Are you tired of constantly running back and forth to check the status of your network devices? Do you wish you had a magic wand that could tell you everything you need to know about your network at a glance? Well, unfortunately, we can't give you a magic wand, but we can give you something pretty close: SNMP monitoring!
Software architectures are greatly influenced by the size and scale of the software applications. With growing size, the code base becomes complex. With scale, the deployment becomes challenging. The result: debugging becomes an increasingly time-consuming process for developers.
The goal of capacity planning is to match resources with demand. There are essentially three outcomes from this analysis. You can underestimate the resources you need (underprovision), which can hurt performance. You can overestimate (overprovision), which adds unnecessary costs. Or you can get it just right (rightsized). And, of course, you want to be rightsized at the lowest possible cost. Because many factors go into cloud capacity planning, it can feel like more of an art than a science.
In the era of cloud-native development, as businesses rely on a growing number of software tools to enable agile application delivery, platform engineering has emerged as a crucial discipline for building the technology platforms that drive DevOps efficiency. In this blog post, we explain the growing importance of platform engineering in high-performance DevOps organizations and how platform teams enable DevOps efficiency, agility, and productivity.
Five worthy reads is a regular column on five noteworthy items we have discovered while researching trending and timeless topics. This week we are exploring the multi-cloud strategy and why its the next biggest thing in cloud computing. In an age where digital innovations happen at breakneck speed, the cloud has become crucial to every enterprise.
In modern business environments, where everything is fast-paced and data-centric, companies need to be able to track and analyze data quickly and efficiently to stay competitive. Metrics play a crucial role in this, providing valuable insights into product performance, user behavior, and system health. By tracking metrics, companies can make data-driven decisions to improve their product and grow their business.
In the previous blog in our root cause analysis with logs series, we explored how to analyze logs in Elastic Observability with Elastic’s anomaly detection and log categorization capabilities. Elastic’s platform enables you to get started on machine learning (ML) quickly. You don’t need to have a data science team or design a system architecture. Additionally, there’s no need to move data to a third-party framework for model training.
