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.
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.
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.
Datadog Network Performance Monitoring (NPM) gives you visibility into all the communication that takes place between the network components in your environment, including hosts, processes, containers, clusters, zones, regions, and VPCs. As organizations scale, and as their networks grow in complexity, the massive volume of network data to be monitored can become overwhelming. Knowing precisely what network data to surface to resolve issues within these larger environments can be a challenge.
Visibility into the upstream and downstream dependencies of your services is key to maintaining a performant microservices environment. Application developers and SREs rely on this visibility to quickly trace issues back to the source, which is essential during incidents—when time is of the essence—throughout day-to-day operations, and as systems evolve and scale.
The Datadog Service Catalog centralizes your organization’s knowledge about the ownership, reliability, performance, costs, and security of your services. If you’re also using Backstage to keep track of your services, you can leverage our support for Backstage YAML to easily consolidate and maintain all your service information in the Service Catalog.
AJ Stuyvenberg is a Staff Engineer at Datadog and an AWS Serverless Hero. A version of this post was originally published on his blog. In AWS Lambda, a cold start occurs when a function is invoked and an idle, initialized sandbox is not ready to receive the request. Features like Provisioned Concurrency and SnapStart are designed to reduce cold starts by pre-initializing execution environments.
NVIDIA is well known for its computing advancements across a broad range of industries and has become the clear leader in the artificial intelligence (AI) space. Due to their high-performance capabilities, NVIDIA’s discrete graphics processing units (GPUs) now account for approximately 80 percent of the market share for production-level AI, gaming, graphics rendering, and other complex data processing tasks.
