Datadog

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Introducing Metrics from Logs and Log Rehydration

As your application grows in size and complexity, it becomes increasingly difficult to manage the number of logs it generates and the cost of ingesting, processing, and analyzing them. Organizations often have little control over fluctuations in the volume of logs generated—and the resulting costs of collecting them—so they are forced to limit the number of logs generated by their applications, or to pre-filter logs before sending them to their log management platform.

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Introducing Datadog Network Performance Monitoring

Network Performance Monitoring is currently available in private beta. Request access here. Your applications and infrastructure components rely on one another in an increasingly complex fabric, regardless of whether you run a monolithic application or microservices, and whether you deploy to cloud infrastructure, private data centers, or both.

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Dash 2019: Guide to Datadog's newest announcements

At Dash 2019, we are excited to share a number of new products and features on the Datadog platform. With the addition of Network Performance Monitoring, Real User Monitoring, support for collecting browser logs, and single-pane-of-glass visibility for serverless environments, Datadog now provides even broader coverage of the modern application stack, from frontend to backend.

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Signal Sciences brings real-time web attack visibility to Datadog

Signal Sciences is proud to announce our integration with the Datadog platform. This integration furthers our mission of producing the leading application security offering that empowers operations and development teams to proactively see and respond to web attacks—wherever and however they deploy their apps, APIs, and microservices.

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Automate workflows with Datadog's Amazon EventBridge integration

Amazon EventBridge is a serverless event bus that routes real-time data streams from your applications and services to targets like AWS Lambda. EventBridge facilitates event-driven application development by simplifying the process of ingesting and delivering events across your application architecture, and by providing built-in security and error handling. We are excited to announce that you can now use our new integration to route Datadog alerts to EventBridge with minimal configuration or setup.

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Cross-tenant monitoring with Azure Lighthouse and Datadog

Azure Lighthouse is a new feature that provides improved access management for users and applications across different Azure tenants. With Azure Lighthouse, managed service providers (MSPs) can manage their customers’ environments more easily and efficiently than ever before. Datadog is proud to announce support for Azure Lighthouse, which ensures that MSPs can implement a streamlined, scalable approach to monitoring their customers’ Azure environments.

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How to use ApacheBench for web server performance testing

When developing web services and tuning the infrastructure that runs them, you’ll want to make sure that they handle requests quickly enough, and at a high enough volume, to meet your requirements. ApacheBench (ab) is a benchmarking tool that measures the performance of a web server by inundating it with HTTP requests and recording metrics for latency and success.

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Kubernetes Control Plane monitoring with Datadog

In a Kubernetes cluster, the machines are divided into two main groups: worker nodes and master nodes. Worker nodes run your pods and the applications within them, whereas the master node runs the Kubernetes Control Plane, which is responsible for the management of the worker nodes. The Control Plane makes scheduling decisions, monitors the cluster, and implements changes to get the cluster to a desired state.

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Lessons learned from running Kafka at Datadog

At Datadog, we operate 40+ Kafka and ZooKeeper clusters that process trillions of datapoints across multiple infrastructure platforms, data centers, and regions every day. Over the course of operating and scaling these clusters to support increasingly diverse and demanding workloads, we’ve learned a lot about Kafka—and what happens when its default behavior doesn’t align with expectations.