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.
Today, we are happy to announce Hazelcast Cloud Public API support via GraphQL to provide more flexibility to automate Hazelcast Cloud clusters. If you have a Hazelcast Cloud account, you can go to GraphQL Explorer to try queries and mutations. Hands-on Examples Create Cluster In order to create a cluster in Hazelcast Cloud, you need […]
Imagine a client-server system that involves reading values from a data store. In the event of a connection failure with the server, the system will not be reading values. The consequences of such an event might be quite significant, including potentially costing money for a financial institution or time for a time-critical operation. If a […]
Hazelcast is a swiss-knife for distributed computing. You can use it as cache, distributed lock, key-value store, pub/sub, computing platform, and many more. Hazelcast also has a rich array of integrations that allow you to deploy it in cloud environments, inside microservices, or connect with your Java EE applications. How to find yourself in this […]
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NOTE: This is an updated version of a previous blog. This article contains updated information. You can use Hazelcast for HTTP session replication between multiple replicas of your Spring Boot application. For example, imagine that you have a classic example of an online shop with customer’s items stored in the session. Users start to add […]
It has been said that there are two things hard in software development, naming things and cache invalidation (while some add off-by-one errors to the mix). I believe that keeping the cache in sync with the source of truth might count as a third one. In this post, I’d like to tackle this issue, describe […]
Jet 4.2 is finally here! Here’s an overview of what’s new: Change Data Capture Support for MySQL and PostgreSQL Previously, Jet has had support for Debezium as a contrib package. We’re happy to announce that we’ve made several improvements to this package and decided to make this a part of our main release. Debezium was developed initially […]
Hazelcast IMDG can be fairly simply configured to work on AWS ECS. This Blog Post presents this process step by step.
Learn how to set up a Hazelcast cluster on AWS Auto Scaling group.
Today, we’re happy to announce the preview version of quarkus-hazelcast-client is publicly available and accessible via quarkus-platform and code.quarkus.io. Quarkus is a modern Kubernetes-native application framework leveraging Docker and GraalVM which allows it to achieve impressive performance and resource utilization while maintaining portability. Quarkus Hazelcast Client extension makes it convenient to configure Hazelcast Client and […]
It’s useful to understand how, why and where Hazelcast stores your data in the grid. What happens is what you really need, but it won’t necessarily be what you first think you want. We’ll explore this in this blog by putting some number keys in a map, a common starting point and source of confusion. […]
Learn how to use Hazelcast Helm Chart
Note: This post is part 2 of 2 on edge-to-cloud. You can read part 1 here. Inside the Gateway: Edge-to-Cloud Stage 1 The edge-to-cloud pipeline begins inside the gateway, where two key tasks have to be performed: Data from sensors and devices must be captured in its raw form Sensor and device data must be […]
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