Microsoft followed through with the general release of an important cloud service last week, laying the groundwork for real-time insight with Azure Stream Analytics.
As the name suggests, Azure Stream Analytics (ASA) is about analysing data in flight.
Examples include data flowing from devices, sensors, web sites, apps, and infrastructure systems. Analysis is done with a SQL-like language that lets users visualise, alert, and act on events in real time.
Introduced as a technology preview last year, ASA has been used by beta customers in use cases including smart-grid management, predictive maintenance, and remote device monitoring.
Japanese technology manufacturer Fujitsu, for example, used Azure services including ASA to capture sensor and machine data within one of its factories so it cloud pioneer environmental monitoring capabilities.
That ECO-Management Dashboard (shown below) is now part of a larger IoT/M2M platform that teams Fujitsu devices with Azure services to support industrial monitoring and analytics.
“Microsoft says ASA offers faster development and simpler coding compared with Java-centric streaming options such as Apache Storm,” observes Doug Henschen, VP and Principal Analyst, Constellation Research.
“You could also read that as a dig against Google, which last week announced that its beta Google Cloud Dataflow service is available as a preview.”
Announced about a year ago and designed to support both batch and stream processing, Dataflow is aimed at Java and Python developers. The service includes an open-source Java SDK and work is underway to support Apache Spark, another streaming-data-analysis option.
“What Microsoft didn’t mention is the fact that it’s playing catch-up on the streaming front with Amazon Web Services, which made its Kinesis streaming data ingestion and processing service generally available more than a year ago,” Henschen adds.
“In a reversal of those roles, however, AWS was the follower two weeks ago when it introduced Amazon Machine Learning, lagging behind Microsoft’s July preview and February general release of the Azure Machine Learning service.”
For Henschen however, the industry should forget the horse race.
“We’re at the very beginning of a mainstream push into real-time analytics,” he explains.
“Complex event processing vendors that have long supported financial trading floor operations and intelligence agencies have been talking about the mainstreaming of real-time performance for years.
“It looks like the cloud is finally making streaming data analysis broadly accessible. Two years from now, nobody will remember which cloud provider was the first to deliver a stream processing service.”
Henschen believes stream processing and analysis is table stakes for building real-time, data-driven applications.
Indeed, Azure Stream Analytics and Azure Machine Learning are two key components of a Microsoft Azure IoT Suite announced last month and set for preview release in the second half of this year.
Microsoft says the Azure IoT suite will offer three ready-to-customise applications: remote monitoring (think KPIs from remote devices), asset management (controlling remote devices in some way), and predictive maintenance (monitoring and analysing performance over time so you can avoid unplanned downtime).