Serverless event-driven architectures are composed of AWS Lambda functions that regularly interact with databases, APIs, message queues, and other resources to facilitate complex workflows and functionalities. It is therefore crucial to monitor every component of your stack to ensure your applications perform optimally at scale. But traditionally, telemetry data for AWS resources has lived in silos, making it difficult to quickly get the context you need to debug issues.

Containers are lightweight, portable, easily scalable, and enable you to run multiple workloads on the same host efficiently, particularly when using an orchestration platform like Kubernetes or Amazon ECS. But containers also introduce monitoring challenges. Containerized environments may comprise vast webs of distributed endpoints and dependencies that rely on complex network communication.

Amazon Elastic File System (EFS) provides shared, persistent, and elastic storage in the AWS cloud. Like Amazon S3, EFS is a highly available managed service that scales with your storage needs, and it also enables you to mount a file system to an EC2 instance, similar to Amazon Elastic Block Store (EBS).

In Part 1 of this series, we looked at EFS metrics from several different categories—storage, latency, I/O, throughput, and client connections. In this post, we’ll show you how you can collect those metrics—as well as EFS logs—using built-in and external tools.

In Part 1 of this series, we looked at the key EFS metrics you should monitor, and in Part 2 we showed you how you can use tools from AWS and Linux to collect and alert on EFS metrics and logs. Monitoring EFS in isolation, however, can lead to visibility gaps as you try to understand the full context of your application’s health and performance.

From containerized workloads to microservice architectures, developers are rapidly adopting new technology to scale their products at unprecedented rates. To manage these complex deployments, many teams are increasingly moving their applications to third party–managed services and infrastructure, trading full-stack visibility for simplified operations.

When you migrate workloads from on-premise infrastructure into a public cloud, you can improve the performance, reliability, and security of your application, and you might also lower your costs.

The ASP.NET Core framework enables you to build and deploy .NET applications on a wide variety of platforms, each of which has different observability concerns. In a previous post, we looked at monitoring a containerized ASP.NET Core application. In this guide, we’ll show how Datadog provides visibility into ASP.NET Core applications running on AWS Fargate. We’ll walk through.

Amazon FSx for Windows File Server is a fully managed file storage service built on Windows Server. Migrating on-premise Windows file systems to a managed service like FSx enables organizations to reduce operational overhead and take advantage of the flexibility and scalability of the cloud. But having visibility into file access activity across their environment is key for security and compliance requirements, particularly in sectors such as financial services and healthcare.

When developing large, customer-facing applications, it’s paramount to have visibility into real user behavior in order to optimize your UX. Without a direct view into what users are actually doing when navigating your app, it can be difficult to reproduce bugs and understand how aspects of your frontend design are causing user frustration and churn. With Datadog RUM’s Session Replay feature, currently available in beta, you can watch individual user sessions using a video-like interface.