Category: Hazelcast

Feed: Blog – Hazelcast. Author: Dan Ortega. Stream processing refers to real-time management of data entering a banking system (or any information system, actually) at high speed and volume, usually from a broad range of sources. The “management” aspect means data is wholly or partially processed and contextualized before entering an in-memory (operational) system, where pre-processing can significantly accelerate response times. Before in-memory technologies, transactional data was managed as a batch process and analyzed every hour, once a day, etc., depending on the data involved, the bank’s policies for handling data, regulatory and compliance requirements, and so on. While having ... Read More

Feed: Blog – Hazelcast. Author: Can Gencer. After releasing Hazelcast Jet 3.0 in May, we are happy to announce its first update, Hazelcast Jet 3.1. Hazelcast Jet is now an Apache Beam Runner Apache Beam is a framework for building distributed batch and stream processing applications over a unified API. The API itself is decoupled from the underlying execution implementation, making it possible to use Apache Beam with different execution engines. Over the last few months, we have been developing a Runner for it and we are happy to announce that the Hazelcast Jet Runner will be included in the next ... Read More

Feed: Blog – Hazelcast. Author: Greg Luck. Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business – IoT sensor network data streams, mobile usage statistics, large scale monitoring, the list is endless. Numerous applications seek the ability to quickly react to dynamic streaming data, as it is either a mandatory requirement or a competitive advantage.  API ChurnAs a consequence, lots of companies see streaming as a valid technology, however many consider it still to be evolving too fast. The programming interface of Spark changed 3 times over the last 5 years (from low-level RDDs to something similar to database ... Read More

Feed: Blog – Hazelcast. Author: Rafal Leszko. Hazelcast IMDG is tightly integrated into the Kubernetes ecosystem thanks to the Hazelcast Kubernetes plugin. In previous blog posts, we shared how to use auto-discovery for the embedded Hazelcast and steps for scaling it up and down using native kubectl commands. In this post, we’ll focus on another useful feature, Rolling Upgrade. You can apply it to your Hazelcast cluster no matter if you use client-server or embedded Hazelcast and regardless of whether you deploy using Kubernetes StatefulSet or Deployment. Everything is, as always with Hazelcast, intuitive and straightforward. Rolling Upgrade StatefulSet The ... Read More

Feed: Blog – Hazelcast. Author: Kelly Herrell. The megatrends that have been driving the adoption of Hazelcast by the world’s largest organizations are now strikingly clear: Enterprises are awash in data and must leverage it for business advantage New applications must process stored and streaming data Application latency is the new downtime. The money is in the microseconds To meet these needs data processing must move beyond the RPM speeds of disk-based systems and into the megahertz speeds of in-memory computing. After more than a decade of adoption of Big Data, the new business advantage is now being found in ... Read More

Feed: Blog – Hazelcast. Author: Ensar Basri Kahveci. At Hazelcast we take reliability very seriously. With the new CP Subsystem module, Hazelcast has become the first and only IMDG that offers a linearizable distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. In addition to well-grounded designs and proven algorithms, reliability also requires a substantial amount of testing. We have been working hard to ensure the validity of our consistency claims. In this blog post I’ll try to demystify the linearizability semantics of the CP Subsystem and explore our Jepsen test suite. This blog post is ... Read More

Feed: Blog – Hazelcast. Author: Rafal Leszko. The sidecar pattern is a technique of attaching an additional container to the main parent container so that both would share the same lifecycle and the same resources. You may think of it as a perfect tool for decomposing your application into reusable modules, in which each part is written in a different technology or programming language. For example, imagine you have an application with some business logic and you want to add monitoring. This monitoring part can be a separate component, very generic, and packaged as a Docker image. Then, you can ... Read More

Feed: Blog – Hazelcast. Author: Matt Wolpin. Last week, Hazelcast earned a coveted spot on the list of Red Herring’s Top 100 North America private technology companies. Make no mistake, there was nothing fishy or misleading about this honor. Ok, enough of the “red herring” puns. If you’re not familiar with Red Herring, it is a global media organization aimed at uniting the world of high-tech innovators, venture capital investors, and business makers. For more than two decades, the publication and Alex Vieux, its current CEO and Chairman, have covered the technology sector with the goal of impacting the global ... Read More

Feed: Blog – Hazelcast. Author: Dan Ortega. Artificial Intelligence (AI) as a concept has been around since the development of computational devices, as early as the creation of Turing machines during World War II. The term itself was first coined by University of Washington professor John McCarthy in 1956, and now, 60+ years later we see the actual commercialization of AI. Why now, and what has finally enabled this? AI is designed to process and interpret vast sums of data (aka Big Data), and while humanity has always generated a lot of data, the volumes in the last few years ... Read More

Feed: Blog – Hazelcast. Author: Hazelcast Community. On Monday, Speedment and Hazelcast announced a partnership focused on accelerating application development when deploying an in-memory data grid alongside legacy databases. The following post was written by Speedment’s CTO, Per-Åke Minborg. What if you want to use a Hazelcast In-Memory Data Grid (IMDG) to speed up your database applications, but you have hundreds of tables to handle? Manually coding all Java POJOs and serialization support would entail weeks of work and when done, maintaining that domain model by hand would soon turn into a nightmare. Now there is a graceful way to ... Read More