{"id":16548,"date":"2016-11-25T05:00:32","date_gmt":"2016-11-25T13:00:32","guid":{"rendered":"http:\/\/insidebigdata.com\/?p=16548"},"modified":"2016-11-23T14:59:18","modified_gmt":"2016-11-23T22:59:18","slug":"the-gridgain-in-memory-data-grid","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2016\/11\/25\/the-gridgain-in-memory-data-grid\/","title":{"rendered":"The GridGain In-Memory Data Grid"},"content":{"rendered":"

In this special technology white paper, The GridGain In-Memory Data Grid<\/a>, you\u2019ll learn that with the cost of system memory dropping 30% every 12 months, in-memory computing has become the first choice for a variety of workloads across all industries. In-memory computing can provide a lower TCO for data processing systems while providing an unparalleled performance advantage. In-memory computing technologies take many forms, ranging from in-memory data caches on a single server to in-memory databases (IMDBs),\u00a0 inmemory data grids (IMDGs), and comprehensive in-memory computing platforms (IMCPs). High performance in-memory computing technologies can even allow real-time analytics to run on operational datasets, enabling the implementation of hybrid transactional\/analytical processing (HTAP) systems that can provide significant cost and complexity savings.<\/p>\n

This white paper provides an overview of in-memory computing technology with a focus on in-memory data grids. It discusses the advantages and uses of in-memory data grids and introduces the GridGain In-Memory Data Fabric. Finally, it presents a deep dive on the capabilities of the GridGain solution.<\/p>\n

\"gridgain_data_grid\"<\/p>\n

As disk-based technologies fail to keep up with the demands of Big Data, the superior speed and scalability of in-memory computing technologies make them the clear choice for data-intensive applications. Platforms built around in-memory data grids can inexpensively harness the memory and computing power of a large array of nodes built on commodity hardware. With the right combination of additional features, they can provide the
\nessential tools for ingesting and analyzing very high volumes of streaming and transactional data with ease \u2013 turning Big Data into Fast Data.<\/p>\n

The white paper includes the following sections:<\/p>\n