Skip to main content

Running Azure Stream Analytics on Edge Devices

Azure Stream Analytics enable us to analyze data in real time. Connecting multiple streams of data, running queries on top of them without having to deploy complex infrastructure become possible using Azure Stream Analytics.
This Azure service is extremely powerful when you have data in the cloud. But there was no way to analyze the data stream at device or gateway level.
For example if you are in a medical laboratory you might have 8 or 12 analyzers. To be able to analyze the counters of all this devices to detect a malfunction you would need to push counters value to Azure, even if you don't need for other use cases.

Wait! There is a new service in town that enable us to run the same Azure Stream Analytics queries but at gateway or device level - Azure Stream Analytics on Edge Devices. Even if the name is long and has Azure in the name, the service is a stand alone service that runs on-premises.
This service allows us to run the same queries in real time over data streams that we have on our on-premises devices. From the job (query) perspective we have the same features like SQL language, temporal filters, window query and full integration with JavaScript code.
On of the key features of Azure Stream Analytics on Edge Devices is the low latency, that is perfect for systems where we need to react fast without downtime. This service comes as a component inside Azure IoT Gateway SDK, but doesn't limit us to use Azure, we can push data from our queries to our own custom modules.

Using this service we can now analyze data streams directly on our gateways and push to the cloud only the tings that we want. The same query that we have on the cloud we can run it now on our gateway and push only relevant data to Azure. Beside this, we can generate alerts or actions on our gateway from Azure Stream Analytics on Edge Devices and react directly on our gateway or device.
Key features:

  • On-premises
  • SQL-like query
  • Low latency
  • OPC-UA, MQTT, Modbus support
  • Same features as Azure Stream Analytics 
You can find more about this it on Azure blog.

Comments

Popular posts from this blog

Why Database Modernization Matters for AI

  When companies transition to the cloud, they typically begin with applications and virtual machines, which is often the easier part of the process. The actual complexity arises later when databases are moved. To save time and effort, cloud adoption is more of a cloud migration in an IaaS manner, fulfilling current, but not future needs. Even organisations that are already in the cloud find that their databases, although “migrated,” are not genuinely modernised. This disparity becomes particularly evident when they begin to explore AI technologies. Understanding Modernisation Beyond Migration Database modernisation is distinct from merely relocating an outdated database to Azure. It's about making your data layer ready for future needs, like automation, real-time analytics, and AI capabilities. AI needs high throughput, which can be achieved using native DB cloud capabilities. When your database runs in a traditional setup (even hosted in the cloud), in that case, you will enc...

Cloud Myths: Migrating to the cloud is quick and easy (Pill 2 of 5 / Cloud Pills)

The idea that migration to the cloud is simple, straightforward and rapid is a wrong assumption. It’s a common misconception of business stakeholders that generates delays, budget overruns and technical dept. A migration requires laborious planning, technical expertise and a rigorous process.  Migrations, especially cloud migrations, are not one-size-fits-all journeys. One of the most critical steps is under evaluation, under budget and under consideration. The evaluation phase, where existing infrastructure, applications, database, network and the end-to-end estate are evaluated and mapped to a cloud strategy, is crucial to ensure the success of cloud migration. Additional factors such as security, compliance, and system dependencies increase the complexity of cloud migration.  A misconception regarding lift-and-shits is that they are fast and cheap. Moving applications to the cloud without changes does not provide the capability to optimise costs and performance, leading to ...

Cloud Myths: Cloud is Cheaper (Pill 1 of 5 / Cloud Pills)

Cloud Myths: Cloud is Cheaper (Pill 1 of 5 / Cloud Pills) The idea that moving to the cloud reduces the costs is a common misconception. The cloud infrastructure provides flexibility, scalability, and better CAPEX, but it does not guarantee lower costs without proper optimisation and management of the cloud services and infrastructure. Idle and unused resources, overprovisioning, oversize databases, and unnecessary data transfer can increase running costs. The regional pricing mode, multi-cloud complexity, and cost variety add extra complexity to the cost function. Cloud adoption without a cost governance strategy can result in unexpected expenses. Improper usage, combined with a pay-as-you-go model, can result in a nightmare for business stakeholders who cannot track and manage the monthly costs. Cloud-native services such as AI services, managed databases, and analytics platforms are powerful, provide out-of-the-shelve capabilities, and increase business agility and innovation. H...