When you’re running databases at scale, finding performance bottlenecks can often feel like looking for a needle in a haystack. In any troubleshooting scenario, you need to know the exact state of your database at the onset of an issue, as well as its behavior leading up to it.
Black Hat USA is one of the industry’s oldest and most well-established security events. Last year, the conference was held virtually for the first time in its history. This year’s conference brought together the best of both worlds, with a hybrid event that was held virtually and in person in Las Vegas. Historically, Black Hat has seen about 20,000 attendees at its in-person conference.
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.
