When monitoring applications with streaming data pipelines, there are additional complexities to consider that are not present in traditional batch-processing systems. Whether you’re using streaming data pipelines to power a digital trading platform, capture sensor data from an IoT device, or recommend news articles to users, it can be challenging to identify the root cause of delays when you’re dealing with distributed systems, real-time data, and the dynamic nature of events.

Azure Kubernetes Service (AKS) enables you to easily deploy and manage containerized applications in Azure while leveraging Microsoft resources such as development tools, security features, and more. As with any Kubernetes service, the sheer volume of containers being orchestrated makes monitoring AKS cluster health challenging, which can slow response times to critical incidents and create bottlenecks around long-term optimizations.

BigQuery is Google Cloud Platform’s fully managed serverless data warehouse. It enables data analysis and storage at petabyte scale while eliminating the overhead of managing infrastructure. As a managed service, BigQuery autoscales and provisions compute resources and storage as needed, helping you reduce the overhead of managing infrastructure but also reducing your visibility into performance. And BigQuery users face other challenges when it comes to visibility.

Datadog Database Monitoring (DBM), which provides host-level and query performance metrics and insights for PostgreSQL, MySQL, and SQL Server, is now available for Oracle. Oracle is one of the most common database types, and now teams that operate Oracle databases can use Datadog to monitor these resources alongside telemetry from across their environments.

Modern-day engineering teams rely on continuous integration and continuous delivery (CI/CD) providers, such as GitHub Actions, GitLab, and Jenkins to build automated pipelines and testing tools that enable them to commit and deploy application code faster and more frequently.

Zendesk provides support teams with an integrated solution for processing all types of customer inquiries and feedback. But as organizations scale, support tickets multiply, making it increasingly difficult to parse all of your customers’ feedback and time-consuming to investigate issues. Customers often report issues without providing the detailed context needed for troubleshooting, creating unclear and indirect paths to remediation.

In Java applications, concurrency issues can be difficult to reproduce and debug. Because work is scheduled nondeterministically across threads, the conditions that have led to an error in one execution of the program may not trigger the same issue the next time around. Exceptions that are silently handled—also known as swallowed exceptions—can also be challenging to debug because they typically do not leave any trace in the logs.

Datadog Workflow Automation speeds up incident response and remediation for DevOps, SRE, and security teams by enabling them to automatically run predefined task sequences whenever specific alerts or security signals are triggered. After the feature’s initial release in 2023, Datadog is now excited to announce a significant expansion of its Workflow Automation capabilities with Azure actions, allowing engineers to create automated workflows for their Azure resources for the first time.

Your applications are only as powerful as they are iterable. To keep up with their rapidly changing production environments, your teams need reliable CI/CD systems that implement best practices—including build and test automation, flaky test management, and deployment management. By optimizing their CI/CD pipelines, your teams can build their apps more efficiently, deploy them more safely, and catch bugs and security vulnerabilities before they make it to production.

Large-scale organizations typically collect and manage millions of logs a day from various services. Within these orgs, many different teams may set up processing pipelines to modify and enrich logs for security monitoring, compliance audits, and DevOps. Datadog Log Pipeline let you ingest logs from your entire stack, parse and enrich them with contextual information, add tags for usage attribution, generate metrics, and quickly identify log anomalies.