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Now, in-memory cache technology is becoming popular, motivating companies to experiment with distributed systems. The technology is advertised to be fast, and data-load speed is often critical for building a successful solution prototype. This blog post provides a technical tutorial on how to populate a distributed Apache Ignite cluster with values that originate from large relational tables. All…
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Using the initial-query, listener, and remote-filter features of Ignite continuous queries to detect, filter, process, and dispatch real-time events (Note that this is Part 3 of a three-part series on Event Stream Processing. Here are the links for Part 1 and Part 2.) Real-time handling of streams of business events is a critical part of modern information-management systems, including online…
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Characteristics, Types & Components of an Event Stream Processing System (Note that this is Part 1 of a three-part series on Event Stream Processing. Here are the links for Part 2 and Part 3.) Like many technology-related concepts, Streams or “Event Streaming” is understood in many different contexts and in many different ways such that expectations for Event Stream Processing (ESP) vary…
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With this first part of “Getting Started with Ignite Data Loading” series we will review facilities available to developers, analysts and administrators for data loading with Apache Ignite. The subsequent two parts will walk through the two core Apache Ignite data loading techniques, the CacheStore and the Ignite Data Streamer. We are going to review these facilities in relation to specific…
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Hadoop Data Lakes are an excellent choice for analytics and reporting at scale. Hadoop scales horizontally and cost-effectively and performs long-running operations spanning big data sets. GridGain, in its turn, enables real-time analytics across operational and historical data silos by offloading Hadoop for those operations that need to be completed in a matter of seconds or milliseconds. In…
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Apache Ignite can scale horizontally to accommodate the data that your applications and services generate. If your in-memory cluster is about to run out of memory space, you can take advantage of horizontal scaling, which is one of Ignite’s foundational architectural capabilities. The “throw more resources into the cluster” approach is an often-heard piece of advice. However, in practice, most of…
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Glenn Wiebe, Solutions Architect at GridGain, has created a helpful video series that introduces developers to Apache Ignite as an in-memory database (IMDB) and features a demo that will set up a working IMDB in ten minutes. The demo walks through the process of configuration creation, data loading and cluster querying via SQL tools. 1. Introduction Learn the difference between Apache Ignite as…
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Note: This is post 1 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 2 click here and for post 3 click here. This is my first blog post in a series that discusses continuous machine learning at scale with the Apache® Ignite™ machine learning (ML) library. In this article, I’ll introduce the notion of continuous machine learning at scale. Then, I’ll discuss…
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Kafka with Debezium and GridGain connectors allows synchronizing data between third party Databases and a GridGain cluster. This change data capture based synchronization can be done without any coding; all it requires is to prepare configuration files for each of the points. Developers and architects who can’t yet fully move from a legacy system can deploy this solution to give a performance…
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In-memory computing can provide tremendous benefits for the 5G ecosystem. We’ve seen the marketing for the new fifth-generation mobile networks. The benefits of 5G for end-users are easy to understand. Speeds faster than your home broadband and latencies only a little slower promise to be game-changers for consumers, enhancing existing applications and opening open entirely new categories that we…
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In keeping with our commitment to more regular and frequent releases, GridGain Web Console 2019.11.00 is now available for download from GridGain Downloads and DockerHub. This release includes improvements for deploying Web Console on RedHat OpenShift, updates to the hosted Web Console, and bug fixes.   RedHat OpenShift Support GridGain and Apache Ignite have supported container-based…
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Ease-of-use is one of the core requirements at GridGain® which influences the way we see and build our products. While in-memory computing is a complex topic, the application development experience should not be equally complex. In the coming months you will see changes to GridGain and Apache® Ignite™ that will simplify Core APIs and the way that you debug running applications. In keeping with…
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Join Greg Stachnick, GridGain's director of product management, for a free one-hour webinar on Aug. 28 at 10 a.m. Pacific time and learn about deploying Apache® Ignite™ into production in public and private clouds. Register here. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices…
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Join the in-memory computing community for two days of learning and networking at the In-Memory Computing Summit North America 2019, November 13-14 at the Hyatt Regency San Francisco Airport’s conference center. I invite current users of in-memory computing to contribute to the conversations by submitting a speaking proposal by August 9 that’s focused on one or more of the following topics…
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GridGain Systems today launched blocks of consulting hours for Apache® Ignite™ and GridGain Community Edition, designed to help companies in their Digital Transformation initiatives. Distributed, in-memory computing is a critical part of nearly all digital transformation initiatives. But it can be challenging for many organizations to apply in-memory computing technology effectively. That's…
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