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.
Recently Hazelcast Management Center team faced the challenge of finding the right way to store large volumes of time series data.
After considering all possible options, we had to build an embedded Java time series storage on top of existing well-known components.
In this (very) practical talk we are going to discuss technical challenges and design decisions made during the process.
The talk explains the basic principles of time-series data processing and how they affected the design of our TS storage.
Andrey has been involved in the design and development of various web apps and systems for many years and always enjoyed it. It has been a long road from a greenhorn junior developer in a small product team to a solution architect in a large company. Some time ago he joined the Hazelcast engineering team and started working on the company’s products, including Management Center.
He enjoys non-trivial technical challenges that require a deep dive. Has a personal blog, maintains several open-source libraries. Constantly learns new stuff from others and shares his experience with the community.
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.