When building serverless applications on AWS Lambda, Amazon CloudWatch provides out-of-the-box metrics that measure the performance, errors, and duration of your functions. Although these standard Lambda metrics provide visibility into your serverless applications, it can also be invaluable to monitor custom metrics that are unique to your use case and application.
Service level objectives (SLOs) state your team’s goals for maintaining the reliability of your services. Adopting SLOs is an SRE best practice because it can help you ensure that your services perform well and consistently deliver value to users. But to gain the greatest benefit from your SLOs, you need ongoing visibility into how well your services are performing relative to your objectives.
Azure Functions is an on-demand serverless compute offering built on top of Azure App Service that enables you to deploy event-driven code without the need to provision and manage infrastructure. Because applications rely on Azure Functions to handle business-critical tasks such as processing orders or logging in users, it’s important to ensure that your functions respond quickly when they’re invoked.
Whether you run an ecommerce site, a digital publication, or any other customer-facing service, delivering optimum user experiences is key to the success of your business. Customers can grow frustrated and abandon your site when they run into hurdles such as JavaScript errors or confusing page designs, and that frustration negatively impacts your company’s bottom line.
When you’re operating databases at scale, being able to get real-time insights across all your databases is essential for addressing issues and identifying areas for optimization. Datadog Database Monitoring’s Database List allows you to monitor your entire database fleet in one place, so you can quickly identify and troubleshoot overloaded hosts and gauge the impact of problematic queries throughout your infrastructure.
AWS’s new Graviton3 EC2 instances are built on its third generation of custom Arm-powered processors. These instances promise up to 25 percent better performance over Graviton2 for compute-intensive workloads. This means that, for applications like distributed data analytics, machine learning, video encoding, gaming, and more, migrating to Graviton3 instances can provide better performance, cost savings, and more energy efficiency.
The Internet of Things (IoT) can be found in a diverse range of devices, including fleets of autonomous vehicles, automobiles, planes, electric charging stations, and voice controllers. These devices are embedded with gateways, electronics, actuators, platform hubs, and cloud-service connectivity, enabling them to exchange data across the physical, network, and application layers that constitute IoT architecture.
Datadog Database Monitoring (DBM) provides comprehensive visibility into SQL queries running on your databases. Using DBM, you can troubleshoot database performance issues by drilling into frequently used queries and analyzing historical trends in your queries’ metrics and execution plans. Whether you operate self-hosted SQL Server instances or leverage Azure’s fully managed services, DBM can provide deep visibility into the databases your application depends on.
We’re pleased to announce a new out-of-the-box dashboard for Azure Kubernetes Service (AKS) that allows you to immediately visualize the health and performance of your AKS clusters. This dashboard organizes and highlights the most critical information from the standard AKS metrics, while also incorporating log data to provide observability into the control plane.
Many organizations manage applications that are supported by a large number of services in multiple environments, ranging from the cloud to their own data centers across the globe. As these organizations scale and accelerate service adoption, the volume of telemetry data in their environments multiplies every year.
