1 min read
Real-Time Streaming Analytics with Azure Stream Analytics and Event Hubs
Enrich streaming data with reference data lookups for context-aware processing and alerting in real-time scenarios.
Streaming
29 articles
1 min read
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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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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Streaming LLM Responses: Patterns for Real-Time User Experience
Add a blinking cursor effect while streaming to indicate ongoing generation. Users perceive streaming responses as 3-5x faster than equivalent non-streaming…
3 min read
Microsoft Fabric Real-Time Intelligence: Patterns for Streaming Analytics
Microsoft Fabric's Real-Time Intelligence workload has matured significantly since GA. We've implemented several production streaming scenarios, and I want…
1 min read
Streaming Inference: Building Modern AI Systems
Understanding streaming inference is essential for production AI systems. Here's what you need to know.
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Streaming Inference Patterns: Processing Data as It Flows
Streaming inference brings AI insights to real-time data. Design for throughput, latency, and reliability from the start.
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Real-Time AI Patterns: Building Low-Latency Intelligent Systems
Real-time AI requires discipline around latency. Design for the worst case and optimize for the common case.
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Real-Time AI: Streaming Inference for Live Data Applications
Real-time AI requires careful architecture to balance latency, cost, and quality. Start with simple use cases and optimize based on actual performance data.
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Real-Time Analytics in Fabric: Architecture Patterns and Implementation
Real-time analytics in Fabric provides a powerful, integrated solution for streaming data. Start with simple patterns and evolve to more complex scenarios…
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Streaming Machine Learning: Training and Inference on Real-Time Data
Streaming ML enables intelligent real-time systems that continuously learn and adapt. Start with robust feature engineering and monitoring before enabling…
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Fabric Eventstream Enhancements: Real-Time Data at Scale
Eventstream enhancements make real-time analytics more powerful and accessible. Start with simple streaming scenarios and progressively add complexity.
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Fabric Eventstreams Patterns: From Ingestion to Analytics
Eventstreams lets teams deliver streaming analytics without managing complex infra. From deployments I've supported, these patterns make ingestion and…
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Fabric Real-Time Intelligence: Building Streaming Analytics Solutions
I've built several streaming pipelines on Fabric; the combination of Eventstreams, KQL, and Data Activator covers most real-time needs. This post shows…
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Eventstreams in Fabric: Streaming Data Ingestion
Tomorrow we'll explore Real Time Dashboards for visualizing streaming data. Eventstream Documentation Event Hub Integration Streaming Best Practices
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Fabric Real-Time Analytics: Stream Processing at Scale
'{' ' "SoftDeletePeriod": "30.00:00:00",' ' "Recoverability": "Enabled"' '}'
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Response Streaming: Real-Time LLM Output
Streaming improves the user experience significantly. Tomorrow, I will cover error handling for AI applications.
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Synapse Real-Time Analytics in Microsoft Fabric: Streaming at Scale
Real-Time Analytics in Fabric provides powerful streaming capabilities for time-sensitive workloads. Tomorrow, I will cover Power BI integration in Fabric.
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Streaming Responses with Azure OpenAI
Streaming transforms AI applications from feeling sluggish to feeling responsive. The implementation adds complexity, but the UX improvement is substantial.…
1 min read
Streaming Responses with Azure OpenAI: Real-Time AI Output
Without streaming, users wait for the entire response: 500 tokens at 50 tokens/second = 10 seconds of waiting Users see nothing, then everything at once…
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Working with IoT Hub Built-in Endpoints
The built-in endpoint provides a reliable, scalable way to consume IoT device telemetry for real-time processing.
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Real-Time Analytics on Azure: Building Live Dashboards
Azure's real time analytics stack enables instant insights from millions of events per second, powering live dashboards and alerts.
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Streaming Tables in Delta Live Tables
DLT streaming tables use append mode by default. For aggregations: DLT handles checkpointing automatically: Streaming tables in Delta Live Tables provide:…
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Using Azure Event Hubs with Kafka Protocol
Event Hubs with Kafka protocol offers: No Kafka cluster management Automatic scaling 99.99% SLA Native Azure integration Pay per throughput pricing Event…
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Real-Time Data Processing with Spark Structured Streaming
The appeal of Spark Structured Streaming is that you write it almost identically to a batch Spark job. Same DataFrame API, same transformations, same Spark…
2 min read
Building Data Pipelines with Delta Lake on Azure Databricks
Delta Lake has gone from "interesting open source project" to the default storage layer for data lakes inside about eighteen months. ACID, schema…
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Azure Media Services: Video Streaming Platform
Azure Media Services: broadcast-quality video for everyone.
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Databricks Structured Streaming: Real-Time Data Processing
Incrementally process files as they arrive. Structured Streaming makes real-time processing accessible with familiar DataFrame semantics.
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Azure Event Hubs with Kafka API
The Kafka API compatibility makes Event Hubs a drop-in replacement for many streaming scenarios.