Operations | Monitoring | ITSM | DevOps | Cloud

May 2020

Best practices for monitoring GCP audit logs

Google Cloud Platform (GCP) is a suite of cloud computing services for deploying, managing, and monitoring applications. A critical part of deploying reliable applications is securing your infrastructure. Google Cloud Audit Logs record the who, where, and when for activity within your environment, providing a breadcrumb trail that administrators can use to monitor access and detect potential threats across your resources (e.g., storage buckets, databases, service accounts, virtual machines).

Enhanced Azure monitoring with Datadog

Microsoft Azure is a cloud computing platform for building, deploying, and managing global-scale applications. With a wide range of offerings, including dozens of different services, Azure provides tools for users to create large and sophisticated systems for hosting any type of workload. But with the huge number of configuration options and resource types, understanding the health and performance of your applications in Azure can be challenging.

Introduction to Site Reliability Engineering

In this session, we start with the basics of SRE, including some common terminology and theory, then dive into practical examples—including lessons learned from our own journey here at Datadog. We discuss the relationship between SRE and DevOps, what success looks like (and how to measure it), and how to identify and nurture both internal and external talent in order to build a cross-functional team. SRE is a large, complex topic, so the session ends with a live Q&A and deep-dive into some great topics.

How to Create a Graph Using Tags and Time Aggregation | Datadog Tips & Tricks

In this video, you’ll learn how to use tag-based grouping and time aggregation (with the rollup function) to create actionable time-series graphs. Datadog offers various ways to manipulate your metric graphs so that you can create graphs that are specific and actionable for all of your use cases. Two methods of doing this—as explored in this video—are tag-based grouping and time aggregation.

How To Monitor Containers in Real-Time with Datadog Live Containers | Datadog Tips & Tricks

In this video, you’ll learn how to utilize Datadog’s Live Container View to monitor and troubleshoot container performance underlying your applications. Datadog makes it easy to monitor ephemeral, containerized infrastructure. In this video you’ll learn how to leverage Datadog’s Live Container View to effectively dive into your container health. Using this view, you can sort and group your containers by tags or labels imported from Kubernetes, such as container name.

How to Import Kubernetes Labels as Tags | Datadog Tips & Tricks

In this video, you’ll learn how to turn Kubernetes node labels and pod labels into tags in Datadog in order to correlate metrics, traces, and logs back to Kubernetes deployments. Using labels for Kubernetes objects—such as pods or nodes—is key to organizing and making sense of your deployments. Datadog can automatically bring your Kubernetes labels from your clusters into the Datadog platform as tags, regardless of whether you’re using on-prem Kubernetes or a cloud-based service such as AKS, EKS, or GKE.

How to Use Browser Tests to Monitor Web App User Journeys | Datadog Tips & Tricks

In part 2 of this 2 part series, you’ll learn how to create Datadog Browser Tests to replicate user journeys and verify both that your web applications are responsive and functioning properly at all times. In part 1 of this series (link), you learned how Datadog’s API tests can be used to check API and website uptime. Datadog Browser Tests take this a step further, allowing you to replicate entire user journeys and transactions through your web applications. This is done with our browser recorder: simply click “Start Recording” and click through your application to record a test.

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.