There’s a revolution underway that is positioning companies to take operational efficiency to new levels and inform the next generation of products and services. This revolution of course, is the Internet of Things (IoT).
Here at Microsoft, we’re committed to helping our customers harness the power of IoT through our Azure IoT solutions. We’re also committed to helping customers take the first steps through our IoT in Action series. Our next delivery is coming February 13, 2018 in San Francisco, which I’d encourage you to attend.
But first, I’d like to introduce you to some recent updates to Azure IoT Suite that are making IoT solutions easier and more robust than ever.
Azure IoT powers the business revolution
With our long history of driving business success and digital transformation for our customers, it’s no surprise that we’re also focused on powering the business revolution through our robust Azure IoT suite of products.
So how does Azure IoT benefit businesses?
First off, it’s a quick and scalable solution. Our preconfigured solutions can accelerate your development process, so you can get up and running quickly. You can connect existing devices and add new ones using our device SDKs for platforms including
As companies continue to fully roll out their IoT projects, management of the various components of the solution becomes a critical part of their operations. The flexibility of Azure IoT Hub to enable customers to start small, paying only for the amount of IoT Hub capacity needed at any point along the device deployment curve, helps drive predictability in the cost of an IoT solution.
However, the potentially irregular rate of device and message growth in an IoT solution does add a unique challenge for operations. When the number of messages ingested from devices in a given day exceeds the limit of the chosen IoT Hub capacity, the IoT Hub will begin to reject messages until either the IoT Hub is scaled-up, or the time rolls over into the next day (UTC time). Wouldn’t it be nice to have IoT Hub just automatically scale up to a higher capacity when a certain threshold of messages is met, before this limit is reached?
While at this point, IoT Hub does not have this capability built into the service, we have published a sample solution for monitoring and automatically scaling your IoT Hub based on reaching a specific threshold of messages. The sample,
How do you go about answering those perplexing questions such as what secure hardware to use? How do I gauge the level of security? How much security do I really need and hence how much premium should I place on secure hardware? We’ve published a new whitepaper to shed light on this subject matter.
In our relentless commitment to securing IoT deployments worldwide, we continue to raise awareness to the true nature of security—that it is a journey and never an endpoint. Challenges emerge, vulnerabilities evolve, and solutions age thereby triggering the need for renewal if you are to maintain a desired level of security.
Securing your deployment as desired comprises planning, architecture, and execution main phases. For IoT, these are further broken down into sub-phases to include design assessment, risk assessment, model assessment, development, and deployment as shown in Figure 1. The decision process at each phase is equally important, the process must take all other phases into consideration for optimal efficacy. This is especially true when choosing the right secure hardware, also known as secure silicon or Hardware Secure Module(HSM), to secure an IoT deployment.
Figure 1: The IoT Security Lifecycle
Apache Kafka on the Azure HDInsight was added last year as a preview service to help enterprises create real-time big data pipelines. Since then, large companies such as Toyota, Adobe, Bing Ads, and GE have been using this service in production to process over a million events per sec to power scenarios for connected cars, fraud detection, clickstream analysis, and log analytics. HDInsight has worked very closely with these customers to understand the challenges of running a robust, real-time production pipeline at an enterprise scale. Using our learnings, we have implemented key features in the managed Kafka service on HDInsight, which is now generally available.
A fully managed Kafka service for the enterprise use case
Running big data streaming pipelines is hard. Doing so with open source technologies for the enterprise is even harder. Apache Kafka, a key open source technology, has emerged as the de-facto technology for ingesting large streaming events in a scalable, low-latency, and low-cost fashion. Enterprises want to leverage this technology, however, there are many challenges with installing, managing, and maintaining a streaming pipeline. Open source bits lack support and in-house talent needs to be well versed with these technologies to ensure the highest levels of