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Interview: Global Technology Leader PNY

The following whitepaper download is a reprint of the recent interview with our friends over at PNY to discuss a variety of topics affecting data scientists conducting work on big data problem domains including how “Big Data” is becoming increasingly accessible with big clusters with disk-based databases, small clusters with in-memory data, single systems with in-CPU-memory data, and single systems with in-GPU-memory data. Answering our inquiries were: Bojan Tunguz, Senior System Software Engineer, NVIDIA and Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY.

Enterprise Search in 2025

This white paper by enterprise search specialists Lucidworks, discusses how data is eating the world and search is the key to finding the data you need. The enterprise search industry is consolidating and moving to technologies built around Lucene and Solr. In the next few years we’ll see nearly all search become voice, conversational, and predictive. Search will surround everything we do and the right combination of signal capture, machine learning, and rules are essential to making that work. Fortunately, much of the technology to drive this is available to us today!

The Four Stages of the Data Journey (and how to get ahead)

Where is your organization on the data journey? Most of us are steadily moving toward the cloud, with most businesses planning to migrate to the cloud or expand their cloud footprint within a few years. But how do you get where you want to go? In this new whitepaper, our friends over at Matillion, the cloud-native platform for all your data integration, take a look at the different stages of the end-to-end journey and learn what it takes to get to the next level.

NVIDIA’s New Data Science Workstation – a Review and Benchmark

This new whitepaper from NVIDIA’s Authorized Channel Partner, PNY Technologies, tests and reviews the recently released Data Science Workstation, a PC that puts together all the Data Science hardware and software into one nice package. The workstation is a total powerhouse machine, packed with all the computing power—and software—that’s great for plowing through data.

Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance

This whitepaper from Imply Data Inc. introduces Apache Druid and explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they leveraged the same technology to create end-to-end real-time analytics architectures.

Introducing Apache Druid

This whitepaper provides an introduction to Apache Druid, including its evolution,
core architecture and features, and common use cases. Founded by the authors of the Apache Druid database, Imply provides a cloud-native solution that delivers real-time ingestion, interactive ad-hoc queries, and intuitive visualizations for many types of event-driven and streaming data flows.

Understanding Intention – Using Content, Context, and the Crowd to Build Better Search Applications

This white paper by enterprise search specialists Lucidworks, points out that unlike consumer search, which has become a seamless part of our everyday lives, the enterprise side might as well still be running Windows 95. Imagine if Amazon, Google, or Facebook treated every user the same, regardless of who they are, where they are, what they’re searching for, and what they’ve clicked. Your users expect that same sophistication in their enterprise apps.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.