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Fabric Capacity Management: Right-Sizing Your Platform
Right-sizing Fabric capacity is a finance-and-engineering problem: choose a capacity SKU that matches peak loading patterns, instrument CU consumption, and…
Blog
Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 39 of 92
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Query Performance Optimization in Microsoft Fabric
Query performance is where UX and cost meet. My practical approach is to measure user-facing latency, inspect query plans, and prioritise optimisations that…
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V-Order Optimization: Deep Dive into Fabric's Secret Weapon
V-Order feels like a secret weapon because it's a write-time optimisation with outsized read-time benefits. In practice I enable V-Order on wide, heavily…
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Direct Lake Optimization: Maximizing Power BI Performance
Direct Lake changes the Power BI performance story — but it's not automatic. Over the past months I've seen Direct Lake deliver dramatic improvements when…
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Fabric Performance Tuning: Optimizing Your Data Platform
Performance tuning in Fabric blends write-time optimisations (v-ordering, partitioning) with query-time strategies (predicate pushdown, materialised views).…
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Fabric Components: When to Use What
Fabric's component set is broad; the hard part is picking the minimal surface that solves your business need. This guide distils when to use Lakehouse…
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Lakehouse vs Warehouse in Fabric: Making the Right Choice
Choosing Lakehouse vs Warehouse is an architectural trade-off: for open-format, large-scale analytics and ML, Lakehouse is the better fit; for classic T-SQL…
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Fabric Architecture Patterns: Designing for Scale
Designing for scale in Fabric is about decomposition and clear contracts between layers. The medallion architecture works well: small, composable…
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Microsoft Fabric Best Practices for Production
Shipping Fabric into production exposes common anti-patterns — unbounded capacity use, duplicated data copies, and insufficient observability. The best…
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Fabric Migration Stories: Real-World Experiences
There is no substitute for learning from real migrations: the patterns we reuse (lift-and-shift for bulk data, iterative modernization for dependent…
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Microsoft Fabric Adoption: Strategies for Success
A practical Fabric adoption roadmap balances fast wins with platform work — start with a high-impact pilot, instrument the value stream, and invest in a…
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AI Explainability: Making the Black Box Transparent
Explainability techniques are not one-size-fits-all: explanations that help a clinician are different from what helps a product manager. My rule is to pick…
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AI Transparency: Building Understandable AI Systems
Transparency is a practical tool for trust: concise user-facing explanations, developer-oriented model cards, and automated provenance logs make AI systems…
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Responsible AI Practices: Building Ethical AI Systems
Responsible AI isn't merely compliance box-ticking; it's product design that protects people and preserves trust. In practice I've found that pairing…
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EU AI Act: Preparing for the World's First AI Law
The EU AI Act signals a shift from voluntary standards to enforceable obligations; product teams need a simple classification step (is this high-risk?) and…
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GDPR and AI: Practical Compliance for ML Systems
GDPR's implications for AI are practical, not theoretical: logging decisions, maintaining provenance, and ensuring human review where necessary are the…
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AI Compliance Considerations: Navigating the Regulatory Landscape
Compliance moved from a legal exercise to a product requirement in 2023. My pragmatic approach: map data flows to jurisdictions, embed consent and retention…
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Operational Risk in AI Systems: Keeping the Lights On
Operational resilience is the unsung prerequisite for AI adoption. Practical SRE for AI means instrumenting model performance, bounding cost exposure, and…
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Model Risk Management for Large Language Models
LLMs are different beasts: unpredictable, context‑sensitive, and often opaque. Model risk management for LLMs needs to emphasise provenance, prompt…
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AI Risk Management: Identifying and Mitigating AI Risks
AI introduces risks that cut across data, models, operations and people. Over the past year I've helped teams map those risks to concrete controls — from…
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AI Governance Frameworks: Building Trust at Scale
Governance isn't a checkbox — it's what lets organisations scale AI safely. The frameworks I use combine risk tiers, model lifecycle controls, and pragmatic…
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Enterprise AI Lessons: What We Learned in 2023
Working with teams across finance, healthcare and retail this year, the same practical lessons come up: start with the business question, instrument for…
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AI Adoption Patterns: What Separates Success from Failure
After advising a range of enterprise AI programmes in 2023, the patterns are consistent: success comes from narrow business-first problem definition…
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GenAI Impact Assessment: Measuring Real Business Value
When teams move from pilots to production, the question I hear most is: 'Did this deliver measurable business value?' From dozens of GenAI assessments this…