Archive
June 2020

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…
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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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