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Stream Deduplication with Hazelcast Jet

Jaromir Hamala

Hazelcast Jet 3.2 introduces stateful map, filter, and flatmap operations, which are very strong primitives. In this blog, I am going to show you how to use stateful filter for detecting and removing duplicate elements in a stream. Why Deduplication? Deduplication is often used to achieve idempotency or effectively-once delivery semantics in messaging systems. Imagine […]

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Hazelcast Delivers Ultra-Fast Cloud Application Performance in IBM Cloud Pak for Applications

Kelly Herrell

We are excited to announce that Hazelcast in-memory technologies are now available for sale from IBM with the IBM Cloud Pak™ for Applications. The Hazelcast in-memory computing platform is an ultra-fast processing architecture for mission-critical applications where microseconds matter. The world’s most data-centric companies complement their systems of record, such as databases, with in-memory solutions […]

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Hazelcast IMDG 4.0 BETA is Released

Matko Medenjak

It is my pleasure to announce that after 6 years a new major version of Hazelcast has been released! This new release brings a breath of fresh air into Hazelcast while also being more robust than ever. We did try to keep enough familiarity to not surprise our users too much, but we also invested […]

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In-Memory Computing for All (Ft. Intel Optane)

Dale Kim

If you had a choice of processing data in-memory versus not in-memory, all other things being equal, wouldn’t you always choose in-memory? You might not need the higher performance, but if it were available to you, you’d take it because faster is always better than slower, right? It would be wonderful if we lived in […]

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Hazelcast Autoscaling with Horizontal Pod Autoscaler (HPA)

Mesut Celik

Cloud technologies give you on-demand options so that you can create compute, disk, or network resources based on your requirements. When your demand changes, you update the infrastructure by releasing some resources or adding more. That is actually named “manual scaling” which is based on human intervention. Kubernetes is no different in this particular use […]

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Hazelcast Jet Connectors Release Announcement

Emin Demirci
by Emin Demirci | September 18, 2019

Since the first release of Hazelcast Jet Extension Modules in July, we’ve been focused on enhancing the set of extension modules that we have to make the integration easy between Hazelcast Jet and third-party data providers.  Today, we are happy to announce the release of the following modules: MongoDB Connector MongoDB is a general-purpose, document-based, […]

Open Banking and the Application of In-Memory Technologies

Dan Ortega
by Dan Ortega | September 12, 2019

The Open Banking initiative, also referred to as PSD2 (Payment Services Directive), is a textbook example of streaming technology applied to modern banking requirements. In this model, banks (primarily in Europe) are required to make their back-end systems for customer accounts and payment services available to other members of the financial payment ecosystem. While this […]

Where Is My Cache? Architectural Patterns for Caching Microservices

Rafal Leszko
by Rafal Leszko | September 10, 2019

I’m sure you use caching somewhere in your system. This can be either to improve performance, reduce backend load, or to decrease downtime. Everybody uses caching. Caching is everywhere. However, in which part of your system should it be placed? If you look at the following diagram representing a simple microservice architecture, where would you […]

Redis Load Handling vs Data Integrity: Tradeoffs in Distributed Data Store Design

Greg Luck
by Greg Luck | September 04, 2019

Introduction We all know that selecting the right technology for your business-critical systems is hard. You first have to decide what characteristics are most important to you, and then you need to identify the technologies that fit that profile. The problem is that you typically only get a superficial view of how technologies work, and […]

Running Apache Beam on Hazelcast Jet

Neil Stevenson
by Neil Stevenson | September 03, 2019

In June 2019, we announced the inclusion of Hazelcast Jet as a runner for Apache Beam. Now it’s time for an example showing how it’s done. As a bonus, it’s not “Word Count.” IoT Data The data we will use is a series of 2,000 GPS points and time offsets: # Latitude, Longitude, Time-Offset 45.417,8.179,1629 45.417,8.178,1630 […]

Auto-Scaling Clusters with Hazelcast Cloud

Enes Akar
by Enes Akar | August 08, 2019

As cloud technologies evolve, applications require less human intervention and maintenance. “Serverless” is a term that implies that users should have nothing to do with servers. The most exciting claim of serverless functions is that they scale automatically as the user base and load grows. Moreover, when there is no user activity, there will be […]

Serverless Fraud Detection Using Amazon Lambda, Node.js, and Hazelcast Cloud

Nazarii Cerkas
by Nazarii Cerkas | July 24, 2019

Recently, an interesting paper was published by UC Berkeley with their review of serverless computing and quite reasonable predictions: “…Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate […]

Announcing the First Release of Hazelcast Jet Extension Modules

Emin Demirci
by Emin Demirci | July 17, 2019

We are excited to announce the new repository for extension modules of Hazelcast Jet. This repository will be home for the Hazelcast Jet extension modules, including connectors (including both sources and sinks), custom aggregations, and context factories. All of these modules will help make the integration easier for 3rd-party products in your pipelines. The main […]

The Role of Streaming Technology in Retail Banking

Dan Ortega
by Dan Ortega | July 16, 2019

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 […]

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