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.

With thousands of logs generated every minute from your infrastructure, applications, services, and devices, retaining this copious amount of data for active search and analysis can be cost-prohibitive. Because log volumes continue to grow rapidly as operations scale, it’s common for organizations to implement log management strategies and store only a limited number to minimize costs.