Microsoft’s Azure Stream Analytics is a real-time streaming service. It consists of a sophisticated event processing engine that analyses and processes large amounts of real-time data such as stock trading, credit card fraud detection, Web clickstream analysis, and social media feed, among other things. You can run them in batch mode or in real-time mode to analyze data more quickly.

In this blog post, you will be learning about one of Azure’s most powerful tools known as Azure Stream Analytics. It will also tell you about the working and key features of the product and how to set up an analytics process quickly.

Introduction to Azure Stream Analytics

Stream Analytics

Azure Stream Analytics is a real-time streaming service provided by Microsoft. It consists of a complex event processing engine designed to analyze and process vast volumes of real-time data like stock trading, credit card fraud detection, Web clickstream analysis, social media feeds, etc. For quicker analysis of data, you can either execute them in batch mode or in a real-time way. For example, you can run real-time analysis on the credit card to detect the fraud immediately, to prevent the card’s misuse again in the future.

Azure Stream Analytics is a fully managed Platform-as-a-Service(PaaS) offering, which means you don’t need to handle any hardware and resources to perform analytics. It also uses Stream Analytics Query Language, a variant of T-SQL, and hence users from SQL backgrounds can quickly adapt and learn ASA.

Azure Stream Analytics (ASA) allows you to define patterns and relationships to trigger actions/workflows to perform analytics. The data can be used to send alerts, store information, or feed information to the reporting tools.

More information regarding Azure Stream Analytics can be found here.

Below are the key areas where you can use Azure Stream Analytics:

  • Real-time streaming from IoT devices, web streams.
  • Geospatial analytics for fleet and vehicle management.
  • Predictive analysis of high asset data.
  • Fraud and anomaly detection and many more.

Working of Azure Stream Analytics

Azure Stream Analytics’ working rule is straightforward – ingest data, analyze data and deliver it to the downstream application. It can ingest data from Apache Kafka, IoT devices, or Azure Blob storage. Azure Stream Analytics uses SQL queries to analyze the data by performing analytical functions like sort, filter, aggregate, joins, and many more. You can also use Java and C# to create user-defined functions if you need out-of-box capabilities. 

Below schematics shows the end-to-end process of data analytics in Azure Stream Analytics.

Stream Analytics - End to End Process.

Here is the high-level step by step approach of data analytics:

  • Various sources like IoT devices, log files, weblogs, web clickstream, weather data, Azure Blob, financial data like credit card transactions and many more can ingest data to Azure Streaming Platform.
  • Upon ingestion of data to the Azure platform, you can use a Stream Analytics query (similar to SQL query) to perform analytics on the data like sort, filter, aggregations, window functions, and many more.
  • Upon completing the analysis, downstream applications like a data warehouse, reporting tools can use the data for further analysis, dashboarding, or sending alerts and notifications.

More information regarding the working can be found here.

Simplify your Data Analysis with Hevo’s No-code Data Pipelines

Hevo Data, a No-code Data Pipeline helps to transfer data from multiple data sources(among 100+ sources) to a data warehouse/destination of your choice to visualize it in your desired BI tool. Hevo is fully-managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.

Get Started with Hevo for Free

Check out what makes Hevo amazing:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!

Key Features of Azure Stream Analytics 

The following are some of the key features of Azure Stream Analytics:

  1. Ease of Use
  2. Ease to Adopt
  3. Fully Managed
  4. Reliability
  5. Security
  6. Performance

1) Ease of Use 

Azure Stream Analytics is easy to use, and it has a very user-friendly UI that takes only a few clicks to connect to various sources and sink to create an end-to-end pipeline. 

2) Ease to Adopt

Azure Stream Analytics uses the SQL query to analyze the data. The Stream Analytics query language allows user to perform complex analysis by using data processing clusters. It also allows you to update/modify the functionality by creating a user-defined function.

3) Fully Managed

Azure Stream Analytics is a fully managed Platform-as-a-Service(PaaS) offering, which means the entire platform is ready to use, and you don’t have to manage any hardware or software, or job. You only have to look into business logics, and the Azure Stream Analytics service will handle everything. 

4) Reliability

Azure Stream Analytics is highly reliable in job processing and highly fault-tolerant in case of failure and have built-in recovery capabilities. It offers at least once delivery of events and guarantees event processing with a 99.9% availability at a minute level of granularity as a managed service.

5) Security

Microsoft Azure provides excellent security to the data at rest and data in motion. It supports TLS 1.2 security system and VNET to handle data and users information securely.

6) Performance

Azure Stream Analytics can efficiently process a massive volume of data per second, producing shallow latency results. Azure Stream Analytics is highly scalable, and you can quickly scale up and scale down based on the requirement. 

Solve your data replication problems with Hevo’s reliable, no-code, automated pipelines with 150+ connectors.
Get your free trial right away!

Analyzing IoT Data with Azure Stream Analytics

Now that you have a basic understanding of Azure Stream Analytics let’s understand how you can execute the platform by analyzing IoT devices’ data.

Consider the JSON payload for the input data:

{
    "timestamp": "2021-01-20T20:47:53.0000000",  
    "displayId": "WeatherSensor",  
    "temperature": 40,  
    "humidity": 34  
}  

The above data is a sample record, but in real-time hundreds of fields will be generated as a stream.

  • Log in to your Azure account, select Create a resource from the navigation pane, and then choose Analytics Job from Analytics.
Stream Analytics - Selecting the connector.
  • Choose a unique name for your job, subscription mode, resources, and location of your cluster, and then click on create.
New Stream Analytics Job - Stream Analytics
  • Navigate to the Azure Stream Analytics job in the Azure portal and select Query from the Job Topology menu.
  • Now you need to filter the data by using the below query:
Topology Menu - Stream Analytics
SELECT 
    timestamp,
    displayId AS SensorName,
    temperature AS daily_temperature,
    humidity AS Humidity
INTO
   Output
FROM
    InputStream
WHERE displayId = 'sensorA'
Query - Stream Analytics
  • The above query outputs the filtered data to the console.

For more tutorials on Azure Stream Analytics, follow the official guide from Microsoft.

Conclusion

This article teaches you about Azure Stream Analytics and how to set up an analytics process quickly. It also provides in-depth knowledge about every step’s concepts to help you understand and implement them efficiently. Carrying out an in-depth analysis of your data requires you to integrate complex data from numerous data sources in real time. These methods, however, can be challenging especially for a beginner & this is where Hevo saves the day.

Visit our Website to Explore Hevo

Hevo Data, a No-code Data Pipeline helps you transfer data from a source of your choice in a fully-automated and secure manner without having to write the code repeatedly. Hevo with its strong integration with 150+ sources & BI tools, allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also look at our unbeatable pricing that will help you choose the right plan for your business needs!

Share your experience of learning about Stream Analytics! Let us know in the comments section below!

No-code Data Pipeline For Your Data Warehouse