Azure Stream Analytics - Analyzing Real Time Data

In this blog, we will explore what stream analytics is in Azure, how it works, its key capabilities, and its features. We will also go through the steps to create a stream analytics job using the Microsoft Azure portal.

Table of Contents

• What is Azure Stream Analytics?
• How Does Azure Stream Analytics Work?
• Key Capabilities and Benefits of Azure Stream Analytics
• How to Create Stream Analytics Job Using Microsoft Azure Portal
• AWS Kinesis Vs. Azure Stream Analytics
• Conclusion
• FAQs

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What is Azure Stream Analytics?

Azure Stream Analytics is a comprehensive real-time analytics service that is fully managed, aiming to assist in the analysis and processing of rapidly flowing streams of data. Its purpose is to provide insights, generate reports, and initiate alerts and actions based on the streaming data. Once we’ve sifted through and analyzed all pieces of data, they provide us with valuable insights into how consumers behave. This valuable process is what we call streaming analytics or event stream processing.

Thanks to streaming analytics, companies nowadays have access to a wide range of analytical tools like spotting connections, summarizing data, sorting out important bits, and taking random samples. These tools help them understand their business operations better. There are a lot of Azure Stream Analytics use cases, some of which we will see. When it comes to social media, streaming analytics can also measure public sentiment towards a brand or product. This helps companies react accordingly when situations demand it.

One advantage of streaming data analytics is that it operates quickly, processing data in mere milliseconds without the need for complex mathematics. However, it does need systems that can grow as needed, handle errors gracefully, and be trustworthy.

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How Does Azure Stream Analytics Work?

This diagram provides a visual representation of the Azure Stream Analytics architecture, demonstrating the journey of data from its intake, through the analysis phase, and finally to its destination for presentation or further actions. It shows a clear picture of how data flows and gets processed in this system.

Azure Stream Analytics pipeline begins by taking in streaming data from various sources. This data can be collected in Azure using tools like Azure Event Hubs or IoT Hubs. Instead, it can be fetched from a data repository such as Azure Blob Storage.

To analyze this data stream, we set up a Stream Analytics job that specifies where the data is coming from. Additionally, the job defines how to search for specific data, patterns, or connections within the stream. Stream Analytics provides a language similar to SQL that allows us to filter, sort, group, and link streaming data over a defined time span.

We can also add web services to Azure Stream Analytics. In Azure Stream Analytics, web services are added as custom events. Custom events allow you to track any user interaction or event on your website or web application that is not automatically tracked by Azure Web Analytics. This includes events that occur on your web service, such as API calls, logins, or purchases.

Lastly, the job determines where the transformed data should be sent. We have control over what actions we should take based on our analysis. For example, we might use the analysis for the following:

• Send instructions to modify device settings
• Forward data to a monitored queue for further processing based on our discoveries
• Share data with a Power BI dashboard
• Store data in repositories like Data Lake Store, Azure Blob Storage, or Azure SQL Database

While the job is running, we can fine-tune the number of events processed per second. Also, there’s an option to generate diagnostic logs to help with problem-solving. This flexible system enables us to make the most use of our streaming data for various applications and insights.

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Key Capabilities and Benefits of Azure Stream Analytics

Stream Analytics is built to be user-friendly, adaptable, and capable of handling jobs of any size.

• Connection Benefits: It seamlessly connects with Azure IoT Hub and Azure Event Hubs for bringing in streaming data. It also integrates with Azure Blob storage to collect historical data. Stream Analytics allows us to combine data from various origins, including reference data for performing lookup operations, resembling the functionality found in databases. Some of the key capabilities and benefits of Azure Stream Analysis are mentioned below.

