Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
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When recency and speed drive the value of your data, in-memory stream processing solutions from Hazelcast can elevate your business to new levels of performance.
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Overview
Stream processing is a technique to process the data on-the-fly, prior to its storage. This is in contrast with a traditional batch approach, where the data set has to be completely available and stored in the database or file before processing can begin.
A stream processing framework is vital when the value of information contained in the data decreases rapidly as the data ages. The faster the information is extracted from the data and provided to consumers the better.
Typical streaming applications include:
In these use cases, processing data fast is of the same importance as processing vast volumes of data.
Data streams are potentially unbounded and infinite sequences of records, and records usually represent events or changes that happen in time. Stream processing applications are observing flowing records and literally query the stream for relevant data in near real-time.
Typical stream processing tasks:
Hazelcast Jet provides the tooling necessary to build a streaming data application. It gives you a powerful processing framework to query the data stream and elastic in-memory storage to store the results of the computation.
Hazelcast Jet processing tasks, called jobs, are distributed across the Jet cluster to parallelize the computation. Jet is able to scale out this way to process large data volumes.
Hazelcast Jet has very high-speed integration with Hazelcast IMDG, storing large amounts of data that is joined to the Jet stream in microseconds. Latency can also be lowered by using IMDG for stream ingestion or for publishing the results.
Hazelcast Jet works with streaming data in terms of “windows," where a window represents a slice of the data stream, usually constrained for a period of time. Jet supports Tumbling, Sliding and Sessions Windows.
Hazelcast Jet supports the notion of “event time,” where events can have their own timestamp and can arrive out of order. This is achieved by inserting watermarks into the stream of events that drive the passage of time forward.
Hazelcast Jet provides simple fault-tolerant streaming computation without external system or storage using distributed in-memory. Jobs restart automatically using in-memory snapshots, and processing can be resumed where it left off.
The need to trade-off performance and correctness in event processing systems may not allow firm guarantees. Hazelcast Jet allows you to choose the processing guarantee at start time, choosing between No guarantee, at-least-once, or exactly-once.
Resources
Java Champion, Ben Evans, will provide an introduction to stream processing and teach more about core techniques and how to get started building a stream processing application using real world use cases and live demos.
This white paper walks through the business level variables that are driving how organizations can adapt and thrive in a world dominated by streaming data, covering not only the IT implications but operational use cases as well.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
