Datadog Synthetic private locations play a key role in your organization’s test infrastructure by serving as highly customizable points of presence (e.g., data centers, geographic locations) for running synthetic tests on internal services. You can deploy private locations using the orchestrator of your choice, enabling you to seamlessly roll them out and scale them with the rest of your container fleet.
Confluent Cloud is a fully managed, cloud-hosted streaming data service. Enterprise customers use Confluent Cloud for real-time event streaming within cloud-scale applications. We’re excited to announce a new integration between Datadog and Confluent Cloud, which enables users to get deep visibility into their Confluent Cloud environment with just a few clicks. In this post, we’ll introduce how to set up the integration and start monitoring key metrics from your clusters.
After you release an Android application, you need to ensure a smooth, engaging experience for users. Poor performance and heavy resource consumption can cause your application to rank lower for prospective users in the Google Play Store, and existing users can become frustrated and even uninstall your application. All of this can spell trouble for business-related performance indicators like engagement and discoverability.
Since its release in 2013, React.js has become one of the most popular solutions for building dynamic, highly interactive web app frontends. React’s declarative nature and component-based architecture make it easier to build and maintain single-page applications.
Tracing has become essential for monitoring today’s increasingly distributed architectures. But complex production applications produce an extremely high volume of traces, which are prohibitively expensive to store and nearly impossible to sift through in time-sensitive situations. Most traditional tracing solutions address these operational challenges by making sampling decisions before a request even begins its path through your system (i.e., head-based sampling).
Datadog Synthetic Monitoring allows you to proactively monitor your applications so that you can detect, troubleshoot, and resolve any availability or performance issues before they impact your end users. With our API test suite, you can send simulated HTTP requests to your API endpoints, check the validity of SSL certificates, verify the performance and correctness of DNS resolutions, test TCP connections, and ping endpoints to detect server connectivity issues.
Azure Government is a dedicated cloud for public sector organizations that want to leverage Azure’s suite of services in their highly regulated environments. As these organizations migrate their applications to Azure Government, they need to ensure that they can maintain visibility into the status and health of their entire infrastructure.
At Datadog, we believe that having visibility into production is crucial to building better software, especially as modern environments become more and more complex. Bugs that occur in production are often difficult to reproduce locally, which leaves developers guessing about what went wrong. To solve this problem, teams need the same depth of visibility into their production environments as they do into their local environments.
Within distributed applications, data moves across many loosely connected endpoints, microservices, and teams, making it difficult to know when services are storing—or inadvertently leaking—sensitive data. This is especially true for governance, risk management, and compliance (GRC) or other security teams working for enterprises in highly regulated industries, such as healthcare, banking, insurance, and financial services.
Sending custom metrics to Datadog allows you to monitor important data specific to your business and applications, such as latency, dollars per customer, items bought, or trips taken. And tags are key to being able to slice and dice these custom metrics to quickly find the information you need. But collecting enough custom metrics to have complete visibility can be cost prohibitive. For example, you might run microservices instrumented across thousands of containers.
