May
02
2019
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Microsoft brings Plug and Play to IoT

Microsoft today announced that it wants to bring the ease of use of Plug and Play, which today allows you to plug virtually any peripheral into a Windows PC without having to worry about drivers, to IoT devices. Typically, getting an IoT device connected and up and running takes some work, even with modern deployment tools. The promise of IoT Plug and Play is that it will greatly simplify this process and do away with the hardware and software configuration steps that are still needed today.

As Azure corporate vice president Julia White writes in today’s announcement, “one of the biggest challenges in building IoT solutions is to connect millions of IoT devices to the cloud due to heterogeneous nature of devices today – such as different form factors, processing capabilities, operational system, memory and capabilities.” This, Microsoft argues, is holding back IoT adoption.

IoT Plug and Play, on the other hand, offers developers an open modeling language that will allow them to connect these devices to the cloud without having to write any code.

Microsoft can’t do this alone, though, since it needs the support of the hardware and software manufacturers in its IoT ecosystem, too. The company has already signed up a number of partners, including Askey, Brainium, Compal, Kyocera, STMicroelectronics, Thundercomm and VIA Technologies . The company says that dozens of devices are already Plug and Play-ready and potential users can find them in the Azure IoT Device Catalog.

May
02
2019
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Microsoft brings Azure SQL Database to the edge (and Arm)

Microsoft today announced an interesting update to its database lineup with the preview of Azure SQL Database Edge, a new tool that brings the same database engine that powers Azure SQL Database in the cloud to edge computing devices, including, for the first time, Arm-based machines.

Azure SQL Edge, Azure corporate vice president Julia White writes in today’s announcement, “brings to the edge the same performant, secure and easy to manage SQL engine that our customers love in Azure SQL Database and SQL Server.”

The new service, which will also run on x64-based devices and edge gateways, promises to bring low-latency analytics to edge devices as it allows users to work with streaming data and time-series data, combined with the built-in machine learning capabilities of Azure SQL Database. Like its larger brethren, Azure SQL Database Edge will also support graph data and comes with the same security and encryption features that can, for example, protect the data at rest and in motion, something that’s especially important for an edge device.

As White rightly notes, this also ensures that developers only have to write an application once and then deploy it to platforms that feature Azure SQL Database, good old SQL Server on premises and this new edge version.

SQL Database Edge can run in both connected and fully disconnected fashion, something that’s also important for many use cases where connectivity isn’t always a given, yet where users need the kind of data analytics capabilities to keep their businesses (or drilling platforms, or cruise ships) running.

Feb
07
2019
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Microsoft Azure sets its sights on more analytics workloads

Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine learning models, and the more data you have, the better. It’s maybe no surprise, then, that the big cloud vendors started investing in data warehouses and lakes early on. But that’s just a first step. After that, you also need the analytics tools to make all of this data useful.

Today, it’s Microsoft turn to shine the spotlight on its data analytics services. The actual news here is pretty straightforward. Two of these are services that are moving into general availability: the second generation of Azure Data Lake Storage for big data analytics workloads and Azure Data Explorer, a managed service that makes easier ad-hoc analysis of massive data volumes. Microsoft is also previewing a new feature in Azure Data Factory, its graphical no-code service for building data transformation. Data Factory now features the ability to map data flows.

Those individual news pieces are interesting if you are a user or are considering Azure for your big data workloads, but what’s maybe more important here is that Microsoft is trying to offer a comprehensive set of tools for managing and storing this data — and then using it for building analytics and AI services.

(Photo credit:Josh Edelson/AFP/Getty Images)

“AI is a top priority for every company around the globe,” Julia White, Microsoft’s corporate VP for Azure, told me. “And as we are working with our customers on AI, it becomes clear that their analytics often aren’t good enough for building an AI platform.” These companies are generating plenty of data, which then has to be pulled into analytics systems. She stressed that she couldn’t remember a customer conversation in recent months that didn’t focus on AI. “There is urgency to get to the AI dream,” White said, but the growth and variety of data presents a major challenge for many enterprises. “They thought this was a technology that was separate from their core systems. Now it’s expected for both customer-facing and line-of-business applications.”

Data Lake Storage helps with managing this variety of data since it can handle both structured and unstructured data (and is optimized for the Spark and Hadoop analytics engines). The service can ingest any kind of data — yet Microsoft still promises that it will be very fast. “The world of analytics tended to be defined by having to decide upfront and then building rigid structures around it to get the performance you wanted,” explained White. Data Lake Storage, on the other hand, wants to offer the best of both worlds.

Likewise, White argued that while many enterprises used to keep these services on their on-premises servers, many of them are still appliance-based. But she believes the cloud has now reached the point where the price/performance calculations are in its favor. It took a while to get to this point, though, and to convince enterprises. White noted that for the longest time, enterprises that looked at their analytics projects thought $300 million projects took forever, tied up lots of people and were frankly a bit scary. “But also, what we had to offer in the cloud hasn’t been amazing until some of the recent work,” she said. “We’ve been on a journey — as well as the other cloud vendors — and the price performance is now compelling.” And it sure helps that if enterprises want to meet their AI goals, they’ll now have to tackle these workloads, too.

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