Implementing Azure Stream Analytics for data streaming

Posted on by Panther

Introduction:

Azure Stream Analytics is a cloud-based real-time analytics service provided by Microsoft that helps to process and analyze high-speed data streams from multiple sources. It integrates with various Azure services such as Azure Event Hubs, Azure IoT Hub, and Azure Blob Storage, and allows processing of data from cloud-based or on-premises data sources in near real-time. The service provides a SQL-like language called Query Language for Stream Analytics (QLSA) to process and analyze data.

In this tutorial, we will learn how to implement Azure Stream Analytics for data streaming from various sources. We will go through the following steps:

  1. Create an Azure Stream Analytics job.
  2. Connect to data sources.
  3. Define a query to process data.
  4. Configure the output for processed data.
  5. Monitor the job for errors and warnings.

Prerequisites:

Before starting with this tutorial, make sure you have the following prerequisites:

  • An Azure subscription.
  • Basic knowledge of Azure services such as Event Hubs and IoT Hub.
  • Basic knowledge of SQL.

Step 1: Create an Azure Stream Analytics Job:

The first step in implementing Azure Stream Analytics is to create a Stream Analytics job. Follow the below steps to do so.

  1. Go to the Azure portal and select Create a resource.

  2. Search for Stream Analytics Job under Analytics and select Create on the panel that appears on the right.

  3. Fill out the fields as per your requirements, such as Job Name, Subscription, Resource Group, Region, Output blob storage, etc.

  4. Select Review + Create and then Create to create a Stream Analytics job.

Step 2: Connect to Data Sources:

After creating an Azure Stream Analytics Job, we need to connect it to data sources from which we want to stream data. In this example, we will use an Event Hub as our data source.

Follow the below steps to connect to an Event Hub:

  1. In the Azure portal, go to your Stream Analytics Job overview and select Inputs.

  2. Select +Add to add a new input.

  3. Select the relevant input type (in this case, Event Hub) and fill in the required information as per your Event Hub details.

  4. Provide the Event Hubโ€™s namespace, Event Hub name, consumer group, and connection string.

  5. After filling in the required fields, select Create to create the input.

Step 3: Define a Query to Process Data:

After connecting to the data source, we can write a query to process data. Azure Stream Analytics provides a SQL-like language called Query Language for Stream Analytics (QLSA) to write queries.

For this example, we will use a simple query to count the number of messages in the Event Hub stream. Follow the below steps to write a query:

  1. In the Azure portal, go to your Stream Analytics Job overview and select Query.

  2. Write the following query in the Query Editor:

SELECT COUNT(*) AS MessageCount
FROM InputEventType

Here, InputEventType should be replaced with the name of your Event Hub input stream.

  1. Select Save to save the query.

Step 4: Configure the Output for Processed Data:

After processing the data, we need to configure the output where the processed data will be stored. In this example, we will use an Azure Blob Storage account to store the processed data.

Follow the below steps to configure output to blob storage:

  1. In the Azure portal, go to your Stream Analytics Job overview and select Outputs.

  2. Select +Add to add a new output.

  3. Select the relevant output type (in this case, Blob storage) and fill in the required details as per your Blob storage requirements.

  4. Provide the Blob Storage account details, such as Storage account name, container name, and access key.

  5. Select Enable Event time if you want to enable an event timestamp.

  6. After filling in the required fields, select Create to create the output.

  7. Now, in the output settings page, select the setup Guide for details on how to set up Blob storage as an output from an Azure Stream Analytics job

Step 5: Monitor the Job for Errors and Warnings:

After configuring the input, query, and output, we can use the Monitoring dashboard to monitor the job for errors and warnings.

Follow the below steps to monitor the job:

  1. In the Azure portal, go to your Stream Analytics Job overview and select Monitor.

  2. Select Logs under Monitoring hub to view detailed job logs.

  3. If there are any errors, they will be listed here.

  4. Go through the error messages and resolve the issue accordingly.

Conclusion:

In this tutorial, we learned how to implement Azure Stream Analytics for data streaming. We understood the steps involved in creating an Azure Stream Analytics job, connecting to data sources, defining a query to process data, configuring the output for processed data, and monitoring the job for errors and warnings.

Azure Stream Analytics is an important tool for processing and analyzing high-speed data streams. With the help of this service, we can process and analyze data from cloud-based or on-premises data sources in near-real-time, allowing us to make informed decisions quickly.

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