Streaming data pipelines that use queuing technologies like Kafka, RabbitMQ, and Amazon SQS are pivotal for delivering high-performant, event-driven applications that power real-time data processing and analysis. However, the asynchronous and distributed nature of these applications poses challenges to effectively managing the interdependencies between application services and queues within the streaming data pipeline, as well as to resolving delays before they propagate downstream.In this session, we will explore how Datadog Data Streams Monitoring (DSM) addresses these challenges through automatic topology mapping, preconfigured latency metrics, consumer lag insights, and other troubleshooting tools that are unique in the APM market. You will understand how DSM seamlessly integrates into your Datadog observability workflows, empowering you to proactively detect delays, pinpoint bottlenecks, and alleviate message backlogs. Join us to discover how you can enhance uptime, meet SLAs and customer performance expectations, and further adopt streaming data technologies with confidence.