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.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away. Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
Overview
Hazelcast can be used to accelerate and scale your SaaS or custom internal applications by increasing throughput and reducing the latency of data accesses on disk-based databases. Add Hazelcast as an in-memory store between your application and your database to handle more users and higher load while improving response times. Updates that your applications make to data in Hazelcast will be passed through to the underlying database to ensure data synchronization.
Hazelcast provides the enterprise-ready capabilities to run your business-critical applications:
Just add more servers and they cluster automatically
Can deploy in backup pairs or even WAN replicated
No single point of failure
Deploy right into the public cloud
To accelerate their applications and is also leveraged/embedded in many open source projects
Resources
Machine learning (ML) is being used almost everywhere, but the ubiquity has not been equated with simplicity. If you solely consider the operationalization aspect of ML, you know that deploying your models into production, especially in real-time environments, can be inefficient and time-consuming. Common approaches may not perform and scale to the levels needed. These challenges are especially true for businesses that have not properly planned out their data science initiatives.
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The Infinity Data research, commissioned in collaboration with Intel, examines how companies are addressing the challenge imposed by latency. The research was conducted through a survey of more than 350 IT decision-makers in the US and across industries: financial services, e-commerce, telecommunications, energy, and the public sector.
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.
