Navigatie overslaan

8 reasons to choose Azure Stream Analytics for real-time data processing

Gepost op 7 juni, 2018

Program Manager, Azure Big Data

Processing big data in real-time is now an operational necessity for many businesses. Azure Stream Analytics is Microsoft’s serverless real-time analytics offering for complex event processing. It enables customers to unlock valuable insights and gain competitive advantage by harnessing the power of big data. Here are eight reasons why you should choose ASA for real-time analytics.

1. Fully integrated with Azure ecosystem: Build powerful pipelines with few clicks

Whether you have millions of IoT devices streaming data to Azure IoT Hub or have apps sending critical telemetry events to Azure Event Hubs, it only takes a few clicks to connect multiple sources and sinks to create an end-to-end pipeline. Azure Stream Analytics provides best-in-class integration to store your output, like Azure SQL Database, Azure Cosmos DB, Azure Data Lake Store. It also enables you to trigger custom workflows downstream with Azure Functions, Azure Service Bus Queues, Azure Service Bus Topics, or create real-time dashboards using Power BI.

2. Developer productivity

One of the biggest advantages of Stream Analytics is the simple SQL-based query language with its powerful temporal constraints to analyze data in motion. Familiarity with SQL language is sufficient to author powerful queries. Azure Stream Analytics supports language extensibility via JavaScript user-defined functions (UDFs) or user-defined aggregates to perform complex calculations as part of a Stream Analytics query. With Stream Analytics Visual Studio tools you may author queries offline and use CI/CD to submit jobs to Azure.  Native support for geospatial functions makes it easy to tackle complex scenarios like fleet management and mobile asset tracking.

3. Intelligent edge

Most data becomes useless just seconds after it’s generated. In many cases, processing data closer to the point of generation is becoming more and more critical. This allows for lower bandwidth costs and ability of a system to function even with intermittent connectivity. Azure Stream Analytics on IoT Edge enables you to deploy real-time analytics closer to IoT devices so that you can unlock the full value of device-generated data.

4. Easily leverage the power of machine learning

Azure Stream Analytics offers real-time event scoring by integrating with Azure Machine Learning solutions. Additionally, Stream Analytics offers built-in support for commonly used scenarios such as anomaly detection, which helps reduce the complexity associated with building and deploying an ML model in your hot-path analytics pipeline to a simple function call. Users can easily detect common anomalies such as spikes, dips, slow positive or negative trends with these online learning and scoring models.

5. Lower your cost of innovation

There are zero upfront costs and you only pay for the number of streaming units you consume to process your data streams. There is absolutely no commitment or cluster provisioning allowing you to focus on making best use of this technology.

6. Best-in-class financially backed SLA by the minute

We understand it is critical for business to prevent data loss and have business continuity. Stream Analytics processes millions of events every second and can deliver results with low latency. This is why Stream Analytics guarantees event processing with a 99.9 percent availability SLA at the minute level, which is unparalleled in the industry.

7. Scale instantly

Stream Analytics is a fully managed serverless (PaaS) offering on Azure. There is no infrastructure to worry about, and no servers, virtual machines, or clusters to manage. We do all the heavy lifting for you in the background. You can instantly scale up or scale-out the processing power from one to hundreds of streaming units for any job.

8. Reliable

Stream Analytics guarantees “exactly once” event processing and at least once delivery of events. It has built-in recovery capabilities in case the delivery of an event fails. So, you never have to worry about your events getting dropped.

Getting started is easy

There is a strong and growing developer community that supports Stream Analytics. We at Microsoft are committed to improving Stream Analytics and always listening for your feedback to improve our product! Learn how to get started and build a real-time fraud detection system.