experiments and online production predictions can silently kill ML ROI — and feature stores are the bridge most teams are missing.
January 9, 2026
ReadData Mesh vs. Data Fabric in 2026: Choosing the Right Architecture
Both promise to untangle your sprawling data landscape, but they take fundamentally different bets. We break down the trade-offs with real-world examples so you can make the call that fits your organisation.
November 28, 2025
ReadImplementing Zero-Trust Security in a Modern Lakehouse
Perimeter-based security was never designed for open data lakehouses accessed by hundreds of microservices. A zero-trust approach — attribute-based, continuous, and policy-as-code — changes the game.
October 14, 2025
ReadUsing LLMs to Automate Data Cataloguing and Lineage Tracking
Manual data cataloguing does not scale. We explore how large language models can automatically infer schema semantics, tag sensitive columns, and generate lineage graphs — cutting catalogue maintenance by 80%.
September 3, 2025
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