<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>The Data Grill</title><description>Articles on data platforms, Microsoft Fabric, AI, and governance by Amrutha Satishkumar.</description><link>https://thedatagrill.com/</link><language>en-us</language><item><title>How to Optimize Microsoft Fabric Workloads and Prevent Capacity Throttling</title><link>https://thedatagrill.com/blog/how-to-optimize-microsoft-fabric-workloads-and-prevent-capacity-throttling/</link><guid isPermaLink="true">https://thedatagrill.com/blog/how-to-optimize-microsoft-fabric-workloads-and-prevent-capacity-throttling/</guid><description>A practical method to find the Fabric workload consuming your capacity, reduce its CU footprint, and decide when optimization, isolation, or scaling is the right fix.</description><pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate><category>Microsoft Fabric</category><category>Capacity</category><category>Performance Optimization</category><category>Throttling</category><category>Cost Optimization</category></item><item><title>How to Estimate Microsoft Fabric Capacity for Data Agent Usage</title><link>https://thedatagrill.com/blog/how-to-estimate-microsoft-fabric-capacity-for-data-agent-usage/</link><guid isPermaLink="true">https://thedatagrill.com/blog/how-to-estimate-microsoft-fabric-capacity-for-data-agent-usage/</guid><description>A practical way to estimate Fabric Data Agent consumption from request volume, token usage, and generated query cost, and why this is different from sizing pipelines, Spark, and warehouses.</description><pubDate>Tue, 08 Sep 2026 00:00:00 GMT</pubDate><category>Microsoft Fabric</category><category>Data Agents</category><category>Capacity</category><category>Cost Estimation</category><category>AI</category></item><item><title>Why AI Solutions Work in Development but Fail in Production</title><link>https://thedatagrill.com/blog/why-ai-solutions-work-in-development-but-fail-in-production/</link><guid isPermaLink="true">https://thedatagrill.com/blog/why-ai-solutions-work-in-development-but-fail-in-production/</guid><description>An AI solution that works in development is not automatically ready for production. Here are the seven gaps enterprises must close across ownership, evaluation, security, integration, adoption, operations, and cost.</description><pubDate>Tue, 18 Aug 2026 00:00:00 GMT</pubDate><category>AI</category><category>Enterprise</category><category>Governance</category><category>Agents</category><category>Data</category></item><item><title>Automating Microsoft Fabric Capacity Scaling: Four Ways to Do It</title><link>https://thedatagrill.com/blog/automating-microsoft-fabric-capacity-scaling/</link><guid isPermaLink="true">https://thedatagrill.com/blog/automating-microsoft-fabric-capacity-scaling/</guid><description>A hands-on guide to automating Microsoft Fabric capacity scaling: Logic Apps, Automation runbooks, Azure Functions, and Fabric Activator.</description><pubDate>Wed, 29 Jul 2026 00:00:00 GMT</pubDate><category>Fabric</category><category>Capacity</category><category>Cost Optimization</category><category>Automation</category><category>Activator</category></item><item><title>Data Governance in Microsoft Fabric: A Practical Framework</title><link>https://thedatagrill.com/blog/data-governance-in-microsoft-fabric/</link><guid isPermaLink="true">https://thedatagrill.com/blog/data-governance-in-microsoft-fabric/</guid><description>Govern data in Microsoft Fabric with domains, the OneLake catalog, sensitivity labels, lineage, and Purview, in a simple framework you can run.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>Data</category><category>Governance</category><category>Fabric</category><category>OneLake</category><category>Purview</category></item><item><title>Ontologies vs Semantic Models: What They Mean for Data Agents and Power BI</title><link>https://thedatagrill.com/blog/ontologies-vs-semantic-models-what-they-mean-for-data-agents-and-power-bi/</link><guid isPermaLink="true">https://thedatagrill.com/blog/ontologies-vs-semantic-models-what-they-mean-for-data-agents-and-power-bi/</guid><description>An ontology and a Power BI semantic model are not the same thing. Here is what each does, when to use which, and why the pairing makes data agents trustworthy.</description><pubDate>Thu, 09 Jul 2026 00:00:00 GMT</pubDate><category>Data &amp; AI</category><category>Fabric</category><category>PowerBI</category><category>Agents</category></item><item><title>From P to F: Migrating Power BI Premium to Fabric Capacity</title><link>https://thedatagrill.com/blog/from-p-to-f-migrating-power-bi-premium-to-fabric-capacity/</link><guid isPermaLink="true">https://thedatagrill.com/blog/from-p-to-f-migrating-power-bi-premium-to-fabric-capacity/</guid><description>Power BI Premium P SKUs are retiring and Fabric F SKUs are taking over. Here is what is changing, the timeline, the SKU map, and how to migrate cleanly.