Our customers rely on Azure to run large scale applications and services critical to their business. To run services at scale, you need to setup alerts to proactively detect, notify, and remediate issues before it affects your customers. However, configuring alerts can be hard when you have a complex, dynamic environment with lots of moving parts.
Today, we are excited to release multi-resource support for metric alerts in Azure Monitor to help you set up critical alerts at scale. Metric alerts in Azure Monitor work on a host of multi-dimensional platform and custom metrics, and notify you when the metric breaches a threshold that was either defined by you or detected automatically.
With this new feature, you will be able to set up a single metric alert rule that monitors:
Storage spaces direct (S2D) lets you host a guest cluster on Microsoft Azure which is especially useful in scenarios where virtual machines (VMs) are hosting a critical application like SQL, Scale out file server, or SAP ASCS. You can learn more about clustering by reading the article, “Deploying laaS VM Guest Clusters in Microsoft Azure.” I am also happy to share that with the latest Azure Site Recovery (ASR) update, you can now protect these business critical applications. The ASR support of storage spaces direct allows you to take your higher availability application and make it more resilient by providing a protection against region level failure.
We continue to deliver on our promise of simplicity and help you can protect your storage spaces direct cluster in three simple steps:
Insid... Read More
Built-in machine learning (ML) models for anomaly detection in Azure Stream Analytics significantly reduces the complexity and costs associated with building and training machine learning models. This feature is now available for public preview worldwide.
What is Azure Stream Analytics?
Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data, and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language (extensible to include custom code) to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies.
Traditional way to incorporate anomaly detection capabilities in stream processing
To meet customer demand, Azure is continuously expanding. We’ve been adding new Azure regions and introducing new capabilities. As a result, customers can now move their existing virtual machines (VMs) to new regions while adopting the latest capabilities. There are other factors that prompt our customers to relocate their VMs. For example, you may want to do that to increase SLAs.
In this blog, we will walk you through the steps you need to follow to move your VMs across regions or within the same region.
Why do customers want to move their Azure IaaS Virtual Machines?
Some of the most common reasons that prompt our customers to move their virtual machines include:
• Geographical proximity: “I deployed my VM in region A and now region B, which is closer to my end users, has become avai... Read More
Customers love Azure Stream Analytics for its ease of analyzing streams of data in movement, with the ability to set up a running pipeline within five minutes. Optimizing throughput has always been a challenge when trying to achieve high performance in a scenario that can't be fully parallelized. This occurs when you don't control the partition key of the input stream, or your source “sprays” input across multiple partitions that later need to be merged. You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data. This new capability unlocks performance and aids in maximizing throughput in such scenarios.
The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number o... Read More
The IT industry is experiencing a shift from monolithic applications to microservices-based architectures. The benefits of this new approach include:
- Independent development and freedom to choose technology – Developers can work on different microservices at the same time and choose the best technologies for the problem they are solving.
- Independent deployment and release cycle – Microservices can be updated individually on their own schedule.
- Granular scaling – Individual microservices can scale independently, reducing the overall cost and increasing reliability.
- Simplicity – Smaller services are easier to understand which expedites development, testing, debugging, and launching a product.
- Fault isolation – Failure of a microservice does not have to translate into failure of other servic...
Promotional planning and demand forecasting are incredibly complex processes. Take something seemingly straight-forward, like planning the weekly flyer, and there are thousands of questions involving a multitude of teams just to decide what products to promote, and where to position the inventory to maximize sell-through. For example:
- What products do I promote?
- How do I feature these items in a store? (Planogram: end cap, shelf talkers, signage etc.)
- What pricing mechanic do I use? (% off, BOGO, multi-buy, $ off, loyalty offer, basket offer)
- How do the products I'm promoting contribute to my overall sales plan?
- How do the products I'm promoting interact with each other? (halo and cannibalization)
- I have 5,000 stores, how much inventory of each promoted item should I stock at each store?
This blog post was co-authored by Jürgen Weichenberger, Chief Data Scientist, Accenture and Mathew Salvaris, Senior Data Scientist, Microsoft
Drilling for oil and gas is one of the most dangerous jobs on Earth. Workers are exposed to the risk of events ranging from small equipment malfunctions to entire off shore rigs catching on fire. Fortunately, the application of deep learning in predictive asset maintenance can help prevent natural and human made catastrophes.
We have more information than ever on our equipment thanks to sensors and IoT devices, but we are still working on ways to process the data so it is valuable for preventing these catastrophic events. That’s where deep learning comes in... Read More
This blog post was co-authored by Daniel Savage, Principal Program Manager, Azure Stack and Tiberiu Radu, Senior Program Manager, Azure Stack.
Azure Stack at its core is an Infrastructure-as-a-Service (IaaS) platform
When we discuss Azure Stack with our customers, they see the value in Azure Stack providing cloud-native capabilities to their datacenters. They see the opportunity to modernize their apps and address the unique solutions Azure Stack can deliver, but they often pause as they ponder where to begin. They wonder how to get value from the investments they have in apps currently running on virtual machines (VM)... Read More
Azure IoT Edge is a fully managed service that allows you to deploy Azure and third-party services—edge modules—to run directly on IoT devices, whether they are cloud-connected or offline. These edge modules are container-based and offer functionality ranging from connectivity to analytics to storage—allowing you to deploy modules entirely from the Azure portal without writing any code. You can browse existing edge modules in the Azure Marketplace.
Today, we’re excited to offer the open-source Azure IoT Edge runtime preinstalled on Ubuntu virtual machines to make it even easier to get started, simulate an edge device, and scale out your automated testing.
Why use virtual machines?
Azure IoT Edge deployments are built to scale so that you can deploy globally to any number of devices and sim... Read More