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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Overview
In today’s digital era, new systems are required that can deliver information with the time-sensitivity to meet rising business and consumer expectations. Hazelcast has already redefined high-performance processing for data at rest. Now, we are bringing that same level of performance to data-in-motion, enabling a new breed of ultra-high-performance applications based on fast streaming data that allows you to process information faster, enable actions within shorter durations, and engage new data at the speed with which it is arriving.
The Hazelcast Jet stream processing framework is built on the foundation of Hazelcast IMDG, the industry’s fastest in-memory data grid.
Hazelcast Jet is an application embeddable, distributed computing platform for fast processing of big data sets. The Hazelcast Jet architecture is high performance and low-latency-driven, based on a parallel, streaming core engine that enables data-intensive applications to operate at near real-time speeds. Jet is used to develop stream or batch processing applications using directed acyclic graph (DAG) at its core.
Architecture & Features
Built on a distributed computing platform, Hazelcast Jet offers a parallel, low-latency core engine for data-intensive applications to operate at real-time processing speeds, while cooperative multi-threading architecture enables operation of thousands of jobs simultaneously.
Hazelcast Jet operates as shared application infrastructure or embedded directly in applications. It is ideal for microservices with a lightweight footprint, making data manipulation easy for developers and DevOps. Cloud and container ready.
One solution provides stream, batch, and RPC processing, with a variety of connectors to enable easy integration into data processing pipelines. Scaling, failure handling and recovery are all automated for ease of operational management.
Embed Hazelcast IMDG as an operational storage for enterprise-grade reliability and resilience, with high-performance integration that eliminates network transit latency and integration issues between processing and storage.
Big data processing at millisecond speed
Highly available and fault tolerant
Distributed, in-memory computation
Supports common data sources (Kafka, HDFS, Sockets, JMS, JDBC etc.)
Enables easy creation of custom sources
Contains scalable data storage with the clients Java, .NET, C++, Python, Node.js and Go
Embeddable for isolated and fully self-contained data-centric services
Elasticity for fault-tolerance and automatic scaling
Supports network discovery
Supports in-memory messaging
Enables low-latency analytics and decision making
Saves bandwidth and enhances privacy by processing data locally
Fully embeddable for simple packaging
Turnkey solution for using trained models in a production environment
Low-latency model execution environment
Apply real-time analytics to high-volume event data to make faster and better decisions
Integrate multiple applications to support multiple business functions
Build continuous analytics processing for enhanced revenue generation, smart resource allocation, improved customer service and other metrics
Use Cases
SigmaStream is deploying Hazelcast as a processing backbone to monitor and analyze high-frequency (50,000+ events per second) data streaming in from oil well sensors on drilling rigs operating thousands of feet beneath the North Sea (an extreme edge use case). This is an industry-leading application of streaming technology in the context of oil and gas drilling platforms and has both broad and deep applicability across the entire domain. Initial estimates indicate a 10% + cost savings in the time required to achieve true vertical depth.
Hazelcast Jet uploads a stream of stock market pricing data from a Kafka topic into an IMDG map. Data is analyzed as part of the upload process, calculating the moving averages to detect buy/sell indicators.
Hazelcast Jet reads a stream of telemetry data from ADS-B on all commercial aircraft flying anywhere in the world, typically 5,000 to 6,000 aircraft at any point in time. This data is filtered and aggregated, and then certain features are enriched and displayed in Grafana.
The world's most advanced stream processing framework.
Learn
The basics of stream processing using Hazelcast Jet
Hazelcast Jet® is an application embeddable, distributed computing platform for fast processing of big data sets. The Hazelcast Jet architecture is high performance and low latency driven, based on a parallel, streaming core engine which enables data-intensive applications to operate at near real-time speeds. Hazelcast Jet is built on top of Hazelcast IMDG®, the leading open source in-memory data grid with tens of thousands of installed clusters. Hazelcast Jet processing jobs take full advantage of the distributed in-memory data structures provided by Hazelcast IMDG
Performant, elastically scalable, and resilient, Hazelcast Jet has been used extensively in financial services for a wide range of real-time use cases. In this webinar, Hazelcast Jet product manager Vladimir Schreiner will provide an overview of Hazelcast Jet, and how it is used at some of the world’s leading banks and credit card providers.
Event stream processing continues to play an increasingly important role in today’s data architectures. This is no surprise, considering that companies are striving to respond faster to ongoing changes in their business environments. However, these companies are still not taking full advantage of the value of their data, typically because they have not planned for the right approaches and architectures for stream processing. Read this Gartner report to learn more.
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.