</description><pubDate>Mon, 06 Jul 2026 00:00:00 GMT</pubDate><category>Data</category><category>PowerBI</category><category>Fabric</category><category>Licensing</category></item><item><title>Microsoft IQ: The Intelligence Layer Behind Enterprise Agents</title><link>https://thedatagrill.com/blog/microsoft-iq-the-intelligence-layer-behind-enterprise-agents/</link><guid isPermaLink="true">https://thedatagrill.com/blog/microsoft-iq-the-intelligence-layer-behind-enterprise-agents/</guid><description>Microsoft IQ turns your company&apos;s scattered knowledge into agent-ready intelligence. Here is how Work IQ, Fabric IQ, Foundry IQ, and Web IQ fit together.</description><pubDate>Fri, 26 Jun 2026 00:00:00 GMT</pubDate><category>AI</category><category>Agents</category><category>Microsoft</category><category>Fabric</category><category>Foundry</category></item><item><title>Data Governance with Microsoft Purview</title><link>https://thedatagrill.com/blog/data-governance-with-microsoft-purview/</link><guid isPermaLink="true">https://thedatagrill.com/blog/data-governance-with-microsoft-purview/</guid><description>How Microsoft Purview&apos;s Unified Catalog turns a sprawling data estate into governed, discoverable data products, with governance domains, data quality, and self-service access.</description><pubDate>Fri, 15 May 2026 00:00:00 GMT</pubDate><category>Data</category><category>Governance</category><category>Purview</category></item><item><title>Agent-Ready Data: Why Dashboard-Ready Data Is No Longer Enough</title><link>https://thedatagrill.com/blog/agent-ready-data-why-dashboard-ready-data-is-no-longer-enough/</link><guid isPermaLink="true">https://thedatagrill.com/blog/agent-ready-data-why-dashboard-ready-data-is-no-longer-enough/</guid><description>AI agents raise the bar on data readiness. The five layers of the agent-ready data stack that take data from dashboard-ready to trustworthy enough for AI to act on.</description><pubDate>Tue, 05 May 2026 00:00:00 GMT</pubDate><category>AI</category><category>Data</category><category>Agents</category><category>PowerBI</category><category>Governance</category></item><item><title>Real-Time Event Streaming in Microsoft Fabric: How It Works, What You Need, and How to Set It Up</title><link>https://thedatagrill.com/blog/real-time-event-streaming-in-microsoft-fabric-how-it-works-what-you-need-and-how-to-set-it-up/</link><guid isPermaLink="true">https://thedatagrill.com/blog/real-time-event-streaming-in-microsoft-fabric-how-it-works-what-you-need-and-how-to-set-it-up/</guid><description>Learn how real-time event streaming works in Microsoft Fabric, including Event Streams, Eventhouse, KQL, and Power BI, with a step-by-step setup guide for real-time analytics.</description><pubDate>Wed, 18 Mar 2026 00:00:00 GMT</pubDate><category>Data &amp; AI</category><category>Fabric</category><category>Real-Time</category></item><item><title>Fabric IQ, Fabric Data Agents, Azure AI Foundry, and MCP: How They Work Together</title><link>https://thedatagrill.com/blog/fabric-iq-fabric-data-agents-azure-ai-foundry-and-mcp-how-they-work-together/</link><guid isPermaLink="true">https://thedatagrill.com/blog/fabric-iq-fabric-data-agents-azure-ai-foundry-and-mcp-how-they-work-together/</guid><description>How Fabric IQ, Fabric Data Agents, Azure AI Foundry, and MCP fit together in enterprise AI architectures, and when the Model Context Protocol is actually required.</description><pubDate>Tue, 24 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Data</category><category>Agents</category><category>Fabric</category><category>Governance</category></item><item><title>Understanding MCP: What the Model Context Protocol Actually Does</title><link>https://thedatagrill.com/blog/understanding-mcp-what-the-model-context-protocol-actually-does/</link><guid isPermaLink="true">https://thedatagrill.com/blog/understanding-mcp-what-the-model-context-protocol-actually-does/</guid><description>A clear, practical guide to understanding the Model Context Protocol (MCP), how it works in AI agents, and when modern data teams actually need it.</description><pubDate>Mon, 23 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Agents</category><category>Data</category></item><item><title>Attention Mechanism: The Secret Behind How Transformers Think</title><link>https://thedatagrill.com/blog/attention-mechanism-the-secret-behind-how-transformers-think/</link><guid isPermaLink="true">https://thedatagrill.com/blog/attention-mechanism-the-secret-behind-how-transformers-think/</guid><description>How the attention mechanism lets a Transformer weigh every word against every other word to build context, explained with a simple queries, keys, and values analogy.