The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
Tech debt is usually one of the most fraught topics on engineering teams. Engineers often feel they aren’t allowed enough time to address tech debt. Product partners wonder why engineers spend so much time working on it—or at least talking about it. “The business” always seems to insinuate that engineers should do less of it, instead focusing on shipping value to customers.
The Knowledge Base has had an upgrade, introducing our new Support Center! The Support Center is packed with tonnes of useful information to help you make the most of the RapidSpike platform.
Amazon Elastic Container Service (ECS) is a versatile platform that enables developers to build scalable and resilient applications using containers. However, containerized services, like Node.js applications, may face challenges like memory leaks, which can result in container crashes. In this blog post, we’ll delve into the process of identifying and addressing memory leaks in Node.js containers running on ECS. First, let’s look closer at what a memory leak is.
As your infrastructure and applications scale, so does the volume of your observability data. Managing a growing suite of tooling while balancing the need to mitigate costs, avoid vendor lock-in, and maintain data quality across an organization is becoming increasingly complex. With a variety of installed agents, log forwarders, and storage tools, the mechanisms you use to collect, transform, and route data should be able to evolve and adjust to your growth and meet the unique needs of your team.
Integrating AI, including large language models (LLMs), into your applications enables you to build powerful tools for data analysis, intelligent search, and text and image generation. There are a number of tools you can use to leverage AI and scale it according to your business needs, with specialized technologies such as vector databases, development platforms, and discrete GPUs being necessary to run many models. As a result, optimizing your system for AI often leads to upgrading your entire stack.