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Big Data and The HPE Workload and Density Optimized System

This is the fifth entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the exponential growth of data and the changing data landscape, as well realizing a scalable data lake. The fifth entry in the series is focused on the HPE Workload and Density Optimized System.

The HPE Elastic Platform for Big Data Analytics

This is the fourth entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the exponential growth of data and the changing data landscape, as well realizing a scalable data lake. The fourth entry in the series is focused on offerings from HPE for big data analytics.

Realizing a Scalable Data Lake

This is the third entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the exponential growth of data and the changing data landscape, as well as offerings from HPE for big data analytics. The third entry in the series is focused on realizing a scalable data lake.

The Changing Data Landscape

This is the second entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the exponential growth of data and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The second entry in the series is focused on the changing data landscape.

Syncsort’s Annual Mainframe Survey Spotlights Big Iron’s Critical Role in Big Data Analytics

Syncsort, a global leader in Big Iron to Big Data solutions, announced the results from its annual “State of the Mainframe” survey, which underscore the rising importance of mainframe data as a critical component of enterprise-wide strategies that leverage modern data architectures for Big Data analytics.

The Exponential Growth of Data

This is the first entry in an insideBIGDATA series that explores the intelligent use of big data on an industrial scale. This series, compiled in a complete Guide, also covers the changing data landscape and realizing a scalable data lake, as well as offerings from HPE for big data analytics. The first entry is focused on the recent exponential growth of data.

How Retailers are Using Big Data to Their Advantage

In this contributed article, tech writer Rick Delgado, discusses how the retail world is jumping on the big data analytics bandwagon. Analytics are being used at every stage of the buying process — from predicting popular products to pricing and figuring out what to sell to customers next. Retailers aren’t holding back on what big data can do for them.

Unisys Survey Finds Airports’ Investment in Analytics Technology Set to Soar as Digital Transformation Needs Take Flight

New research from Unisys Corporation (NYSE: UIS) reveals that airports expect to fully embrace more advanced analytics solutions in the coming years, with potential applications for use across several key areas within the airport ecosystem. Unisys conducted the survey of 29 airport executives and industry professionals at the Airports Council International – North America Annual Conference and General Assembly held in Montreal, September 25-28, 2016.

Architecting for Access: Simplifying Analytics on Big Data Infrastructure

In this special technology white paper, Architecting for Access: Simplifying Analytics on Big Data Infrastructure, you’ll learn how designing systems for data analytics is a bit of a trapeze act—requiring you to balance front-end convenience and access without compromising on back-end precision and speed.

Big Data Makes a Marketing Promise that Drives Everlasting Value to Businesses Everywhere

In this contributed article, tech writer Linda Gimmeson tackles the important topic of big data marketing and how it comes down to providing a more personalized experience for customers.