Operations | Monitoring | ITSM | DevOps | Cloud

“Try turning it off and on again.” This advice normally works for my mom, but not for me. When I needed to present at an event last year, I got frustrated when I couldn’t connect to the network. The front desk at the venue referred me to technical services. “Uh-oh,” I thought. “Here we go.” But I was wrong. The technical services person quickly assessed my level of know-how and adjusted her talk track to cater to my needs.

One of Checkly's strengths is the capability to monitor key transactions on your site. It'd be missed opportunity if we didn't reuse it to monitor our own product! But for some important flows that comes with a couple of pitfalls. In this post, we'll take a closer look at how we monitor one of our top key flows: signup.

IoT has rapidly moved from a fringe technology to a mainstream collection of techniques, protocols, and applications that better enable you to support and monitor a highly distributed, complex system. One of the most critical challenges to overcome is processing an ever-growing stream of analytics data, from IoT security data to business insights, coming from each device. Many protocols have been implemented for this, but could logs provide a powerful option for IoT data and IoT monitoring?

While Go provides a testing package and a go test command, the former only offers basic testing capabilities. The package also has some drawbacks, such as missing assertions and increasing repetition with large-scale tests. As a result, several Go testing frameworks have been created to augment it. Go testing frameworks consist of tools and resources for creating and designing tests. Some of these frameworks incorporate the testing package and go test command, while others take a different approach.

More than 2.14 billion global consumers are expected to buy goods and services online in 2021, according to Statista. That is up 29% from 1.66 billion digital customers just six years ago. This rapid change in shopping habits is driving retailers’ digital transformations and ever more advanced technologies. Many retailers have begun automating back office functions like claims processing, accounting and inventory management.

Last month Gartner published its first ever “Market Guide for AI Offerings in CSP Network Operations,” and we’re excited to share that Anodot has been identified as a Representative Vendor in the report. According to the Gartner report, “CSPs are focusing on automation of their network operations to improve efficiency and customer experience, and mitigate security concerns.” The market guide presents many new and actionable insights.

The focus of the 2.5 version was on expanding support for HAProxy configuration keywords, and that’s where most of the effort during this release cycle was spent. We will continue doing that during the next couple of versions to gain complete feature parity with both the HAProxy configuration and Runtime API so that you can use the Data Plane API as a full-featured way to configure HAProxy.

Though we’re living through a time of extraordinary innovation in GPU-accelerated machine learning, the latest research papers frequently (and prominently) feature algorithms that are decades, in certain cases 70 years old. Some might contend that many of these older methods fall into the camp of ‘statistical analysis’ rather than machine learning, and prefer to date the advent of the sector back only so far as 1957, with the invention of the Perceptron.