VMware Tanzu Greenplum is a massively parallel processing (MPP) data platform based on the open source Greenplum Database project. It's designed to run the full gamut of analytical workloads, from BI to AI. Because enterprise data lives and grows throughout an organization, it is suboptimal to copy large data sets between different systems as they aren't able to perform fast enough, scale high enough, or offer the right features. In this post, we will discuss the power of MPP on vSphere for end-to-end data science workflows. We will share scalability results from traditional machine learning workloads as well as deep neural networks leveraging GPUs.

Datastores continue to advance on a number of fronts. Some of those that come to mind are adapting to faster networks (e.g. 'FARM: Fast Remote Memory') and persistent memory (see e.g. 'Let's talk about storage and recovery methods for non-volatile memory database systems'), deeply integrating approximate query processing (e.g. Today's paper gives us an exciting look at the untapped potential for network-accelerated query processing.