Streamline and Enhance Real-Time Architectures with Hazelcast 5.2

Hazelcast Inc., provider of a real-time stream processing platform, is making additions to the platform’s capabilities—notably in regards to joining multiple streams of live data, as well as merging them with large volumes of stored data. Zero-code connectors are also featured in this latest update, accelerating integration of stream processing efforts and real-time applications within existing systems.

Hazelcast’s aims to simplify data architecture frameworks for enterprise applications, while simultaneously deviating from common database-centric approaches that inhibit real-time adoption. The platform’s stream processing engine performs upstream, in-flight computation while merging data with the historical context located in the low-latency, scalable data store built into the platform.

“Hazelcast’s innovation of combining stream processing and low-latency data management into a single platform enables new opportunities for instant responsiveness, while also offering simplified management and operations in a real-time architecture,” said Kelly Herrell, CEO of Hazelcast. “Thriving in the real-time economy requires instantaneous computation on both new and historical data, something traditional databases cannot do. After years of building our extremely reliable, low-latency data store, we’re focusing on the convergence with real-time data to give enterprises a new approach to improving customer satisfaction, generating new revenue and mitigating risk.”

Stream-to-stream join functionality empowers organizations to merge several data streams with an ultra-fast, low-latency data store, according to the vendor. This capability prevents analysis and action processes from being bogged down by writing times associated with traditional databases.

Zero-code connectors encourage accessibility for Hazelcast’s platform, enabling users to retrieve contextual data from existing data platforms. These connectors are currently in its beta stage and offer support for AWS Relational Database Services (RDS) for MySQL and PostgreSQl; additional connectors will be supplied in future updates.

Hazelcast’s announcement also introduces a highly performant tiered storage system which allows users to keep hot data in memory, increasing throughput, reducing latency, and improving cold data maintenance. This system provides additional functionality to users by enabling enrichment of real-time data with reference data stored on NVMe-based SSDs.

Other features released in this update include JSON SQL functions for OBJECT and ARRAY aggregations; access and field update capabilities for Java nested objects; JDBC driver for streamlined tool connection to Hazelcast; enhanced usability of Management Center; and a series of Compact Serialization revisions that reduces its space consumptions, does not require editing, and allows seamless data model evolution.

For more information about Hazelcast’s platform, please visit