MOMO: Accelerating Ad Hoc Analysis with Spark SQL and Alluxio

Alluxio clusters act as a data access accelerator for remote data in connected storage systems. Temporarily storing data in memory, or other media near compute, accelerates access and provides local performance from remote storage. This capability is even more critical with the movement of compute applications to the cloud and data being located in object stores separate from compute. Caching is transparent to users, using read/write buffering to maintain continuity with persistent storage. Intelligent cache management utilizes configurable policies for efficient data placement and supports tiered storage for both memory and disk (SSD/HDD).

Whitepaper: MOMO – Accelerating Ad Hoc Analysis with Spark SQL and Alluxio

From our friends at MOMO The hadoop ecosystem makes many distributed system/algorithms easier to use and generally lowers the cost of operations. However, enterprises and vendors are never satisfied with that, so higher performance becomes the next issue. We considered several options to address our performance needs and focused our efforts on Alluxio, which improves performance … Continued

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Flexible and Fast Storage for Deep Learning with Alluxio

In the age of growing datasets and increased computing power, deep learning has become a popular technique for AI. Deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI, but also exposes some challenges for the access to data and storage systems. In this article, we further describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.