AWS Systems Manager (SSM), an end-to-end management solution for AWS resources, provides a marketplace of pre-packaged software scripts for SSM-managed Windows and Linux instances, enabling AWS users to automatically install custom software on large groups of instances.
Many organizations rely on service level objectives (SLOs) to help them gauge the reliability of their products. By setting SLOs that define clear and measurable reliability targets, businesses can ensure they are delivering positive end-user experiences to their customers. Clearly defined SLOs also make it much easier for businesses to understand what tradeoffs they may have to make in order to deliver those specific experiences.
In Part 1 of this series, we introduced you to the key metrics you should be monitoring to ensure that you get optimal performance from CoreDNS running in your Kubernetes clusters. In Part 2, we showed you some tools you can use to monitor CoreDNS. In this post, we’ll show you how you can use Datadog to monitor metrics, logs, and traces from CoreDNS alongside telemetry from the rest of your cluster, including the infrastructure it runs on.
In Part 1 of this series, we looked at key metrics you should monitor to understand the performance of your CoreDNS servers. In this post, we’ll show you how to collect and visualize these metrics. We’ll also explore how CoreDNS logging works and show you how to collect CoreDNS logs to get even deeper visibility into your Deployment.
CoreDNS is an open source DNS server that can resolve requests for internet domain names and provide service discovery within a Kubernetes cluster. CoreDNS is the default DNS provider in Kubernetes as of v1.13. Though it can be used independently of Kubernetes, this series will focus on its role in providing Kubernetes service discovery, which simplifies cluster networking by enabling clients to access services using DNS names rather than IP addresses.
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.
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.
Effective mobile application testing that meets all the requirements of modern quality assurance can be challenging. Not only do teams need to create tests that cover a range of different device types, operating system versions, and user interactions—including swipes, gestures, touches, and more—they also have to maintain the infrastructure and device fleets necessary to run these tests.
The volume of logs that organizations collect from all over their systems is growing exponentially. Sources range from distributed infrastructure to data pipelines and APIs, and different types of logs demand different treatment. As a result, logs have become increasingly difficult to manage. Organizations must reconcile conflicting needs for long-term retention, rapid access, and cost-effective storage.
