Insights & Articles
Deep dives, practical guides, and opinion pieces on Microsoft Fabric, Azure Data, Governance, and Enterprise AI.
Selected Publications & Thought Leadership
From Data Lakes to Delta Lakes (4-part series)
A practical series on evolving from raw data lakes to governed, high-trust Delta-powered lakehouses.
Apache Spark Series: Essential for Modern Data Engineers
Foundational concepts and migration patterns from Hadoop-era architectures to Spark-centric workloads.
Microsoft Fabric: Data Science & AI (End-to-End Series)
Hands-on journey from data ingestion to model training, registration, and batch scoring in Fabric.
Key Articles on Advanced Data Engineering
Featured Insights
From Data Lakes to Delta Lakes: A Practical Guide for Azure & Fabric
How to modernise traditional data lakes into governed, high-performance lakehouses using Delta technologies on Azure.
Building a Secure AI Policy Search on Azure OpenAI & AI Search
A step-by-step architecture for secure, VNet-integrated policy and knowledge search using Azure AI Search and Azure OpenAI.
Getting Started with Microsoft Fabric in Higher Education
Key concepts, patterns, and starting points for universities adopting Microsoft Fabric for analytics and reporting.
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All Articles
Business-Ready AI: The Missing Layer Between “Great Models” and Real Value
Most AI failures aren’t technical.They fail because the organisation never made AI a business system — with owners, ROI, adoption, trust, and risk controls. You
Bridging BI and AI: Power BI, Semantic Models, and LLMs
How to connect semantic models, metrics layers, and LLMs to deliver AI experiences that are grounded in governed, trusted data.
MLOps Patterns for Microsoft Fabric and Databricks
Practical MLOps patterns for teams running on Fabric and Databricks, including CI/CD, environments, and model lifecycle.
MLflow for AI Governance: Beyond Model Tracking
How to use MLflow as a backbone for AI governance—linking experiments, datasets, approvals, and deployment decisions.
Architecting Agentic AI Workflows with LangGraph and Azure
A reference architecture for building multi-agent systems in Azure using LangGraph, with a focus on reliability, guardrails, and observability.