We know a lot of you are on vacation so we’ll keep this month’s State of Robotics edition short and sweet. Looking for an easy way to get familiar with ROS 2? We recently published a few helpers on how to simulate robots with turtlesim to help our readers get a rolling start on ROS2. For something that carries a little more punch, follow our tutorial on simulating a TurtleBot3.

Scripting languages (VBScript, Python, PowerShell etc) are both flexible and convenient to create small scripts, to handle a simple monitoring task (such as poll a device for data or execute custom alert). Python has an advantage of being general purpose cross-platform scripting language for years, with many well-known scripts either already available on the Net, or quick to compose.

Quick, do you know what code is currently deployed to production? How about staging? How far apart are these environments? Sleuth tracks your deployments, and today, Sleuth is launching first-class environments support. I'm going to show you how it works, steps to take to migrate your existing projects, and where we plan to take it in the future.

In this post, we learn how to use tasks in combination with checks for monitoring with InfluxDB.

AWS Step Functions is a great service for orchestrating multi-step workflows with complex logic. It’s fast to implement, relatively easy to use and just works. The problem is its price. For relatively low-scale projects, it’s a feasible solution. But for large-scale, enterprise-grade orchestration with hundreds of millions of processes, each with dozens of steps, it can be cost prohibitive.

San Jose, CA, August 6, 2020 – AUCloud’s IT infrastructure has seen a 30% savings in IT capacity and a 20% improvement in performance by implementing deep IT monitoring tools. The Australian Government and Critical National Industry Infrastructure-as-a-Service (IaaS) provider’s improvements in results were gained through its partnership with Virtana, a leader in enterprise hybrid cloud migration and optimization.

The Silicon Review In today’s digital era, the IT organizations equipped to see and fully understand their applications and infrastructure are well poised to find, fix, and prevent performance or cloud-related issues – often before they arise. However, without expert guidance and fine-tuned solutions, digital transformation and the promise of hybrid cloud infrastructures can create unforeseen complexities throughout the process.

“If I have to pull this car over, there will be trouble!” Does this evoke childhood memories of summer adventures in the family car? It’s the same sort of dread we get when the IT budget holder turns around and threatens to pull over on our journey to the cloud or hybrid cloud. The family summer road trip may be filled with great expectations, but the last thing we want is an unforeseen disruption along the way. And migration to the cloud is no different.

Today, we made an important announcement that builds on and extends our category leadership in Continuous Intelligence. This blog is to put that announcement into context for our customers, partners and the broader industry. In 2017, we shared our strategy to democratize machine data by building a cloud-native, Continuous Intelligence Platform™ to 1) unify modern application and machine data; 2) support the ongoing proliferation of users and access; and 3) leverage cloud-scale economics.

As more and more applications move to the cloud, the complexity of application architectures inevitably increases. It is a burden we willingly take on because the benefits—flexible deployment, technology diversity, independent scaling, and much more— tend to far outweigh the costs. But along this transition, most organizations face a dilemma, to divert resources to the necessary tooling for effective monitoring and troubleshooting of these systems – i.e.