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.
Understanding driver behavior via the use of connected cars can help organizations make data-driven decisions to reduce safety risks, improve commercial driver productivity, and streamline fleet operations.
One area of analysis pertains to distracted driving, which poses a huge risk to the driver and the environment, in addition to financial loss. But distracted driving is not always manifested in obvious patterns, so the use of machine learning can help to uncover the indicators that drivers are distracted.
In this webinar by Hazelcast and Intel, we will show a method for the non-intrusive and real-time detection of visual distraction. We will discuss:
Terry Walters is a Senior Solution Architect at Hazelcast®, a Java-based, open-source operational in-memory computing platform. He is well known as a speaker at user groups/conferences and he enjoys helping others succeed with web scale. Prior to Hazelcast, he worked many industry leaders such as AT&T, Verizon, McKesson, UPS, and the formally known as BEA Systems. Walters holds a bachelor’s degree in computer science.