Hazelcast Frequently Asked Questions

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A unified real-time data platform combines critical components of a real-time system into a single, tightly integrated cluster. Hazelcast satisfies these requirements by offering a high-performance stream processing engine and an ultra-fast data store within the same cluster. With fewer moving parts to manage, Hazelcast provides state storage, resilience through snapshots, fast stream enrichment lookups, and digital integration hub capabilities, all essential for real-time stream processing deployments.

Hazelcast is used over other streaming data platforms for 3 key reasons:

It is designed for instant action, where your applications can automate work so you can take advantage of time-sensitive opportunities that are otherwise buried in the data.

It simplifies application development and deployment by reducing the number of siloed technologies that add complexity to a streaming data deployment.

It has proven superior performance (check out our results in the ESPBench benchmark from the Hasso Plattner Institute and the NEXMark benchmark).

Stream processing refers to the advanced analysis and manipulation of data streams in real-time. It involves performing tasks such as stateful aggregations, window operations, mutations, and materialized view creation on an endless flow of data.

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Event streaming provides the infrastructure and means to transmit and store real-time data streams. Stream processing, on the other hand, is the next step that involves processing and analyzing these data streams for more meaningful insights and actions.

The main difference lies in their primary purposes and functionalities. Event streaming deals with the transportation and persistence of data streams, while stream processing focuses on the real-time analysis and transformation of those streams.

Absolutely! Event streaming platforms like Apache Kafka serve as a reliable foundation for transmitting and storing data streams, while stream processing technologies like Apache Flink and Spark Streaming and Hazelcast platform allow developers to process and derive valuable insights from these streams.

Stream processing enables real-time data analysis, providing immediate insights and faster responses to dynamic data changes. In contrast, traditional batch processing processes data in fixed intervals, resulting in delayed insights and actions.

Hazelcast platform excels in stream processing by offering developers an optimized approach to handle real-time data streams. It leverages the powerful aggregation framework to efficiently process streams and unlock their full potential, enabling businesses to make well-informed and prompt decisions.