In this webinar you will learn how to do high-performance stream enrichment. We’ll discuss multiple ways of enrichment, explaining the trade-offs. We will feature hands-on examples and live coding using Hazelcast Jet 0.7.
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.
Are you a developer, software engineer or architect looking to apply in-memory technologies to your current architecture to deliver ultra-fast response times, better performance, scalability and availability? Are you looking for new tools and techniques to manage and scale data and processing through an in-memory-first and caching-first architecture?
The Hazelcast IMDG® 3.11 release has been a significant community driven effort, with Hazelcast IMDG 3.11, we are making sure all of our code will be functional with the language features and ecosystem improvements introduced in Java 9, 10 and 11.
Join Rafal Leszko, Software Engineer at Hazelcast, as he discusses caching and other valid use cases for in-memory key-value stores. He will introduce the Hazelcast In-Memory Data Grid (IMDG) tool and present the features which make so many companies choose it over other similar solutions. Rafal will also provide details with regards to the Hazelcast OpenShift integration.
In this webinar, we will take a look at what it means for anyone currently using or interested in using Hazelcast to build a cloud native application. We will review how to setup and deploy using a sample application, and get you ready to have a starter application of your own running on Kubernetes.
Many companies today have a mix of batch, mini-batch, and stream-based processing. The question is whether organizations should embrace streaming as the default mode of data acquisition? Several vendors are now pitching streaming-first architectures and extolling the benefits of processing data in real-time. This webinar will explore the pros and cons of a streaming-first architecture and examine industry trends in its adoption.
Learn why Gamesys turned to Hazelcast when it was building a highly scalable poker platform. It required a fast, elastic and easy to use solution that could be used for both real money and social gaming.
Java Champion, Ben Evans, will provide an introduction to stream processing and teach more about core techniques and how to get started building a stream processing application using real world use cases and live demos.
The goal of streaming systems is to process big data volumes and provide useful insights into the data prior to saving it to long-term storage. This white paper introduces you to the domain of stream processing covering topics such as use cases that benefit from stream processing, the building blocks of a stream processing solution, key concepts used when building a streaming pipeline, and runtime aspects and trade-offs between performance and correctness.
Hazelcast IMDG® has been a popular choice with MuleSoft users to cache data in an external in-memory cluster. This is also a very easy way to pass data or share state from one service to another. This document provides a step-by-step guide on how to store, retrieve and share data between services using an external Hazelcast cluster from multiple MuleSoft services.
In this webinar, we’ll showcase some of Jet’s capabilities, such as: real time ingestion of data from Kafka into Hazelcast, building a ETL pipeline which can pump data into Hazelcast from a system of record or export to it, enriching stream data with low latency using Hazelcast as a cache, and using Jet for event based processing.
Hazelcast Jet is a 3rd generation stream processing engine. In this webinar you’ll find out how easy it is to deploy and get started. Join us to learn how to build batch and streaming data pipelines using the fluent, high-level Java API of Hazelcast Jet.
The Hazelcast IMDG 3.10 release has been a significant community driven effort and includes the release of a conflict-free replicated data type (CRDT), a Flake ID Generator and several split-brain protection enhancements.
This paper is intended for software developers and architects who are planning a Big Data, IoT, or low latency application that requires scalable, high-performance processing, and want to understand the critical infrastructure requirements.
In this webinar, we will explore ways to easily operationalize or inject the benefits of predictive analytics in real-time application using Hazelcast IMDG and Jet.
In this webinar we’ll cover the possible options for data storage in Hazelcast, and how these impact speed, space and cost. In this webinar, we’ll start from the basics, and progress incrementally through the various options, pointing out the pros and cons for each.
Learn about the new Jet Pipeline API and other improvements that we’ve made in this webinar presented by Marko Topolnik, Senior Software Engineer at Hazelcast.
This webinar will give a quick but fun 10 step guide of the requirements for when you are going into production with Hazelcast IMDG—what are the most common pitfalls for handover between developers and operations, as well as how to solve those issues first hand.
