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
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.
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.
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Key Features of Azure Stream Analytics
The following are some of the key features of Azure Stream Analytics:
- Ease of Use
- Ease to Adopt
- Fully Managed
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.
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.
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.
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.
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:
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.
- Choose a unique name for your job, subscription mode, resources, and location of your cluster, and then click on create.
- 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:
displayId AS SensorName,
temperature AS daily_temperature,
humidity AS Humidity
WHERE displayId = 'sensorA'
- The above query outputs the filtered data to the console.
For more tutorials on Azure Stream Analytics, follow the official guide from Microsoft.
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.
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