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Transformers</category></item><item><title>Feed-Forward Networks: How Transformers Refine What They Learn</title><link>https://thedatagrill.com/blog/feed-forward-networks-how-transformers-refine-what-they-learn/</link><guid isPermaLink="true">https://thedatagrill.com/blog/feed-forward-networks-how-transformers-refine-what-they-learn/</guid><description>The feed-forward network is the quiet workhorse inside every Transformer layer. Here is how it expands, adds nonlinearity, and refines what attention learns.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Transformers</category></item><item><title>Fabric Data Agents and Claude</title><link>https://thedatagrill.com/blog/fabric-data-agents-and-claude/</link><guid isPermaLink="true">https://thedatagrill.com/blog/fabric-data-agents-and-claude/</guid><description>How Fabric Data Agents let a model like Claude answer questions over enterprise data in natural language, while the semantic model keeps every answer governed and consistent.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>Data</category><category>AI</category><category>Fabric</category><category>Agents</category></item><item><title>Microsoft Purview: Classifying and Protecting Data at Scale</title><link>https://thedatagrill.com/blog/microsoft-purview-classifying-and-protecting-data-at-scale/</link><guid isPermaLink="true">https://thedatagrill.com/blog/microsoft-purview-classifying-and-protecting-data-at-scale/</guid><description>How Microsoft Purview classifies and protects sensitive data at scale using sensitive information types, sensitivity labels, and automated policy across your estate.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>Data</category><category>AI</category><category>Security</category><category>Governance</category></item><item><title>Multi-Head Attention: How Transformers Think in Parallel</title><link>https://thedatagrill.com/blog/multi-head-attention-how-transformers-think-in-parallel/</link><guid isPermaLink="true">https://thedatagrill.com/blog/multi-head-attention-how-transformers-think-in-parallel/</guid><description>Why Transformers use multiple attention heads instead of one, and how parallel heads capture different relationships and combine into a richer understanding of text.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Transformers</category></item><item><title>Positional Encoding: Teaching Transformers the Concept of Order</title><link>https://thedatagrill.com/blog/positional-encoding-teaching-transformers-the-concept-of-order/</link><guid isPermaLink="true">https://thedatagrill.com/blog/positional-encoding-teaching-transformers-the-concept-of-order/</guid><description>Transformers read every word at once, so how do they track word order? A clear look at how sine and cosine positional encoding teaches sequence to a Transformer.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Transformers</category></item><item><title>Protecting Sensitive Data in a Digital (and AI-Driven) World</title><link>https://thedatagrill.com/blog/protecting-sensitive-data-in-a-digital-and-ai-driven-world/</link><guid isPermaLink="true">https://thedatagrill.com/blog/protecting-sensitive-data-in-a-digital-and-ai-driven-world/</guid><description>Modern data security is shifting from protecting infrastructure to protecting the data itself, wherever it lives and moves. A practical look at why that matters.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>Data</category><category>Security</category><category>Governance</category><category>AI</category></item><item><title>The Foundation Beneath Every Power BI Report</title><link>https://thedatagrill.com/blog/the-foundation-beneath-every-power-bi-report/</link><guid isPermaLink="true">https://thedatagrill.com/blog/the-foundation-beneath-every-power-bi-report/</guid><description>How the Power BI semantic model, built on a clean medallion architecture, turns raw data into trusted, consistent metrics, and why it matters even more with AI.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>Data</category><category>PowerBI</category><category>Fabric</category></item><item><title>Understanding Transformers: Encoder, Decoder, and Everything In Between</title><link>https://thedatagrill.com/blog/understanding-transformers-encoder-decoder-and-everything-in-between/</link><guid isPermaLink="true">https://thedatagrill.com/blog/understanding-transformers-encoder-decoder-and-everything-in-between/</guid><description>Encoder-only, decoder-only, and encoder-decoder Transformers explained, and how models like BERT, GPT, and T5 map to each, with plain-language intuition.</description><pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate><category>AI</category><category>Transformers</category></item></channel></rss>