Azure Stream Analytics: Real-Time Solutions for Data Streams

What is Azure Stream Analytics?

Azure Stream Analytics architecture is a complex and real-time event processing engine crafted to simultaneously process and analyze large volumes of rapidly streaming data from different sources.

Relationships and patterns are identified, and information is gained from input sources such as sensors, devices, and applications. These patterns trigger actions and promote workflows, such as feeding information to any reporting tool, creating alerts and storing the transformed data for further use.

It is a completely managed engine created by Microsoft for real-time analytics. It provides the feature to perform real-time analytics on different data streams from multiple sources, such as web data, sensors, social media, sources, and other applications. So, whether you are into IoT applications, financial data analysis, social media monitoring, fraud detection or real-time analytics for operational efficiency, this application is a must-have!

When to Use Azure Stream Analytics?

When you have incoming live streaming data that you’re willing to report on with Power BI, store or receive insights by transforming it, then this platform offers the best services for you. It is a perfect solution if you want a completely managed service without worrying about any infrastructure set-up hassle. Some common Azure Stream Analytics use cases are as follows:

• Storing streaming data to make it accessible to other cloud services for later reporting, analysis, or logging.
• Real-time dashboarding for monitoring purposes with Power BI
• Analyzing and transforming data in real-time
• Triggering workflows on specific conditions
• Forwarding alerts
• Making real-time decisions
• Machine learning

Azure Stream Analytics is used if the input data is in JSON, AVRO, or CSV format, and the application logic is programmed in any query language like SQL.

Key Benefits of Azure Stream Analytics

It is well-known for its high programmer productivity, ease of use, low total cost of ownership and full management. Azure Stream Analytics offers the following other benefits:

Programmer Productivity:

Azure Stream Analytics utilizes SQL query language with powerful temporal constraints, making it simple for developers to analyze data in motion. It even supports multiple functions for data aggregation, manipulation, analytics, anomaly detection, and pattern matching.

Ease of Use:

Connecting different sinks and sources, making an end-to-end pipeline, requires only a few clicks. The service can be smoothly leveraged with other Azure services, such as SQL database, event hubs, and blob storage.

Fully managed:

Azure Stream Analytics is fully managed. This means you need not worry about provisioning, infrastructure or hardware, updating software, or OS. You can only focus on your business logic.

Low total cost of ownership:

A cloud service comes with cost optimization. Hence, you only need to pay for the streaming units you use. No cluster or commitment is required; you can scale the functions up or down based on your requirements.

Mission-critical ready:

Azure Stream Analytics is designed to run mission-critical workloads by promoting security, reliability, and compliance requirements. It even guarantees one event processing and one event delivery so that data is not lost. Considering security, all outgoing and incoming connotations are encrypted.

Limitation of Azure Stream Analytics

Some limitations that come along with the Azure Stream Analytics are as follows:

• Azure Stream Analytics supports only SQL.
• Your input data must be JSON, AVRO or CSV
• To add static data, you can only use blob storage
• It does not come with automatic scaling
• You only get to integrate with Azure services 
• You cannot get an advantage from supporting dynamic reference data join

Key features of Azure Stream Analytics

The key features of Azure Stream Analytics are:

• You get to combine data received from numerous streams
• You can utilize declarative SQL-based queries to transform data
• Stream the data with Power BI to real-time dashboards
• Integrate with Azure IoT Hub
• Pay only for the streaming units you use
• No need to handle any infrastructure
• Automatically avail benefit from writing multiple partitions in parallel
• Monitor your jobs visually
• Explore recovery capabilities
• Receive built-in geospatial functions
• Perform operations in temporal windows on data, such as sliding, tumbling, hopping and session windows

How Azure Stream Analytics Functions

The Azure Stream Analytics functions as follows:

• It includes an input, an output and a query.
• It collects data from Azure Blob Storage, Azure IoT Hub, or Azure Event Hubs.
• The query is based on SQL query language and can easily aggregate, filter, sort, and join streaming data.

Every job comes with several or one output from the data transformed, and you are controlled in response to the analyzed information. For instance,

• Forward data to service bus topics, Azure Functions, or other services to custom workflows, downstream, or more considerable communications.
• Data is stored in Azure storage services to help the machine learning models learn based on performing batch analytics or historical data.

