Data Warehouse Disruptions 2016: Gartner Magic Quadrant

Data Warehouse Disruptions 2016: Gartner Magic Quadrant

Cloud computing, virtualization, and the need to analyze non-relational data types are all driving disruption in the data warehouse market. Here's a look at how traditional and new vendors have shifted their placements in Gartner's Magic Quadrant report for 2016.

Like everything else in IT, the data warehouse market is undergoing a transformation. The forces of cloud computing and virtualization are having an impact on this market, even as organizations are looking to incorporate insights from data that don't fit the traditional relational database model.

Within this environment, Gartner released its 2016 Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics. While this year's report adds four vendors and drops none, there's been some significant shuffling of vendors among the four quadrants. Plus, Gartner provided an overview of four big trends affecting the data warehouse and data management solutions for analytics markets today and going forward.

First, Gartner's report said the definition of the data warehouse is expanding. "The term 'data warehouse' does not mean 'relational, integrated repository,'" Gartner said in its report. Rather, the market now has a much broader definition. It now includes the "logical data warehouse" alongside the traditional enterprise data warehouse. Gartner defines a logical data warehouse (LDW) as a data warehouse that uses repositories, virtualization, and distributed processes in combination. LDWs will become more popular over the next five years, Gartner said. And that brings us to the next trend.

Read Also:
12 Barriers To Real-Time Analytics

Second, Gartner noted that more organizations are considering cloud-based deployments of their analytics environments. This shift will set new expectations for LDWs, Gartner said. It will also disrupt the data warehouse appliance market.

Third, big data and data lakes have altered the market, according to Gartner, with data lakes gaining popularity in 2015. Organizations have relied on a few use cases to get value out of big data with analytics, including data exploration sandboxes. Gartner also said that successful organizations pursuing big data in advanced analytics are typically taking a best-of-breed approach because "no single product is a complete solution." But that approach may also shift in the months ahead.

Learn to integrate the cloud into legacy systems and new initiatives. Attend the Cloud Connect Track at Interop Las Vegas, May 2-6.;

 



HR & Workforce Analytics Summit 2017 San Francisco

19
Jun
2017
HR & Workforce Analytics Summit 2017 San Francisco

$200 off with code DATA200

Read Also:
Will Business Intelligence (BI) Ever Be Consumerized?
Read Also:
Why Embedded Analytics Will Change Everything

M.I.E. SUMMIT BERLIN 2017

20
Jun
2017
M.I.E. SUMMIT BERLIN 2017

15% off with code 7databe

Read Also:
The internet of things and the future of logistics

Sentiment Analysis Symposium

27
Jun
2017
Sentiment Analysis Symposium

15% off with code 7WDATA

Read Also:
Positioning a Machine Learning Company

Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

28
Jun
2017
Data Analytics and Behavioural Science Applied to Retail and Consumer Markets

15% off with code 7WDATA

Read Also:
Free data visualization with Microsoft Power BI: Your step-by-step guide

AI, Machine Learning and Sentiment Analysis Applied to Finance

28
Jun
2017
AI, Machine Learning and Sentiment Analysis Applied to Finance

15% off with code 7WDATA

Read Also:
Artificial Intelligence Poised To Revolutionise Media Agency Structure

Leave a Reply

Your email address will not be published. Required fields are marked *