Operations | Monitoring | ITSM | DevOps | Cloud

May 2021

Announcing support for Oracle Arm-based Ampere A1 instances

Arm processors have long been at the center of mobile computing, powering billions of smartphones, tablets, smartwatches, and other IoT devices. Today, these processors are beginning to see broader adoption in the cloud as they promise better performance, higher energy efficiency, and lower costs than their x86-based predecessors. Just this week, Oracle announced its new Oracle Cloud Infrastructure Ampere A1 Compute platform, built on the Ampere Altra Arm processor.

Announcing support for Amazon ECS Anywhere

Amazon Elastic Container Service (ECS) is a managed compute platform for containers that was designed to be simple to configure, with opinionated defaults to help users get started quickly. ECS customers can run containerized workloads on either Amazon EC2 instances or the serverless Fargate platform without having to maintain a control plane—and can easily integrate ECS with other AWS resources, like Network Load Balancers, to architect their infrastructure.

Introducing Datadog's Lambda extension

AWS Lambda extensions enable you to seamlessly integrate third-party tooling with your Lambda environment so you can run custom code or monitoring agents alongside your functions. We’ve partnered with AWS to create a Lambda extension that offers a more cost-effective, simplified process for collecting data from your functions.

Use the improved infrastructure list to track your hosts' health

Datadog’s infrastructure list provides a central, high-level view of every host in your environment and pulls together metadata and relevant metrics from across Datadog to help you get the full picture of each one. You can easily filter and sort the list using any host tags, letting you quickly view the status of the parts of your infrastructure you need.

Create powerful data visualizations with the new Datadog dashboards experience

Dashboards are a crucial tool in your monitoring arsenal, as they allow you to visualize and correlate telemetry data from across your stack in a single place. Historically, Datadog offered two dashboard types: Screenboards, for pixel-level control on a canvas, and Timeboards, for troubleshooting a specific point in time. Now, we’re excited to introduce a new dashboard layout that combines the best of Timeboards and Screenboards in a single, seamless editing experience.

How to debug Kubernetes Pending pods and scheduling failures

When Kubernetes launches and schedules workloads in your cluster, such as during an update or scaling event, you can expect to see short-lived spikes in the number of Pending pods. As long as your cluster has sufficient resources, Pending pods usually transition to Running status on their own as the Kubernetes scheduler assigns them to suitable nodes. However, in some scenarios, Pending pods will fail to get scheduled until you fix the underlying problem.

Use Datadog's Notebooks API to programmatically manage your notebooks

Datadog Notebooks simplify the way teams across an organization find and share knowledge. By bringing together live data and rich Markdown text, Notebooks help teams create powerful, data-driven documents—from runbooks and support playbooks to incident postmortems and data reports. And with collaboration functionalities like real-time editing and commenting, team members can simultaneously make changes to a document and gather feedback along the way.

Datadog Synthetic Monitoring now supports cross-browser testing

Your users access your application from a wide range of browsers, which have their own implementations of HTML, CSS, and JavaScript. For instance, many modern JavaScript features such as Promises and Arrow Functions are unsupported by some browsers. These inconsistencies can lead to missing elements and malfunctioning workflows that affect some—but not all—of your user base.

Monitor AWS App Runner with Datadog

Knowing how to deploy and run applications has become a key part of modern app development, meaning that developers need expertise in a number of areas beyond their core application code. Whether it’s container orchestration, networking, scaling, or load balancing, there is a steep learning curve to being able to deploy and run an application at scale.

Monitor JMeter test results with Datadog

Apache JMeter is an open source tool for load testing Java applications in both development and CI environments in order to ensure that sudden spikes in traffic won’t cause latency in production. But because load testing involves sending thousands of requests per minute in order to simulate real traffic, it can be difficult to parse outcomes and read patterns—especially for large organizations that test and deploy new code several times a day.

Correlate software performance and resource consumption with new saved views in Live Processes

Your applications rely on third-party software running throughout your infrastructure, and it can be challenging to monitor each of these technologies individually. To give you the visibility you need, Datadog Live Processes now monitors all of your third-party workloads in one place.