Hazelcast IMDG Integrates with Apache Cassandra to Deliver Fast, Scalable IoT Data Platform for Future Grid
Learn how Future Grid is revolutionizing the electrical industry. By turning to Hazelcast IMDG for thier in-memory infrastucture, Future Grid is solving some of the most difficult data processing problems of a hyper-connected world, while saving thier customers tens of millions of dollars in the process.
Join Hazelcast Core Team Lead, Tom Bujok, in a deep dive overview of the Hazelcast IMDG feature Rolling Upgrades to learn how you can keep your system running continuously while upgrading IMDG servers and clients.
In this Video Case Study, learn how Irish Revenue updated their IT architecture to handle nationwide online reporting of a new property tax.
This paper is intended for software architects and developers who are planning a Eureka Server and Hazelcast IMDG deployment and want to understand how to set this up.
This case study presents a comparison of alternative data stores and approaches, showing how a global bank, ranked in the top ten by assets, has turned to Hazelcast to improve experience for customers across all of its digital platforms (web, mobile, branch).
This case study presents a comparison of alternative data stores and approaches, showing how a top ten US bank uses Hazelcast IMDG to scale their Fraud Detection algorithms against customer data sitting on its old relational database platform.
Hazelcast has enabled the global pizza delivery chain to make optimal usage of its resources and has allowed developers to think in terms of well-known data structures such as Maps,Queues etc…
Learn about the inventory management requirements of leading retailers who straddle brick and mortar as well as e-commerce sites and how in-memory computing is enabling omni-channel inventory management.
This case study presents a comparison of alternative data stores and approaches, showing how the UK’s largest IoT network is using Hazelcast IMDG to meet the operating requirements of their IoT architecture.
This case study presents a comparison of alternative data stores and approaches, showing how a national tax authority uses Hazelcast IMDG to assist with their public service.
This case study presents a comparison of alternative data stores and approaches, showing how a leading national bank used Hazelcast IMDG to do a complete overhaul of their risk system.
Learn about the requirements of how businesses provide accurate and timely risk reporting to regulatory agencies from a disparate set of systems, from legacy to cloud-based, using in-memory computing technology.
This case study presents a comparison of alternative data stores and approaches, showing how a major London investment bank is using Hazelcast IMDG as an all-in-one technology for building the system.
This case study presents a comparison of alternative data stores and approaches, showing how one top global e-commerce retailer with $18.3 billion in sales grows their business using Hazelcast IMDG.
Get up and running with the Hazelcast IMDG C# / .NET Client quickly with this easy to use reference card.
Learn how IoT applications can benefit by combining Apache Kafka and Hazelcast IMDG.
Compare performance of Memcached 1.4.33 vs Hazelcast IMDG 3.7.5
Learn about in-memory distributed processing for big data with Hazelcast Jet.
Roundtable webinar with Aerospike, Attunity and Hazelcast
Join us for this webinar on Hazelcast Striim Hot Cache, where we’ll show you how to keep your cache hot with real-time, push-based synchronization. Live Q&A after the webinar.
In the webinar: Learn how to maintain your cache/database consistency with XA transactions
In this webinar, we’ll cover: What Spring Data is and the benefits it brings; The advantages of using Hazelcast with Spring Data compared to traditional databases; Basics of the coding, querying and general capabilities; Demo of a full example, available to download after.
In this webinar we’ll cover: common issues with big heaps in Java, Garbage Collectors in common JVMs, and how High-Density Memory Store can keep your latencies under control.
Learn how the UK’s largest Internet of Things network, British Gas, uses Hazelcast in-memory data grid to serve over 200,000 homes with a system that allows users to remotely control their heating and hot water temperature from their mobile device or on the Web.
In this webinar, learn about Hazelcast’s extensible, JAAS based security feature.
Microservices, as an architectural approach, has shown a great deal of benefit over the legacy style of monolithic single applications. Nevertheless, microservices are not without their drawbacks. The purpose of this white paper is to show first steps for using Spring Boot and Hazelcast IMDG contribute to the microservices landscape, enhancing the benefits and alleviating some of the common downsides.
