Datadog Releases Data Streams Monitoring to Assess Streaming Data Pipeline Performance
Data Streams Monitoring helps organizations measure and meet strict SLAs and avoid critical downtime by observing queue health, consumer lag and end-to-end latency in event-driven applications like those using Kafka and RabbitMQ
Traditional solutions often provide only a limited view of streaming data pipelines and struggle to consistently identify the root causes of issues, such as drops in throughput or failing services. They may also lack context from upstream and downstream dependencies. This means internal teams must devote more time to manual tasks and troubleshooting in order to gather relevant information, analyze it contextually and determine the necessary collaborative efforts with other teams.
Streaming pipelines lie at the heart of numerous mission-critical applications, including banking and trading technologies, automotive systems, payment platforms and video infrastructures. Teams responsible for managing these streaming pipelines require precise measurement of end-to-end request latencies as they are frequently governed by service-level agreements (SLAs) and regulatory restrictions.
As part of
"Building streaming data pipelines to power our digital trading platform requires visibility into throughput and latency across the entire system, including all queues and services," said
"Kafka has become a key component in powering our data processing infrastructure and gaming data streams, which are built using highly sophisticated architectures," said
"There is a gap in visibility between SREs and application developers today, which limits their understanding of how queues are impacting mission-critical services and end-to-end latency of high-value user experiences like the resolution of banking transactions, the generation of shopping recommendations or payment processing," said
Data Streams Monitoring is now generally available for Kafka and RabbitMQ, with support for more technologies coming soon. Pricing starts at
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