Migrating your on-prem applications to Azure can help you improve scalability, reliability, and security. It can also help reduce costs and free your engineering teams to focus on innovation and performance optimization. But it can be hard to understand Azure costs as they evolve during your migration and to see how they correlate with your resource utilization once you’re up and running in Azure.
Many organizations have faced the complex challenges that come with mainframe monitoring. MIPS-based cost models make native mainframe software expensive, and deploying individual agents to user desktops and devices is difficult to maintain and scale.
As enterprises build and scale business-critical applications on Azure, they need continuous visibility to understand the health and performance of their services. This can be a challenge, especially for enterprises with large-scale deployments that include an ever-increasing number of subscriptions, resources, and teams.
End-to-end visibility into pipelines is crucial for ensuring the health and performance of your CI system, especially at scale. Within extensive CI systems—which operate under the strain of numerous developers simultaneously pushing commits—even the slightest performance regression or uptick in failure rates can compound rapidly and have tremendous repercussions, causing major cost overruns and impeding release velocity across organizations.
Roku is a popular streaming platform that allows users to access a wide variety of TV shows, movies, and other types of online video content. With its easy-to-use interface and affordable hardware, Roku has become one of the most popular streaming platforms in the world. At the end of 2022, Roku reported having over 70 million active users, with content available through 350+ channels.
XDP, or eXpress Data Path, is a Linux networking feature that enables you to create high-performance packet-processing programs that run in the kernel. Introduced in Linux 4.8 and built on extended Berkeley Packet Filter (eBPF), XDP provides a mechanism to process network packets earlier and faster than is possible through the kernel’s native network stack. In this post, we’ll discuss.
OpenAI is an AI research and development company whose products include the GPT family of large language models. Since the introduction of GPT-3 in 2020, these models’ fluent and adaptable processing of both natural language and code has propelled their rapid adoption across diverse fields. GPT-4, ChatGPT, and InstructGPT are now used extensively in software development, content creation, and more.
At Datadog, we’re constantly developing new integrations to give you complete, end-to-end visibility into your entire system. Here are just a few of our latest releases.
Effective network troubleshooting requires collecting and correlating thousands of data points across your entire stack. The more data you ingest, however, the more data you have to search through in order to locate important signals. This can make it hard to find the information you need during time-sensitive investigations.
To get visibility into highly distributed applications, organizations often use various tracing tools that are best suited to each individual service owner’s specifications. However, when a request travels between services that have been instrumented with different tools, the trace data may be formatted differently, resulting in broken traces.
