Operations | Monitoring | ITSM | DevOps | Cloud

One of the fundamental promises of the cloud, when organizations embrace it, is significant cost savings compared to its on-premises costs. However, organizations to realize savings is required to proactively plan and monitor the application’s cost at a granular level. Azure cost anomaly detection involves promptly identifying, rectifying, and analysing unexpected Azure cost events to minimize their impact on the business.

There’s no debate — in our increasingly AI-driven, lean and data-heavy world, automating key tasks to increase effectiveness and efficiency is the ultimate name of the game. No matter what job you hold today, you’re likely being pushed to not only do more with less, but also perform your work with a tighter focus on specific outcomes and SLOs.

Quickly finding and resolving monitoring anomalies can make all the difference between service issues – and service excellence. But it’s far from easy, whether you’re trying to sift through countless alerts, understand the context behind anomalies, or swiftly pinpoint their root causes. If you’re an ITOps practitioner or enterprise architect looking to fine-tune your anomaly detection and resolution skills, you’ve come to the right place.

Anybody who monitors logs of any kinds, knows that the extracting useful information from the gigabytes of data being collected remains one of the biggest challenges. One of the more important metrics to keep an eye on are all sorts of logons that occur in your network – especially if they originate on the Internet – such as VPN logins.

In this webinar hosted by InfluxDB and HiveMQ, we focus on how you can create value for your business using new tools in the AI and database ecosystem to quickly deploy AI models to perform tasks like anomaly detection. The webinar starts with a high-level overview of how MQTT and time series data can be valuable in an industrial IoT environment.

Welcome to the third chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language.

Any existing InfluxDB user will notice that InfluxDB underwent a transformation with the release of InfluxDB 3.0. InfluxDB v3 provides 45x better write throughput and has 5-25x faster queries compared to previous versions of InfluxDB.

We’re extremely excited to announce the availability of the EventSentry v5.1, which will detect threats and suspicious behavior more effectively – while also providing users with additional reports and dashboards for CMMC and TISAX compliance. The usability of EventSentry was also improved across the board, making it easier to use, manage and maintain EventSentry on a day-by-day basis. We also released 60+ training videos to help you get started and take EventSentry to the next level.

Welcome to the second chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 2) is focused on different types of anomalies.

Welcome to the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language. This blog post (Chapter 1) is focused on.