Add Datadog monitoring to your Retool apps

The more tools that your teams need to execute their workflows, the more friction and lost productivity there can be, especially if each tool requires a different CLI or set of APIs. Retool is a low-code platform that allows you to build internal web applications using a drag-and-drop interface. By integrating with a number of key backend databases and APIs, Retool enables you to create custom, centralized management tools to serve a wide range of employee-facing use cases.

Best practices for monitoring dark launches

A dark launch is a deployment strategy for testing new versions of a service in production. When running a dark launch, you deploy a new version of a service and route a copy of production traffic to it without returning responses to users. This lets you see how a new version of a service handles production load, watch for errors, and compare performance between the old and the new versions—without affecting users.

Monitor Cloudflare logs and metrics with Datadog

Cloudflare is a content delivery network (CDN) that organizations across industries use to secure the reliability of their websites, applications, and APIs. With a wide array of security, networking, and performance-management tools, millions of web applications employ Cloudflare’s DDoS protection, load balancing, and serverless compute-monitoring features to maintain high performance and uptime.

Speed up your dashboard workflow with dynamic template variable syntax

Template variables enable you to use tags to filter your Datadog dashboards to the hosts, containers, or services you need for faster troubleshooting. However, there are some cases where it may be difficult to use a standard set of template variables to aggregate all of the data you need without creating a complicated, difficult to manage set of variables. For example, you may use tag values that are a subset of another tag.

Best practices for modern frontend monitoring

Single-page applications (SPAs) provide some significant benefits over multiple-page apps. For JavaScript developers using frameworks like React or Vue, they offer flexibility in moving application logic to the frontend, reducing the need for complex backend operations. For users, SPAs can provide a smooth experience with a highly interactive UI and fewer page loads. But, with increased sophistication, there are some tradeoffs.

Monitor kube-state-metrics v2.0 with Datadog

In order to manage complex containerized applications, modern devops teams need to have deep visibility into the status of their Kubernetes resources. By listening directly to the Kubernetes API, the open source kube-state-metrics service generates key metrics about your Kubernetes objects, including pods, nodes, and deployments, which are essential for understanding the status and performance of your clusters.

Monitor your Google serverless applications with Datadog

Google Cloud Platform is growing quickly, providing solutions for everything from cloud storage to managed Kubernetes to serverless computing. Since Google App Engine launched in 2008, Google’s suite of serverless products has expanded to help enterprises accelerate application development without having to manage or scale their own infrastructure.

Detect application abuse and fraud with Datadog

Protecting your applications from abuse of functionality requires understanding which application features and workflows may be misused as well as the ability to quickly identify potential threats to your services. This visibility is particularly critical in cases where an adversary finds and exploits a vulnerability—such as inadequate authentication controls—to commit fraud.

Automatically create and manage Kubernetes alerts with Datadog

Kubernetes enables teams to deploy and manage their own services, but this can lead to gaps in visibility as different teams create systems with varying configurations and resources. Without an established method for provisioning infrastructure, keeping track of these services becomes more challenging. Implementing infrastructure as code solves this problem by optimizing the process for provisioning and updating production-ready resources.

Datadog on Security and Compliance

At Datadog, customer trust and data security are of the utmost importance. As a high growth company, navigating the tradeoffs of security and development agility are especially critical. Our customers expect us to continually improve our platform, while providing a compliant, secure environment for their most critical data. Balance is key to rolling out features rapidly and keeping systems secure.

Datadog Live Containers - Kubernetes Resources

Datadog Live Containers provides multidimensional, real-time visibility into Kubernetes workloads, from Deployments and ReplicaSets down to individual Containers. Using Datadog's curated metrics, teams can track the health and performance of their Kubernetes resources in the appropriate context and surface critical information about every layer of their Cluster.

Datadog Application Performance Monitoring (APM)

Datadog APM provides end-to-end application monitoring, from frontend browsers to backend database queries and code profiles, so you can monitor and optimize your stack at any scale—no sampling required. APM and distributed tracing are fully integrated with the rest of Datadog, giving you rich context for troubleshooting issues in real time.