Common Applications of Azure Stream Analytics

Some common applications of Azure Stream Analytics include:

• Storing data: It makes streaming data accessible for different cloud services to log, analyze and report.
• Real-time Dashboarding: Power monitoring data in real-time, you can use Power BI
• Triggering workflows: Access Azure functions based on specific conditions
• Data analysis: Analyze and transform data in real-time
• Forward alerts: Forward alerts based on data
• Make decisions: Making real-time decisions based on data
• Machine Learning: Utilize machine learning for predictive maintenance, risk analysis, trend prediction, and fraud detection.

How to Create a Stream Analytics Job in Azure Portal

Follow the steps mentioned below to Create a Stream Analytics Job in Azure Portal:

Step 1: First, you must sign in to the Azure portal

Step 2: Tap the ‘create a resource’ option to add any new resource.

Step 3: Type ‘stream analytics job, or stream’ in the search bar, and click on create.

Step 4: Now, go to the news stream analytics job screen, mention the details, such as job name, resource group, subscription, and location, and tap on ‘create.’

Step 5: After the deployment is completed, tap on the ‘go-to resource’ option

Step 6: Visit your phone analysis-as-a-job Stream Analytics job window, present in the left-hand side menu, located under job topology, and tap on ‘inputs to specify stream analytics job inputs.’

Step 7: Click ‘+ Add stream input’ in the input screen and tap ‘event hubs.’

Step 8: In the event hub screen, mention the required values and tap the ‘save’ button. After completion, you will see the Phone Stream Input job under the input window.

Step 9: Visit the phone analysis-as-a-job Stream Analytics job window, located in the left-hand menu under job topology. Tap on ‘outputs’ to specify the stream, click ‘+ Add,’ and click ‘blob storage.’

Step 10:

• Select or type the desired values in the blob storage window in the pane.
• Fill in the values for the drop-down: Min row = 10 and Max time = 5.
• Tap ‘save.’

You can now close the output screen and return to the resource group page.

Step 11: Click on the ‘edit query’ option in your phone analysis-as-a-job window in the Query screen in the middle of the window.

Step 12: Change and replace the default query with the new one and save.

Step 13: Click on the Start ‘option’ in your phone analysis-as-a-job window in the query screen in the middle of the window to start the Stream Analytics job.

Step 14: In the Start-Job dialogue box, tap on Now and then click ‘Start.’

Hence, these are the steps to create an Azure Stream Analytics job through the Azure portal.

Conclusion

Azure Stream Analytics is a fantastic tool to process and analyze real-time data. It offers ease of use, full management, programmer productivity, and a low total cost of ownership for the table. It is created to handle mission-critical workloads and provides multiple solution patterns for several cases. Whether incorporating insights into applications or powering real-time dashboards, this tool has covered you with its integration and other services.

Enroll for the Microsoft Certified Azure Data Engineer Associate: DP 203 course by Simplilearn, and learn to leverage Azure Stream Analytics along with Azure Databricks, Azure Synapse Analytics, Azure Data Factory, Azure Cosmos DB, and more!

FAQs

1. What is the difference between Azure Synapse Analytics and Azure Stream Analytics?

Azure Synapse analytics is a limitless service that combines big data analytics and enterprise data warehousing. It can query and ingest both unstructured and structured data.

While Azure Stream Analytics is a real-time data processing engine that ingests streaming real-time data for further reporting and analysis into Azure Synapse analytics, it combines data from multiple resources and streams data to real-time dashboards with Power BI.

2. What is the difference between Azure Event Hub and Stream Analytics?

Azure event hub can buffer and interest large amounts of real-time data and forward events like web clicks, sensor readings, and online log events.

Meanwhile, stream analytics analyzes and processes streaming data in real time. It provides management and monitoring features like logging, alerts, and real-time metrics.

3.  What types of data sources can Azure Stream Analytics connect to?

The types of data sources to which Azure Stream Analytics connects are:

• Azure event hubs
• Azure blob storage 
• Azure SQL Database 
• IoT Hub
• Azure Data Lake Storage Gen2
• Kafka