When it comes to sending results from our Stream Analytics job, we have many options, like:

• Azure SQL Database
• Azure Blob
• Azure Cosmos DB
• Azure Data Lake Stores

Furthermore, we can perform batch analytics using Azure HDInsight or send the output to other services, like:

• Event Hubs
• Queues
• Azure Service Bus
• Power BI for visualization

• Easy to Use: Stream Analytics simplifies the process by using a straightforward, declarative query language for defining transformations. This query language allows us to create advanced analyses without needing programming skills. We can filter, sort, calculate, aggregate, and even perform geographical operations on streaming data. Modifying queries within the portal is a straightforward process, made more convenient by helpful tools such as IntelliSense and the ability to check for syntax errors. These features enhance the ease of working with queries and ensure they are error-free. We can also test queries using sample data from the live stream.
• Extendable: To extend the capabilities of the query language, we can define and use additional functions. We have the option to leverage Azure Machine Learning solutions through function calls in the Azure Machine Learning service. Furthermore, we can incorporate JavaScript user-defined functions (UDFs) to handle complex calculations within our Stream Analytics query.
• Security: Azure ensures strong security measures for both stored and transmitted data. It employs a robust security system with TLS 1.2 support and utilizes Virtual Networks (VNETs) to safeguard data and user information, ensuring their protection.
• Scalability: When it comes to scalability, Stream Analytics is quite robust. It can process up to 1 GB of incoming data per second. Thanks to its integration with Azure Stream Analytics IoT Hub and Azure Event Hubs, it can handle millions of events per second from various sources like connected devices, log files, and clickstreams. The partition feature of Event Hubs allows us to scale computations efficiently by breaking them down into logical steps, each of which can be further partitioned.
• Performance: Azure Stream Analytics has the capacity to effectively handle a huge amount of data every second, resulting in swift response times. Furthermore, it possesses excellent scalability, allowing us to easily adjust its capacity up or down as needed to meet specific demands.
• Cost-effectiveness: In terms of cost-effectiveness, Stream Analytics is designed to be budget-friendly. We pay based on streaming-unit usage and the volume of data processed. This cost structure is based on the number of events processed and the computing power allocated within the job cluster.
• Reliability: Reliability is a top priority for Stream Analytics. It’s a managed service that minimizes data loss and ensures business continuity. In case of failure, it offers built-in recovery mechanisms. With its ability to maintain internal state, it offers consistent results, enabling us to review past events and reapply processing when needed, making it valuable for tasks like root-cause analysis and “what-if” scenarios.

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How to Create Stream Analytics Jobs Using Microsoft Azure Portal

Creating the Job

Step 1: 

• Select Create a resource in the upper left-hand corner of the Azure portal.
• Select Analytics > Search Stream Analytics from the results list.

Step 2: 

• Select Stream Analytics job.
• Click on Create.

Step 3: On the New Stream Analytics job page, follow the below-mentioned steps:

• For Subscription, select your Azure subscription. In this case, it displays a Free Trial category.
• For the Resource group, select any resource group that you may have created before or create a new resource group.
• For Name, enter a name for the job.

(The name of the Stream Analytics job can consist of alphanumeric characters, hyphens, and underscores exclusively, with a required length between 3 and 63 characters.)

• For the Hosting environment, confirm that Cloud is selected.

• For Streaming units, select 1. 
• Select Review + Create at the bottom of the page.

• Once the deployment is complete, click on Go to Resource.

Configuring the Job

Upon finishing the above step, we need to provide the inputs for the above job.

Given that we haven’t yet readied the job’s inputs, it’s now the appropriate moment to prepare them.

Creating the IoT Hub:

• Click on Create a resource.
• Navigate to the Create a resource page and choose Internet of Things, followed by IoT Hub, from the options.

On the IoT Hub page, go through these steps:

• In Subscription, click your Azure subscription.
• In the Resource group, select an existing resource group or establish a new resource group.
• In the IoT hub name, add a name of your choice for your IoT hub.
• In Region, click on the region that’s nearest to you.
• In Tier, click Free, if it remains accessible within your subscription. 
• In the Daily message limit, maintain the default value.
• Select Review + Create. Review your information and select Create.

