Push Technology, a leader in real-time data streaming and messaging solutions, is launching a new Kafka Adapter for its Diffusion Intelligent Data Mesh.
With the Diffusion Kafka Adapter, organizations can now efficiently and securely extend Kafka solutions over the Internet, streaming real-time data to millions of end-user applications. In addition, customers can easily manage the high-volume of data across geographically dispersed regions.
The Diffusion Intelligent Data Mesh provides pub/sub functionality over last-mile network connections – web, mobile or satellite – reducing application code complexity and speeding time-to-market.
The new Diffusion Kafka Adapter is fully hosted within the Diffusion Cloud infrastructure with an easy-to-configure user interface, for seamless integration with Kafka brokers.
The Adapter automatically maps Kafka message types to JSON, allowing web, mobile, and IoT clients to securely consume data that is stored as rich data structures within Kafka.
The valuable customer benefits of the Diffusion Kafka Adapter are:
- Scalable Topic Models: Diffusion is purpose built to handle millions of unique topics, allowing data to be scaled independently from consumers.
- Security: Security can be a challenge on edge networks. With Diffusion, dynamic multi-tiered security access control is available out-of-the-box.
- Efficient Data Movement: Diffusion is an intelligent solution for the challenge of optimizing real-time data distribution across unreliable networks, with adaptive flow-control, optional conflation of messages, and automatic delta streaming.
“Many organizations struggle to securely and cost-efficiently integrate Kafka into their operations as they build out highly-scalable, real-time web and mobile applications,” said Sean Bowen, CEO of Push Technology. “Our Diffusion Intelligent Data Mesh and Kafka Adapter handle the complexities and address the challenges of Kafka data management and control over the network edge.”
For more information about this news, visit www.pushtechnology.com.