How Automation Helps You Exploit the Value in Big Data

In this sponsored post, Simon Shah spearheads marketing at Redwood Software to support continued market growth and innovation for their cloud-based IT and business process automation solutions. He believes that by using automation to collect and manage your big data processes, you will truly exploit its value for the business.

Video Highlight: A Vision of Analytics — Challenge the Data Warehouse Status Quo

The pandemic and related disruptions have caused companies to think hard about their data and analytics strategies and how they get real-time answers. In the keynote presentation below, Yellowbrick CEO, Neil Carson, discusses why the single most important set of technologies a company should be investing in today is an analytics infrastructure. Carson explains why these are the technologies that will determine whether your business survives.

The State of Data Management – Why Data Warehouse Projects Fail

Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and UK, this whitepaper explores the data management challenges organizations are facing, the vital role data warehouses play, and the road to success.

The State of Data Management – Why Data Warehouse Projects Fail

Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who  surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and  UK, this whitepaper explores the data management challenges organizations are facing, the  vital role data warehouses play, and the road to success.

Data Warehouse Automation: Five Steps to Success

In this contributed article, Stan Geiger, director of multi-platform tools, which includes WhereScape data automation, at Idera, Inc., discusses how embracing data warehouse automation is not just a matter of implementing new tools or technologies.. Success can depend on building a number of key steps, ideas and processes into the strategy.

83% of IT Leaders are Not Fully Satisfied with their Data Warehousing Initiatives, According to New Research from SnapLogic

New research published by SnapLogic, provider of the Intelligent Integration Platform, reveals that 83% of organizations are not fully satisfied with the performance and output of their data management and data warehousing initiatives. IT leaders cite a growing number of disconnected applications and data sources, outdated legacy systems, and slow and manual data movement as reasons for their frustration, all of which are stalling progress and costing them millions.

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.

What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics

In this contributed article, Christopher Rafter, President and COO at Inzata,, writes that in the age of Big Data, you’ll hear a lot of terms tossed around. Three of the most commonly used are “business intelligence,” “data warehousing” and “data analytics.” You may wonder, however, what distinguishes these three concepts from each other so let’s take a look.

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

This whitepaper from Imply Data Inc. 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.

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.