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.

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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.

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