Azure Stream Analytics documentation

Azure Stream Analytics is a fully managed, real-time analytics service designed to help you analyze and process fast moving streams of data that can be used to get insights, build reports or trigger alerts and actions. Learn how to use Azure Stream Analytics with our quickstarts, tutorials, and samples.

About Azure Stream Analytics

Overview

• What is Azure Stream Analytics?
• Azure Stream Analytics solution patterns

Quickstart

• Create a job with Azure portal

Tutorial

• Capture Event Hubs data in parquet format
• Write to a delta table in Data Lake Storage Gen2
• Build real time Power BI dashboards with no code editor
• Analyze fraudulent call data and visualize results

Concept

• Choose a streaming analytics technology
• Choose a development tool for your jobs

Connect a job to a data source

Overview

• Learn about input types

Concept

• Stream input data
• Join reference data for lookups

How-To Guide

• Use reference data from a SQL Database
• Troubleshoot input connections
• Process Apache Kafka for Event Hubs events
• Process data from Azure Event Hubs

Send data to an output sink

Overview

• Learn about output types

Concept

• Output to Azure Cosmos DB
• Output to Azure SQL Database
• Custom blob output partitioning
• Output error handling policies

How-To Guide

• Troubleshoot output connections

Monitor and troubleshoot your job

How-To Guide

• Monitor and manage jobs using Azure portal
• Monitor and manage jobs from Visual Studio
• Analyze job performance using job metrics and dimensions
• Debug queries locally using job diagram in Visual Studio Code
• Troubleshoot using resource logs
• Debug using the job diagram
• Troubleshoot data errors

Review temporal concepts

Concept

• Common query patterns
• Understand time handling in Stream Analytics
• Internal checkpoint and replay

Transform data with Stream Analytics queries

Concept

• Introduction to windowing functions
• Introduction to geospatial functions
• Parse JSON and Avro data

How-To Guide

• Troubleshoot query logic
• Write JavaScript user-defined aggregates
• Develop .NET user-defined functions for IoT Edge jobs

Tutorial

• Run a JavaScript user-defined function
• Run a C# user-defined function on a Stream Analytics Edge job

Analyze with machine learning

How-To Guide

• Use machine learning to detect anomalies
• Perform sentiment analysis with Machine Learning Studio (classic)
• Scale jobs with Machine Learning Studio (classic) functions

Create and manage jobs with developer tools

How-To Guide

• Explore jobs with Visual Studio Code
• Test queries with sample data in Visual Studio Code
• Install tools for Visual Studio
• Test queries with sample data in Visual Studio
• Test queries with live data in Visual Studio
• View jobs in Visual Studio
• Develop an IoT Edge job in Visual Studio

video

• Author, manage and test with Visual Studio Code

Try Stream Analytics tutorials and solutions

Tutorial

• Real-time fraud detection
• Trigger an Azure Function

How-To Guide

• Build solutions with geofencing and geospatial aggregation
• Run jobs on IoT Edge
• Build a sentiment analysis solution
• Build an IoT solution
• High-frequency trading simulation
• Alert based on adjustable rule thresholds

Deploy Stream Analytics jobs

How-To Guide

• Set up a CI/CD pipeline with Visual Studio Code
• Set up a CI/CD pipeline with Visual Studio
• Use REST APIs to set up CI/CD for IoT Edge jobs

Scale Stream Analytics jobs

How-To Guide

• Scale with streaming units
• Scale with query parallelization
• Scale jobs to increase throughput
• Optimize processing by repartitioning