DataStax launches streaming data platform with backward support for JMS

Or move to Apache Pulsar for efficiency gains, says NoSQL vendor


DataStax, the database company built around open-source wide-column Apache Cassandra, has launched a streaming platform as a service with backwards compatibility for messaging standards JMS, MQ, and Kafka.

The fully managed messaging and event streaming service, based on open-source Apache Pulsar, is a streaming technology built for the requirements of high-scale, real-time applications.

But DataStax wanted to help customers get data from their existing messaging platforms, as well as those who migrate to Pulsar, said Chris Latimer, vice president of product management.

He explained that DataStax had created a framework called Starlight, designed as a wire-level API which supports other platforms and protocols. "As part of Astra Streaming, we have Starlight for JMS, Starlight for RabbitMQ, and Starlight for Apache Kafka. So many of the legacy systems that enterprises use today are using JMS. We have essentially said you can take your applications that are using JMS and – without changing any code – you can just point those to Astra Streaming. You can turn those messaging apps in to streaming apps and start capturing and recording all of the event data," he told The Register.

But customers could still get performance advantages by moving to Pulsar. Research from GigaOm – sponsored by DataStax – found that Pulsar has 35 percent higher performance and up to 81 percent lower three-year cost than Kafka.

"There's a big cost savings element here because a lot of enterprises invested heavily in JMS in the past," Latimer said. "For example, we've seen several customers that have a server farm of around 4000 VMs that are running JMS. There are big cost savings and architectural simplification benefits for these companies. And at the same time, it really helps to advance that data in motion, the real-time data strategy that most of these enterprises are pursuing right now."

Astra Streaming also supports a unified event fabric across on-premises systems and those in the cloud and edge. It also ships with connectors to move messaging data to streaming analytics and machine learning systems.

Amy Machado, IDC streaming data pipeline research manager, said continuously processing streams of data is imperative for enterprises to optimize decisions, actions, and experiences.

"DataStax delivers a unique cloud-native architecture that can manage both streaming data-in-motion and operational data-at-rest so enterprises can get value in real-time from all of their data," she said. ®

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