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Datadog

How Delivery Hero uses Kubecost and Datadog to manage Kubernetes costs in the cloud

As the world’s leading local delivery platform, Delivery Hero brings groceries and household goods to customers in more than 70 countries. Their technology stack comprises over 200 services across 20 Kubernetes clusters running on Amazon EKS. This cloud-based, containerized infrastructure enabled them to scale their operation to support increasing demand as the volume of orders placed on their platform doubled during the pandemic.

Optimize Kubernetes workload resourcing with StormForge and Datadog

StormForge Optimize Live is a machine learning-powered performance and resource optimization solution for Kubernetes workloads. Optimize Live ingests and analyzes production observability data and recommends specific actions to optimize CPU and memory utilization. You can take these actions manually or set them to occur automatically, making it easier to maintain a high level of application performance while minimizing cloud costs.

Autonomously optimize AWS Lambda deployments with Sedai and Datadog

In dynamic production environments, unpredictable traffic loads and frequent code changes can make it difficult for organizations to consistently optimize their cloud infrastructure, resulting in application performance issues, latency, and wasted cloud spend. Teams that manage large-scale cloud infrastructure deployments are often forced to tune their workloads’ configurations using a complicated mesh of script jobs—or worse, manual remediation by on-call engineers prompted by alerts.

Optimize SQL Server performance with Datadog Database Monitoring

Microsoft SQL Server is a popular relational database management system that provides a wide range of performance and reliability features (e.g., AlwaysOn availability groups) to support business-critical applications. As your SQL Server workloads scale and increase in complexity, it can be difficult to monitor all of their components and pinpoint the exact issues that are degrading your databases’ performance.

Understand serverless function performance with Cold Start Tracing

Serverless developers are undoubtedly familiar with the challenge of cold starts, which describe spikes in latency caused by new function containers being initialized in response to increasing traffic. Though cold starts are usually rare in production deployments, it’s still important to understand their causes and how to mitigate their impact on your workload.

Ship high-quality, secure code faster with Datadog Code Analysis

As your engineering teams grow and commit code more frequently, it becomes increasingly difficult to release high-quality, secure code while achieving your desired development velocity. To create smoother developer workflows that ensure high standards for code quality and security, it’s critical for developers to detect and remediate issues earlier in the software development lifecycle— without switching tools or contexts.

Monitor code quality in Datadog with SonarQube

SonarQube is a tool for static code analysis that integrates with your existing CI pipelines to run quality checks on your codebase as it changes. As you develop and release new code, constant monitoring of code quality is crucial to ensure compliance, stability, and security. SonarQube’s Clean-As-You-Code philosophy helps to avoid technical debt by running regular code checks and alerting you to any problems early on.

Monitor your Argo CD clusters with Datadog

Argo CD is a declarative continuous delivery tool for Kubernetes developed by the Cloud Native Computing Foundation (CNCF). Argo CD automates your application deployment by continuously monitoring the live state of your containers and comparing it against the desired state in your Kubernetes manifest files, then pulling changes into your Kubernetes clusters as needed.