Datadog is a monitoring and analytics platform that ingests trillions of data points per day, coming from more than 8,000 customers. Each of those is associated with metadata, mostly in the form of tags, and it can also be part of streams of related data points, which can then be explored, queried, or aggregated. RocksDB is used by many services at Datadog that are part of that metrics ingestion, aggregation, query, and index pipeline.

Terraform allows you to efficiently manage complex infrastructure environments, and Datadog is an important piece of those environments. With the Datadog provider, you can use Terraform to manage your Datadog resources as code, allowing you to create and edit resources with the same tool you’re already using for your infrastructure. This video will show you how to do just that through the example of creating a Datadog monitor.

In this video, you’ll learn how to create unified dashboards to enable your teams with valuable information and performance visualizations from across the Datadog platform. Dashboards allow your teams to see all data from across the Datadog platform side-by-side, enabling holistic visibility and breaking down silos between Dev and Ops teams. In this video, you’ll learn how to create a Screenboard, showcasing data such as frontend system latency, backend system latency, and Service Level Objectives all in one place.

You use CDNs because they allow you to serve content as quickly and reliably as possible. But how well are your systems performing? How securely are you moving data—and how do you know which parts of your environment are slowing you down? Learn how to improve end user experiences, accelerate development, and take full advantage of edge computing in this joint webinar.

In this webinar, Ara Pulido, Technical Evangelist at Datadog, will demonstrate how to autoscale your application workloads on OpenShift. You will learn frameworks for how to identify their key work and resource metrics; as well as how to use them to drive horizontal and vertical pod autoscaling so that you can maximize efficiency, while ensuring service reliability.

Datadog APM provides deep visibility into application performance and code efficiency, so you can monitor and optimize your stack at any scale and provide the best digital experience for your users. APM and distributed tracing are fully integrated with the rest of Datadog, giving you rich context for troubleshooting issues in real time.

In part 2 of this 2 part series, you’ll learn how to use Log Patterns to quickly create log exclusion filters and reduce the number of low-value logs you are indexing. Datadog’s Logging with Limits™ feature allows you to selectively determine which logs to index after ingesting all of your logs. Meanwhile, the Log Patterns feature can quickly isolate groups of low-value logs.

In this video, you’ll learn how to generate metrics using log events attributes to filter your logs more effectively and begin monitoring, graphing and alerting on the new metric immediately. Generating metrics from logs is a powerful tool for monitoring attributes which are parsed from your logs.

When 2 years ago Datadog decided to move its infrastructure platform to Kubernetes we didn’t expect to find so many roadblocks, but ingesting trillions of datapoints per day in a reliable fashion requires pushing the limits of cloud computing. Creating and managing dozens of clusters, with thousands of nodes each and operating in several clouds was a challenging but rewarding learning experience. In this episode Ara Pulido, Developer Advocate, will chat with Laurent Bernaille, Staff Engineer at Datadog and part of the team that created Datadog’s Kubernetes platform. We’ll cover the challenges we found creating and scaling Datadog’s Kubernetes platform and how we overcame them.