Archive
August 2018

GridGain technology evangelist Akmal Chaudhri delivered the second webinar of his popular highly technical two-part series for software developers and architects on Aug. 28 titled, "In-Memory Computing Essentials for Architects and Developers: Part 2." This free webinar was recorded and is available for playback (and the slides for download) here. Akmal continued his introduction of more of the…
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In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together. Let's briefly recap what we covered in the first article. Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale. Spark is a streaming and compute engine…
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Apache® Ignite™ is a very versatile product that supports a wide-range of integrated components. These components include a Machine Learning (ML) library that supports popular ML algorithms, such as Linear Regression, k-NN Classification and K-Means Clustering. The ML capabilities of Ignite provide a wide-range of benefits, as shown in Figure 1. For example, Ignite can work on the data in-place…
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In this two-part series, we will look at how Apache® Ignite™ and Apache® Spark™ can be used together. Ignite is a memory-centric distributed database, caching, and processing platform. It is designed for transactional, analytical, and streaming workloads, delivering in-memory performance at scale. Spark is a streaming and compute engine that typically ingests data from HDFS or other storage.…
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In-memory computing is a journey that requires up-front planning and preparation to complete. Companies often start with caching to add speed and scale to existing applications. But before you know it, you're building new hybrid transactional/analytical processing (HTAP) applications, new streaming analytics applications, or even starting to implement machine and deep learning. If you don't…
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