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.
The speed at which today’s evolving data is exploding (90% of all data was collected in the last 2 years) imposes complex business problems that prevailing technology platforms can not address. This is preventing Enterprises from quickly extracting business value from this data. This poses further challenges as the value of data and the insights we can get from them decrease if it takes too long to take action.
In this talk, we will learn how Hazelcast® addresses these problems and helps Enterprises overcome the challenges of extracting business value from massive scale data.
You will be introduced to distributed systems and in-memory computing with Hazelcast. This talk will cover some familiar distributed data structures like Maps, Lists, Queues, etc., along with running complex business algorithms in parallel over a Hazelcast cluster by using Distributed Executor Service, EntryProcessors and In-Memory MapReduce.
This talk will enable you to get started exploring Hazelcast and give your project a jump start in application speed and scale.
Rahul is a Senior Solutions Architect at Hazelcast® with years of experience in building and architecting scalable, low latency and high throughput infrastructure. His expertise lies in addressing challenges in Big Data and Real Time Analytics space. He specialises in In-Memory Data Grid (IMDG) and governing technologies and Enterprise Architecture. Rahul understands cloud space very well and has delivered several business critical applications deployed on highly mobile yet robust distributed clusters on on-premise, cloud based and/or virtual infrastructure.