Sign up for our newsletter and get the latest big data news and analysis.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 2

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence

In this technical blog for data architects by our friends over at Swim, we learn how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data.

A Comprehensive Guide to Evaluating Customer Data Platforms (CDPs)

This white paper by our friends over at HGS Digital aims to help you evaluate Customer Data Platform (CDP) vendors on various key areas. Investing in a CDP should be done with a long-term aim – various systems from which the data is imported and systems to which data is exported may change over time, but the CDP becomes the master repository of data and should be leveraged by the marketing organization for a very long time.

Data Transformation for Machine Learning

In this contributed article, Damian Chan, Technical Success Manager at Matillion, discusses common data transformations that you can perform so your data can be processed within machine learning models. When it comes to machine learning, you need to feed your models good data to get good insights. Data in the real world can be really messy and in most cases, some sort of data cleansing needs to be performed prior to any data analysis.

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 Essential Guide: Machine Scheduling for AI Workloads on GPUs

This white paper by Run:AI (virtualization and acceleration layer for deep learning) addresses the challenges of expensive and limited compute resources and identifies solutions for optimization of resources, applying concepts from the world of virtualization, High-Performance Computing (HPC), and distributed computing to deep learning.

Intel® Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC & Artificial Intelligence with Faster Parallel Code

In this article we’ll drill down into the capabilities of Intel® Parallel Studio XE 2020, the latest release of a comprehensive, parallel programming tool suite that simplifies the creation and modernization of code. Using this newest release, software developers and architects can speed AI inferencing with support for Intel® Deep Learning Boost and Vector Neural Network Instructions (VNNI), designed to accelerate inner convolutional neural network (CNN) loops.

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.

Machine Learning for All: the Democratizing of a Technology

Our friends over at H2O.ai have produced a short new eBook “Machine learning for all: the democratizing of a technology” which covers machine learning features and automatic AI solutions, and how organizations can benefit from using them.

Heterogeneous Computing Programming: oneAPI and Data Parallel C++

Sponsored Post What you missed at the Intel Developer Conference, and how to catch-up today By James Reinders In the interests of full disclosure … I must admit that I became sold on DPC++ after Intel approached me (as a consultant – 3 years retired from Intel) asking if I’d help with a book on […]