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.
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The GridGain Systems In-Memory Computing Blog
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:
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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…
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…
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…
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…
Memory access is so much faster than disk I/O that many of us expect to gain striking performance advantages by merely deploying a distributed in-memory cluster and start reading data from it. However, sometimes we overlook the fact that a network interconnects cluster nodes with our applications, and it can quickly diminish the positive effects of having an in-memory cluster if a lot of data…