Operations | Monitoring | ITSM | DevOps | Cloud

August 2022

Equip any user to monitor Kubernetes with the Overview Page

Many organizations use Kubernetes to orchestrate their containerized applications. But because Kubernetes is complex, application developers may take some time to ramp up on the intricacies of monitoring a Kubernetes environment. This means that teams often need to create internal documentation and offer hands-on training to bridge the knowledge gap.

Monitor your Microsoft Azure VMs featuring Ampere Altra Arm-based CPUs with Datadog

As organizations continue to expand their cloud footprint, managing costs without risking application performance is a priority. Because of this, Arm processors have become popular for their efficient, cost-effective processing power. Microsoft Azure’s new series of Azure Virtual Machines are powered by Ampere Altra Arm-based processors, which provide excellent price performance for scale-out and cloud-native workloads.

Monitor Akamai Datastream 2 with Datadog

Akamai is one of the world’s largest CDN solution providers, helping companies greatly accelerate the secure delivery of content to their users all across the globe. Akamai provides this content delivery through its Intelligent Edge Platform, which is made up of hundreds of thousands of edge servers distributed around the planet.

Monitor your Edgecast CDN with Datadog

Edgecast is a global network platform that provides a content delivery network (CDN) and other solutions for edge computing, application security, and over-the-top video streaming. Using Edgecast’s JavaScript-based CDN, teams can improve web performance by caching static and dynamic content with low latency and minimal overhead.

Find the root cause faster with Datadog and Zebrium

When troubleshooting an incident, DevOps teams often get bogged down searching for errors and unexpected events in an ever-increasing volume of logs. The painstaking nature of this work can result in teams struggling to resolve issues before new incidents appear, potentially leading to an incident backlog, longer MTTR, and a degraded end-user experience.

Monitor your gRPC APIs with Datadog Synthetic Monitoring

gRPC is an open-source Remote Procedure Call (RPC) framework developed by Google and released in 2016. Although gRPC is still relatively new, large organizations are adopting it in increasing numbers to build APIs that connect complex microservice meshes that use disparate languages and frameworks. gRPC-based APIs can perform requests up to seven times faster than REST APIs and enable customers to easily implement SSL authentication, load balancing, and tracing via plug-in libraries.

Monitor your Dataflow pipelines with Datadog

Dataflow is a fully managed stream and batch processing service from Google Cloud that offers fast and simplified development for data-processing pipelines written using Apache Beam. Dataflow’s serverless approach removes the need to provision or manage the servers that run your applications, letting you focus on programming instead of managing server clusters. Dataflow also has a number of features that enable you to connect to different services.

Manage Service Catalog entries efficiently with the Service Definition JSON Schema

The Datadog Service Catalog helps you centralize knowledge about your organization’s services, giving you a single source of truth to improve collaboration, service governance, and incident response. Datadog automatically detects your APM-instrumented services and writes their metadata to a service definition before adding them to the catalog.

Track your test coverage with Datadog RUM and Synthetic Monitoring

The modern standards of the web demand that user-facing applications be highly usable and satisfying. When deploying frontends, it’s important to implement a comprehensive testing strategy to ensure your customers are getting the best possible user experience. It can be difficult, however, to gauge the effectiveness of your test suite. For instance, all of your tests may be passing, but they might not cover a specific UI element that is crucial to a critical workflow.

Autoscale your Kubernetes workloads with any Datadog metric

Editor’s note: This post was updated on August 9, 2022, to include a demonstration of how to enable highly available support for HPA. It was also updated on November 12, 2020, to include a demonstration of how to autoscale Kubernetes workloads based on custom Datadog queries using the new DatadogMetric CRD.

Monitoring Rails applications with Datadog

Rails is a Ruby framework for developing web applications. It favors the Model-View-Controller (MVC) architecture and includes generators that create the files needed for each MVC component. Rails applications consist of a database, an application server for running application code, and a web server for processing requests. Rails provides multiple integrations for its supporting database (e.g., MySQL and PostgreSQL) and web server (e.g., Apache and NGINX).

How Datadog's Technical Solutions team uses RUM, Session Replay, and Error Tracking to resolve customer issues

Organizations across a wide range of industries share a common goal: deploy stable applications that support their customers’ needs. Many of these organizations rely on the Datadog platform to get complete visibility into the health and performance of their applications, and we understand how important it is that our services are reliable. That’s why we leverage our own products to ensure that the platform works as expected.

Datadog acquires Seekret

APIs are integral to the success of modern enterprises across a wide range of industries, such as finance, logistics, and manufacturing. They not only enable developers to build powerful business solutions by integrating with external applications, but also facilitate communication between internal services. This means that the ability to build reliable, highly-performant APIs—and govern their behavior and performance—is more important than ever.

Expedite infrastructure investigations with Kubernetes Anomalies

Modern Kubernetes environments are becoming increasingly complex. In 2021, Datadog analyzed real-world usage data from more than 1.5 billion containers and found that the average number of pods per organization had doubled over the course of two years. Organizations running containers also tend to deploy more monitors than companies that don’t leverage containers, pointing to the increased need for monitoring in these environments.

Automate incident response workflows with Eventarc and Datadog

Eventarc is a Google Cloud offering that ingests and routes events between GCP products, such as Cloud Run, Cloud Functions, and Pub/Sub, making it easy to build automated, event-driven workflows in complex environments. By taking care of event ingestion, delivery, authorization, and error handling, Eventarc reduces the development overhead that is required to build and maintain these workflows and helps you improve application resilience.

Simplify microservice governance with the Datadog Service Catalog

Moving from a monolith to microservices lets you simplify code deployments, improve the reliability of your applications, and give teams autonomy to work independently in their preferred languages and tooling. But adopting a microservices architecture can bring increased complexity that leads to gaps in your team members’ knowledge about how your services work, what dependencies they have, and which teams own them.

Monitor your GitHub Actions workflows with Datadog CI Visibility

GitHub Actions provides tooling to automate and manage custom CI/CD workflows straight from your repositories, so you can build, test, and deliver application code at high velocity. Using Actions, any webhook can serve as an event trigger, allowing you, for example, to automatically build and test code for each pull request. Datadog CI Visibility now provides end-to-end visibility into your GitHub Actions pipelines, helping you maintain their health and performance.

Datadog on Informed Product Development

Datadog is an observability and security platform. That means that our users may be in a high stress situation: debugging an issue in production, managing an incident or responding to a security threat. Having a good UX is particularly critical in those cases. User interviews are very helpful, but after a product has been released in production we are able to gather a lot more data to understand how customers interact with it and make decisions about how it can be improved.