The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
With Grafana 10, the latest major release of our data visualization platform, we wanted to explore new ways to empower our developer community. Case in point: Grafana Scenes, a new frontend library that enables developers to create dashboard-like experiences — such as querying and transformations, dynamic panel rendering, and time ranges — directly within their Grafana application plugins.
Having your Cribl Stream instance connected to a remote git repo is a great way to have a backup of the cribl config. It also allows for easy tracking and viewing of all Cribl Stream config changes for improved accountability and auditing. Our Goal: Get Cribl configured with a remote Git repo and also configured with git signed commits. Git signed commits are a way of using cryptography to digitally add a signature to git commits.
Business-critical infrastructure and services generate massive volumes of observability data from many disparate sources. It can be challenging to synthesize all this data to gain actionable insights for detecting and remediating issues—particularly in the heat of incident response.
This year at DASH, we announced new products and features that enable your teams to get complete visibility into their AI ecosystem, utilize LLM for efficient troubleshooting, take full control of petabytes of observability data, optimize cloud costs, and more. With Datadog’s new AI integrations, you can easily monitor every layer of your AI stack. And Bits AI, our new DevOps copilot, helps speed up the detection and resolution of issues across your environment.
Maintaining the quality of your code becomes increasingly difficult as your organization grows. Engineering teams need to release code quickly while still finding a way to enforce best practices, catch security vulnerabilities, and prevent flaky tests. To address this challenge, Datadog is pleased to introduce Quality Gates, a feature that automatically halts code merges when they fail to satisfy your configured quality checks.
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.
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.
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.
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.
