Operations | Monitoring | ITSM | DevOps | Cloud

Introducing template variable saved views for dashboards

Datadog dashboards provide immediate visibility and insight into your environments. Setting template variables enables you to filter your dashboard graphs on the fly to visualize specific sets of tagged objects. Now, with saved views, you can save sets of frequently used template variables in order to easily find the data you most care about with just a few clicks.

How to implement log management policies with your teams

Logs are an invaluable source of information, as they provide insights into the severity and possible root causes of problems in your system. But it can be hard to get the right level of visibility from your logs while keeping costs to a minimum. Systems that process large volumes of logs consume more resources and therefore make up a higher percentage of your overall monitoring budget. Further, log throughput can be highly variable, creating unexpected resource usage and financial costs.

Introducing the Datadog Operator for Kubernetes and OpenShift

As more environments run on Kubernetes—including our own— Datadog has been making it easier to get visibility into clusters of any scale. To minimize load on the Kubernetes API server, the Datadog Agent runs in two different modes. The node-based Agent queries local containers or external endpoints for data, while the Cluster Agent fetches cluster-level metadata from the API server.

Monitor ProxySQL with Datadog

ProxySQL is a MySQL/MariaDB protocol–compliant load balancer and reverse proxy with native support for a range of popular backends including ClickHouse, Amazon Aurora, and Amazon RDS. ProxySQL efficiently distributes queries to your database servers and caches results, improving resource management and boosting database performance. You can also configure ProxySQL for high availability to reduce downtime.

Monitor Sidekiq with Datadog

Sidekiq is a Ruby framework for background job processing. Developers can use Sidekiq to asynchronously run computationally intensive tasks—such as bulk email sending, payment processing, and data importing—to help speed up the response times of their applications. If you’re using Sidekiq Pro or Enterprise, Datadog’s integration helps you monitor the progress of your jobs and the applications that depend on them, all in a single platform.

Monitor Windows containers on Google Cloud with Datadog

Many organizations already use Docker to containerize their Windows applications and often run mixed Windows and Linux container environments to support complex architectures. With Kubernetes’s support for deploying clusters with Windows nodes, organizations can leverage the orchestration platform to easily automate container provisioning, networking, scaling, and more for their Windows applications.

Identifying EC2 Right Sizing Opportunities for Cost Optimization | Datadog Tips & Tricks

In this video, you’ll learn how to identify right sizing opportunities for your EC2 instances utilizing Datadog metric dashboards. Optimizing your cloud footprint for cost efficiency can be a huge task, especially for large and scaling environments. Utilizing time series data and toplists, Datadog dashboards allow you to see chronically underutilized EC2s in your AWS environment. Template variables allow you to sort EC2s by teams and instance types, so you quickly identify the scope of cost saving opportunities across your organization.

Monitor Confluent Platform with Datadog

Confluent Platform is an event streaming platform built on Apache Kafka. If you’re using Kafka as a data pipeline between microservices, Confluent Platform makes it easy to copy data into and out of Kafka, validate the data, and replicate entire Kafka topics. We’ve partnered with Confluent to create a new Confluent Platform integration.

NodeJS Instrumentation - Creating Custom Spans for Method-Level Visibility | Datadog Tips & Tricks

In part 2 of this 4 part series, you’ll learn how to instrument your NodeJS application to capture custom method-level spans, allowing visibility into how specific methods behave in your application. Flame graphs allow for deep insight into the performance of your code. During instrumentation, we can capture custom spans for deeper layers of visibility in the resulting flame graphs. In this video, we use instrumentation to capture a method-level span, allowing us to see the performance of that specific method in our flame graphs in the Datadog UI.