Azure Stream Analytics Tutorial

Introduction

Azure Stream Analytics is a real-time analytics and complex event-processing engine that is designed to analyze and process high volumes of fast streaming data from multiple sources simultaneously. Patterns and relationships can be identified in information extracted from various input sources such as devices, sensors, infrastructure, applications, and data.

Setting Up Azure Stream Analytics

To get started with Azure Stream Analytics, you need an Azure account. If you don't have one, you can create a free account on the Azure website.

Once you have an account, follow these steps:

1. Log into the Azure Portal.
2. Click on "Create a resource" in the upper left corner.
3. Search for "Stream Analytics job" and select it.
4. Click "Create" to start the creation of your Stream Analytics job.
5. Fill in the required fields such as job name, subscription, resource group, and location.
6. Click "Review + create" and then "Create" to deploy your Stream Analytics job.

Creating Input Sources

Azure Stream Analytics can ingest data from multiple sources including Azure Event Hubs, Azure IoT Hub, and Azure Blob Storage. Here, we will use Azure Event Hubs as an example.

Creating an Event Hub

1. In the Azure portal, click on "Create a resource".
2. Search for "Event Hubs" and select it.
3. Click "Create" and fill in the necessary information such as namespace name, location, and pricing tier.
4. Click "Review + create" and then "Create".
5. After the namespace is created, go to the Event Hubs namespace and create an Event Hub within it.

Creating a Stream Analytics Job

Once the input source is set up, you can create a Stream Analytics job to process the streaming data.

1. In the Azure portal, navigate to your Stream Analytics job.
2. Click on "Inputs" in the left-hand menu.
3. Click "Add stream input" and select "Event Hub".
4. Fill in the required fields such as input alias, subscription, Event Hub namespace, Event Hub name, and consumer group.
5. Click "Save" to add the input.

Defining the Query

The query in Azure Stream Analytics is defined using a SQL-like query language. Here is an example of a simple query that selects data from the input and writes it to the output.

SELECT * INTO Output FROM Input

You can define more complex queries to perform aggregations, filtering, and transformations on the streaming data.

Outputting Results

Azure Stream Analytics supports multiple output targets including Azure Blob Storage, Azure SQL Database, and Power BI. Here, we will use Azure Blob Storage as an example.

Creating an Output

1. In the Azure portal, navigate to your Stream Analytics job.
2. Click on "Outputs" in the left-hand menu.
3. Click "Add" and select "Blob storage".
4. Fill in the required fields such as output alias, subscription, storage account, and container.
5. Click "Save" to add the output.

Running the Job

Once the input, query, and output are defined, you can start the Stream Analytics job to begin processing the streaming data.

1. In the Azure portal, navigate to your Stream Analytics job.
2. Click on "Overview" in the left-hand menu.
3. Click "Start" and specify the job output start time.
4. Click "Start" to initiate the job.

Your Stream Analytics job will now start processing data from the input source, apply the query, and write the results to the output target.

Monitoring and Troubleshooting

Azure Stream Analytics provides tools to monitor and troubleshoot your jobs. You can view metrics, logs, and input/output events to ensure your job is running as expected.

Monitoring Metrics

To monitor your job's metrics:

1. In the Azure portal, navigate to your Stream Analytics job.
2. Click on "Monitoring" in the left-hand menu.
3. View the available metrics such as Input Events, Output Events, and Errors.

Conclusion

In this tutorial, we covered the basics of Azure Stream Analytics, from setting up the job to defining inputs, writing queries, and outputting results. Azure Stream Analytics is a powerful tool for real-time data processing and can be integrated with various Azure services to build comprehensive data solutions.