IBM MQ is enterprise-grade message-oriented middleware (MOM). Previously known as MQSeries and renamed to WebSphere MQ, IBM MQ is known for its stability and reliability. Companies in industries ranging from financial services to retail to aviation use it as an integral part of their backend infrastructure. Datadog’s new IBM MQ integration enables users to collect key metrics and logs from their IBM MQ instances and visualize them with a customizable out-of-the-box dashboard.

Amazon DocumentDB is an AWS-managed document database service that supports MongoDB, the well known open source database. As a managed service, AWS automatically handles database management tasks, autoscales database clusters, and backs up your data to S3. DocumentDB implements the Apache 2.0 open source MongoDB 3.6 API, making it easy to migrate your existing MongoDB workloads to DocumentDB.

As application architecture moves from monoliths to microservices, observability has become a growing challenge. The services that make up a distributed application, and the many dependencies and communication pathways between them, are difficult to govern and observe. You can get more control and visibility of your application by including a service mesh—a layer of infrastructure that manages traffic among microservices.

Datadog is pleased to announce the availability of Synthetics, a whole new layer of visibility on the Datadog platform. By monitoring your applications and API endpoints via simulated user requests, Synthetics helps you ensure uptime, identify regional issues, track application performance, and manage your SLAs and SLOs. By unifying Synthetics with your metrics, traces, and logs, Datadog allows you to observe how all your systems are performing as experienced by your users.

Since its release in 1995, PHP has been one of the most popular server-side languages for building web applications. It supports a wide range of web servers, databases, and operating systems. PHP developers use popular frameworks like Laravel, Symfony, and Zend to deploy and manage sites that serve high volumes of traffic. To help you monitor PHP performance, identify bottlenecks, and optimize your users’ experience, we’re pleased to announce APM & distributed tracing for PHP.

C# logs can be an invaluable resource for optimizing application performance and debugging errors. But it’s not easy to extract the full potential of your logs if they’re not providing enough context around each error, or if they’re written in a format that’s difficult to parse.

With the growing number of customers monitoring Azure in Datadog, we saw a great opportunity to help our users get faster visibility across their dynamic cloud environments. We’re happy to announce the release of new out-of-the-box dashboards for several Azure services.

Amazon MQ is a cloud-based, AWS-managed service for the popular Apache ActiveMQ message broker. It offers many advantages of being part of the AWS ecosystem as AWS automatically provisions and maintains infrastructure components from services including EC2 and EBS.

As we explained in Part 1, it’s important to monitor task status and resource use at the level of ECS constructs like clusters and services, while also paying attention to what’s taking place within each host or container. In this post, we’ll show you how Datadog can help you: Automatically collect metrics from every layer of your ECS deployment, Track data from your ECS cluster, plus its hosts and running services in dashboards, and more.

Amazon Elastic Container Service (ECS) is an orchestration service for Docker containers running within the Amazon Web Services (AWS) cloud. You can declare the components of a container-based infrastructure, and ECS will deploy, maintain, and remove those components automatically. The resulting ECS cluster lends itself to a microservice architecture where containers are scaled and scheduled based on need.