This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
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.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Enrichment is a frequent technical use case in stream processing. It is a translation of the traditional star schema into the low-latency continuous processing world: the stream of facts is enriched using slowly changing dimension data.
Hazelcast Jet 0.7, released in October 2018, introduces new tools to make enrichment more convenient:
In this webinar you will learn how to do high-performance stream enrichment. We’ll discuss multiple ways of enrichment, explaining the trade-offs. We will feature hands-on examples and live coding using Hazelcast Jet 0.7.
Marko Topolnik is a senior engineer in the Jet Core team. He has been with Hazelcast® since 2015, holds a PhD in computer science and has a six-figure score on Stack Overflow.
Vladimir is a product manager with an engineering background and deep expertise in stream processing and real-time data pipelines. Ten years of building internal software platforms and development infrastructure have made him passionate about new technologies and finding ways to simplify data processing. Vladimir co-authored two white papers on the topic: Understanding Stream Processing: Fast Processing of Infinite and Big Data, and A Reference Guide to Stream Processing. His tutorial video on stream processing and real-time data pipelines discusses the building blocks of a stream processing pipeline and demonstrates how developers can write a full-blown streaming pipeline in less than a hundred lines of Java code for a variety of applications. Vladimir is also a lecturer with the Czechitas Foundation, whose mission is to inspire women and girls to explore the world of information technology. Czechitas Foundation teaches coding in various programming languages, software testing, and data analysis.
This form requires JavaScript to be enabled in your browser. Please enable JavaScript and reload.
