The GridGain Systems In-Memory Computing Blog

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…
In this third article of the three-part series “Getting Started with Ignite Data Loading,” we continue to review data loading into Ignite tables and caches, but now we focus on using the Ignite Data Streamer facility to load data in large volume and with highest speed. Apache Ignite Data-Loading Facilities In the first article of this series, we discussed the facilities that are available to…
In this second article of the three-part “Getting Started with Ignite Data Loading” series, we continue our review of data loading into Ignite tables and caches. However, we now focus on Ignite CacheStore. CacheStore Load Facility Background Let’s review what was discussed about CacheStore in “Article 1: Loading Facilities.” The CacheStore interface of Ignite is the primary vehicle used in…
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…
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…
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…
Apache Ignite Deployment Patterns The Apache Ignite® in-memory computing platform comprises high-performance distributed, multi-tiered storage and computing facilities, plus a comprehensive set of APIs, libraries, and frameworks for consumption and solution delivery (all with a “memory first” paradigm). This rich set of capabilities enables one to configure and deploy Ignite in many diverse…
Note: This is the third and final post in the blog series: Continuous Machine Learning at Scale With Apache Ignite. For post 1 click here and for post 2 click here. In my first post, I introduced Apache® Ignite™ machine learning and explained how it delivers large-scale, distributed, machine-learning (ML) workloads. In my second post, I discussed the Apache Ignite model-building stages. The…
Note: This is post 2 in the blog series: Continuous Machine Learning at Scale with Apache Ignite. For post 1 click here and for post 3 click here. In my first post, I introduced the topic “continuous machine learning at scale with Apache Ignite,” which is how we members of the Apache® Ignite™ community describe machine learning (ML) architectures that offer the following advantages: Support…
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…