Operations | Monitoring | ITSM | DevOps | Cloud

Data center planning can be a chore. It's often a manual and time-consuming process that involves gathering data that may or may not be accurate from multiple tools. Plus, modern data center environments are increasingly complex and distributed which adds to the challenge.

Expenditure on cloud computing services reached a mammoth 225 billion dollars in 2022. Companies start their cloud-native journeys with the best intentions and consume the many benefits including: But current cloud expenditure growth levels are unsustainable for many organizations and with 82% of organizations investing in FinOps staff it shows that cloud expenditure is top of mind in the c-suite.

Amid an AI boom and developing research, machine learning (ML) models such as OpenAI’s ChatGPT and Midjourney’s generative text-to-image model have radically shifted the natural language processing (NLP) and image processing landscape. Due to this new and powerful technology, developing and deploying ML models has quickly become the new frontier for software development.

Monitoring is all about data. When you implement a monitoring tool, you have to make sure that the monitoring software can handle data. Today, data flows at high speeds and in large volumes. Data also comes in diverse forms, which increases the complexity of data ingestion. Because of this, monitoring solution providers promote, among others, their data processing capacities. If a monitoring platform can handle large and diverse data that comes in at a high velocity, it becomes a big advantage.

A containerized approach to software deployment means you can deploy at scale without having to worry about the configuration of each unit. In Kubernetes, clusters do the heavy lifting for you—they’re the pooled resources that run the pods that hold your individual containers. You can divide each cluster by namespace, which allows you to assign nodes (ie the machine resources in a cluster) to different roles or different teams. Resource quotas limit what each namespace can use.

Channels are at the core of how teams collaborate in Mattermost. While team conversations take center stage, every channel is packed with additional features to help you stay in sync. Today, let’s take a closer look at channel headers, and how you can use them to help your team use Mattermost channels more effectively.

Deciding what to migrate, what to modernize, and what to retain on-premises is part of enterprise IT infrastructure management. When a refresh cycle is up in your data center, there are two very different types of competing motions you need to evaluate. While they may appear to be independent, they’re also kind of not, so it can be tricky to decide which one to execute—or even to execute both—and to do so smoothly.

In this article, we provide a concise guide to help you get started with Graphite quickly and efficiently. We cover the basic concepts, architectural considerations, and metrics aggregation of Graphite. We also explain the data feeding methods, metrics format, and storage using Graphite's file-based database. Additionally, we discuss visualization options, including Graphite-Web and Grafana.