Learn how Hazelcast Hot Restart Store can keep your caches hot across application restarts.
Most Powerful Java Runtime Meets Fastest In-memory Data Grid for Demanding Enterprise Workloads | Azul Systems & Hazelcast
Join this webinar to learn how the combination of Hazelcast plus Azul System’s Zing Java Virtual Machine (JVM) delivers compelling benefits to the enterprise.
“Caching Made Bootiful: The Hazelcast Way,” a code-driven webinar, demonstrates how to integrate Hazelcast Distributed Caches into your Spring (Boot) applications using Spring’s Cache Abstraction and Auto Configuration features.
Compare GridGain/Apache Ignite 1.5.0 vs Hazelcast 3.6 Benchmark. This benchmark was prepared using GridGain’s own benchmarking tool, Yardstick.
Join us for our latest webinar as we demonstrate how Heimdall can be used to “Hazelcast-enable” any existing application without changing its code.
This benchmark tests the write and read performance of the Hot Restart Store, introduced in Hazelcast Enterprise HD 3.6. All benchmarks test the performance of one Hazelcast member running on a physical server. As the Hot Restart Store is local to each member, performance is linearly scalable.
Looking to migrate off Terracotta BigMemory/Ehcache and onto Hazelcast IMDG? Download our step-by-step migration guide to help you make the move.
Hazelcast IMDG helps Ellie Mae achieve horizontal scale while continuing to deliver on service level agreements (SLA). Download the case study.
Interested in comparing Hazelcast with Coherence? Hazelcast provides a benchmark suite on speed and performance. Just complete the form on the right and we’ll reach out to you soon.
Compare Hazelcast 3.6-SNAPSHOT and Red Hat Infinispan 7.2 side by side. This benchmark was prepared using Radar-Gun’s own benchmarking framework.
Compare Hazelcast 3.6-EA with GridGain 7.4 / Apache Ignite 1.4.1. This benchmark was prepared using GridGain’s own benchmarking tool, Yardstick.
In this webinar we’ll introduce you to the Hazelcast Web Session Replication module. This module makes replicating and distributing data easier than ever.
How to Achieve Developer-friendly, Resilient Workflow Automation with Camunda and Hazelcast.
Hazelcast IMDG on Azure: Best Practices for Deployment highlights best practices for cloud architects and developers gearing up Hazelcast applications to a virtual cloud environment.
The guide takes a step by step approach, covering all of the main features of Oracle Coherence and then introduces the developer to their equivalent in Hazelcast. Each chapter provides code samples in Oracle Coherence and in Hazelcast.
Join guest speaker Mike Gualtieri, Principal Analyst at Forrester Research, Greg Luck, CEO of Hazelcast, and Ken Kolda, Software Architect of Ellie Mae on this radio-show style webinar to boost your in-memory IQ.
Since version 3.4, Hazelcast engineers have invested months of effort to make broad-based, across-the-board performance enhancements. As a result, the Hazelcast 3.5 release enjoys a wide array of significant performance improvements over the 3.4 version.
A deeper look at key points and architecture of Hazelcast IMDG High-Density Memory Store.
This webinar will start with a simple JAX-RS/JPA application. We will turn this standard Java EE application, step by step, into a fully clustered application using a CDI extension and producers to integrate Hazelcast, as a JCache provider.
Payara 4.1.151 was released at the end of January and one of the new enhancements is Hazelcast session persistence. Technical Director and Founder of Payara, Steve Millidge teamed up with Hazelcast to demonstrate the new feature in action.
This online retailer is bigger than Staples, Walmart.com and Dell and is admired throughout the world for its high performance and top-notch brand and user experience.
This white paper provides a point by point comparison of Hazelcast IMDG and Infinispan/JBoss Data Grid.
This whitepaper provides a point by point comparison of Hazelcast IMDG and Pivotal GemFire.
This guide is for cloud architects and developers preparing an application utilizing Hazelcast IMDG to be deployed from a physical environment to an Amazon EC2 virtualized cloud environment.