• After the resource (IoT hub) has been successfully created, select Go to the resource.
• On the IoT Hub page, click Devices on the left menu, and then select + Add device.
• Enter a Device ID and select Save.

• Once you refresh the page, you should see the device within the list of IoT devices. 

• Opt your device from the list.

• Navigate to the device page and click the copy button adjacent to the “Connection string – primary key.” Paste this information into a notepad for future use.

Create Blob Storage:

• Select Create a Resource > Storage > Storage account.
• In the Create Storage account, input a storage account name, select its location, and specify the resource group. Opt for the identical location and resource group as the one used for the IoT Hub you previously established. 

• On the Review page, review your settings, and click on Create to create the account.
• After the resource is created, click on Go to Resource to navigate to the Storage account page.
• On the Storage account page, click on Containers on the left menu, and then select + Container. On the New container page, type a name for your container, such as intellipaatspotcontainer, and select Create.

Now, we have finished creating all the pre-requisite requirements to provide inputs to the Stream Analytics Job that we created.

Let’s finally configure the job. Follow the steps mentioned below:

• Within the Stream Analytics job page, navigate to the Job topology section on the left menu and click on Input. 
• Then, on the Inputs page, choose Add stream input followed by IoT Hub.

On the IoT Hub page, follow the following steps:

• For Input alias, type the name as IoTHubInput.
• For Subscription, click on the subscription that consists of the IoT hub you created earlier. 
• For IoT Hub, click on your IoT hub.
• Click on Save to save all the input settings for the Stream Analytics job.

Configure Job Output

• Now, click on Outputs under Job Topology on the menu.
• On the Outputs page, Add > Blob storage/ADLS Gen2.        

On this New output page of Blob storage/ADLS Gen2, follow the given steps:

• To see the Output alias, enter BlobOutput.
• To see for Subscription, click on the subscription that has the Azure storage account you created earlier.
• For a Storage account, click on your Storage account.
• For Container, click on your blob container if it is not already selected.
• For Authentication mode, click on Connection string.
• Click on Save at the bottom of the page to save all the output settings.

Define the transformation query:

• Now, click on Query under Job topology on the left menu.
• Enter the given query into the query window.

• Click on Save query on the toolbar.

Run the IoT simulator: 

• Access https://azure-samples.github.io/raspberry-pi-web-simulator/. 
• Click on the Raspberry Pi Azure IoT Online Simulator to open it.

• Retype the placeholder in Line 15 with the Azure IoT Hub device connection string that you saved in the previous section.
• Now, click on  Run. The output should be shown which will be having the sensor data and messages that are being sent to your IoT Hub.
• Start the Stream Analytics job and check the output.
• On the Start job page, confirm that Now

• After a few minutes, within the portal, navigate to the storage account and the container that you’ve previously configured as the output destination for the job. At this point, you should be able to locate the output file within the designated container.
• Once the job has started, it will continue to run and process incoming data as it becomes available.

• Click on the file, and then on the Blob page, click on Edit to view the contents of the file.

One can verify the streaming data by checking the storage container within the resource group created earlier.

These steps help us to create and configure an Azure Stream Analytics job, set up input and output, and define a query for processing streaming data. 

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AWS Kinesis Vs. Azure Stream Analytics

Similar to Microsoft Azure Stream Analytics, AWS also provides their Kinesis for the same purpose. Amazon Kinesis is a real-time data streaming and processing service by Amazon Web Services (AWS). It enables the collection, processing, and analysis of large volumes of data from various sources like IoT devices, logs, and clickstreams.  Let’s have a look at their comparison between Azure Stream Analytics and AWS Kinesis:

Read our blog about which one to choose: AWS vs. Microsoft Azure.

Conclusion

In summary, Azure Stream Analytics transformed data handling. It provides real-time access to critical insights from various sources, simplifies complex analyses, and offers scalability and cost-effectiveness. With robust security and reliability, it empowers businesses to make prompt, informed decisions in today’s data-driven landscape, giving them a competitive edge in the digital age.

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