Today, we are pleased to announce the general availability of Microsoft Azure SQL Edge, a small footprint database engine optimized for IoT workloads. Built on the same engine the powers Microsoft SQL server and Azure SQL, this containerized solution provides the same industry leading security, familiar developer experience, and tooling that many teams already know and trust.
Azure SQL Edge with its many features enables affordable edge solutions for even the most demanding architectures:
- Built-in time series and streaming support for analyzing data while it’s being streamed using time-windowing, aggregation, and filtering capabilities, and achieve deeper insights by combining data types such as time-series and graph.
- Built in ML and AI capabilities by having ONNX runtime support for operationalizing the mL models and providing additional capabilities to run native scoring closest to where the data is generated.
- Your choice of platform. Run SQL on ARM 64 and x64 architectures. A small footprint under 500mb means you can deploy on IoT devices as small as a Raspberry Pi.
- Develop once, deploy anywhere from edge to cloud. Consistent app development, security, and management from Azure SQL to SQL Server to the IoT edge.
- Native integration with Azure products and services. Simplify and strengthen your cloud-to-edge architecture with native integration to Azure products and services such as Azure IoT Edge and Azure Stack Edge.
HOW IT WORKS
Millions of messages (or events) are generated by the IoT devices and sensors. By capturing and analyzing these messages, edge applications built using Azure SQL Edge as the data engine can decipher valuable insights and immediately trigger appropriate actions in a wide range of locations, regardless of whether an internet connection is available.
Events/data generated from the IoT data sources are sent to an instance of IoT Edge including the Azure SQL Edge module, the IoT application module, and other optional modules. Azure SQL Edge provides the application with local storage of time-series and other data, streaming analytics, real-time scoring of data based on ML models, data protection, and real time visibility to stored data.
Data stored in Azure SQL Edge can be sent from IoT Hub to any of the long-term storage services in Azure, such as Azure Cosmos DB or SQL Database. From storage services, data can be sent to Azure Machine Learning for retraining of models. These updated models can then be sent back to Azure SQL Edge to improve AI performance for the IoT application.
Even with its powerful and rich feature set, the startup memory footprint for Azure SQL Edge is less than 500 MB. This small footprint gives teams the freedom to design and build applications that run on a nearly unlimited range of IoT devices, including on simple battery-powered or solar-powered devices deployed in remote areas with limited connectivity. Azure SQL Edge is a containerized Linux application that runs on either an ARM64-based or an x64-based processor.
In addition, Azure SQL Edge offers platform consistency with SQL Server and SQL Database, allowing developers to build apps once and then deploy them anywhere, whether at the edge, on-premises, or in the cloud.
CUSTOMER FEEDBACK
"Traditionally, it took two weeks to prep each client's monthly data, to verify the report, and for the client to receive it. Now we deliver the first draft of that report in eight minutes. This is a game changer, it massively simplifies everything we do.”
Richard Corless, Lead Cloud Architect, Fugro
“Before edge computing, by the time cloud analysis noted a problem, we had lost response time and sometimes wasted product. With SQL Edge, we reduce both our reaction time and the number of cycles needed.“
Jochen Scheuerer: Head of Connected Smart Factory. Zeiss
WANT MORE? KEEP LEARNING
Get more details on Azure SQL Edge
Dig into the Azure SQL Edge Whitepaper
Access tutorials, demos and documentation
Join us on Sept 28 as we interview Vasiya Krishnan, PM - Azure SQL Edge to learn more.
Microsoft