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Microsoft Fabric Real-Time Analytics: Streaming Data Pipelines
Eventhouse is the storage layer optimized for time-series and streaming data. It automatically indexes data for lightning-fast queries.
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Thoughts on data engineering, cloud architecture, and technology. 2205 articles and counting.
Showing 24 of 2205 posts — page 13 of 92
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Azure AI Document Intelligence: Extracting Structured Data from Invoices
First, provision an Azure AI Document Intelligence resource and configure your client. The prebuilt invoice model recognizes vendor details, line items…
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Enterprise AI Adoption: Building Your Center of Excellence
An AI CoE serves as the central hub for AI expertise, governance, and best practices. It prevents siloed implementations and ensures consistent quality…
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Building Multi-Agent Systems with Semantic Kernel
Define clear responsibilities for each agent to avoid confusion and improve reliability. Multi-agent architectures excel at complex workflows where…
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AI-Powered Data Exploration with Microsoft Fabric Copilot
Ask questions about your data in natural language and get executable code. Generate complex SQL from natural language descriptions.
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Semantic Kernel Filters: Intercepting and Modifying AI Operations
Semantic Kernel supports several filter types: function invocation filters, prompt render filters, and auto function invocation filters. Each intercepts…
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Embedding Models: Choosing Between OpenAI, Azure, and Open Source
The text-embedding-ada-002 and newer text-embedding-3 models provide high-quality embeddings with minimal setup.
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Building Data Pipelines with Microsoft Fabric Data Factory
Data Factory pipelines use a visual designer with activities that can be chained together.
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Structured Output with Azure OpenAI: JSON Mode and Response Formats
JSON mode guarantees the model outputs valid JSON, though you still need to specify the schema in your prompt.
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Medallion Architecture in Microsoft Fabric: Bronze, Silver, Gold Explained
Bronze tables store data exactly as received from source systems, preserving the original format for replayability and debugging.
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Function Calling Patterns in Azure OpenAI
Functions are defined as JSON schemas that describe parameters and their types. Well-designed function schemas and clear descriptions ensure the model calls…
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Data Quality for AI: Ensuring Clean Data for ML and LLM Applications
Quality encompasses completeness, accuracy, consistency, timeliness, and validity. Each dimension requires specific checks.
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Vector Database Selection: Comparing Azure AI Search, Pinecone, and Qdrant
Best for organizations already invested in Azure with hybrid search requirements. Pros: Hybrid search, semantic ranking, enterprise security, managed…
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Building TypeScript AI Applications with Vercel AI SDK
The SDK supports function calling with automatic TypeScript type inference, making it easy to extend your AI with custom capabilities.
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Implementing Guardrails for Production AI Systems
Filter and sanitize user inputs before they reach the LLM. Verify AI responses before returning them to users.
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Prompt Engineering Best Practices for Enterprise Applications
A well-structured prompt includes context, instructions, examples, and output format specifications.
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Real-Time Data Processing with Microsoft Fabric Eventstreams
Eventstreams support multiple source types including Azure Event Hubs, Azure IoT Hub, and custom applications. The visual designer makes it easy to…
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Building AI Agents with Semantic Kernel Planners
Planners analyze a user's goal and create an execution plan using available plugins. The Handlebars planner generates a template-based plan, while function…
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GPT-4o Vision: Building Image Analysis Applications
GPT-4o vision provides remarkable understanding of images, but always validate outputs for critical applications. Combine with traditional computer vision…
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Semantic Kernel Memory: Building Persistent AI Context
Use retrieved memories to enrich the system prompt or provide context for the AI. This enables personalized responses without requiring the user to repeat…
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Apache Iceberg on Microsoft Fabric: Open Table Format Integration
Iceberg provides ACID transactions, schema evolution, time travel, and partition evolution for data lakes. Its format-agnostic design works with Spark…
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LLMOps: Operationalizing Large Language Models in Production
LLMs present distinct operational challenges: non-deterministic outputs, prompt sensitivity, context window management, and the difficulty of defining…
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Implementing Hybrid Search in Azure AI Search
Keyword search excels at exact matches and rare terms. Vector search captures semantic similarity. Combining them leverages both strengths while mitigating…
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Microsoft Ignite 2025 Preview: AI and Data Platform Expectations
Ignite remains the premier event for understanding Microsoft's enterprise AI direction. Planning your learning path around expected announcements helps…