Data Warehouse Disruptions 2016: Gartner Magic Quadrant - InformationWeek

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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.

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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.

Data Warehouse Trends

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.

(Image: arcoss/iStockphoto)

(Image: arcoss/iStockphoto)

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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Last, Gartner said best-of-breed is being replaced by something it calls "best-fit engineering," an approach that lets organizations choose the least-required technology for each function and enables adding new pieces to the stack as they make sense.

Data Warehouse Leaders

The changes in the market have meant some shuffling of vendors in Gartner's four quadrants this year. The Leaders Quadrant lost one of its members, HP, as a result. HP placed in the Leaders Quadrant in 2015, before its organization was officially split in November.

HP Enterprise, one of the new companies created after the HP split, has moved to the Challengers Quadrant of this year's report. Vendors in the Leaders Quadrant in both 2015 and 2016 are Oracle, Teradata, Microsoft, IBM, and SAP.


The Challengers Quadrant also saw some vendor shuffling.

Two companies that had 2015 placement in this quadrant -- Amazon Web Services (AWS) and 1010data -- remain in this quadrant this year. AWS offers Redshift, a cloud-based data warehouse, among other services, but Gartner cautioned that all the other major vendors, including IBM, Microsoft, Oracle, SAP, and Teradata, now actively compete with AWS in the cloud with varying degrees of support for true data warehouse platforms as a service.

Two newcomers, MarkLogic and Infobright, moved up from the Niche Players square in 2015 to the Challengers Quadrant this year. And a few companies moved from the Challengers Quadrant to the Visionaries Quadrant, including Cloudera and MapR, the two Hadoop distribution companies that were included in last year's Data Warehouse Magic Quadrant report. That means their "ability to execute" score declined even as their "completeness of vision" ranking improved.


Cloudera and MapR were kept company in the Visionaries Quadrant by the third big Hadoop distribution company, Hortonworks, which is a newcomer to this report. Gartner noted that it did not receive an information submission from Hortonworks, but gathered information about the company in other ways, including briefings.

Two additional companies debuted in this report in the Visionaries Quadrant this year -- Transwarp and MemSQL. Pivotal remained in the Visionaries Quadrant, where it also appeared last year.

Niche Players

Niche Players included MongoDB, which made its debut in the report this year. Actian moved into this quadrant this year, from its position in the Visionaries Quadrant in 2015. And Exasol, Kognitio, and Hitachi remained in the Niche Players Quadrant, where they also placed last year.

"Data and analytics leaders must be aware of the market's evolution and prepare hybrid technology platforms that expand the data warehouse beyond any current practice," Gartner said in its report.

Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: ... View Full Bio

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User Rank: Apprentice
11/15/2016 | 10:28:21 AM
Selective Cloud Usage

   My thought is that persistence of large volumes of data in the cloud is more expensive than on premise, especially if the growth in data is consistent, that is you know in general how your data is growing. With many large organizations having their own on premise clouds, or fabrics, offering the same elasticity - this is even more valid. I also realize that processing, that is ETL/ELT plays into the picture as well. 

   Where I see a strong advantage for cloud is in serving up summary data, for spinning up quick data marts, and in cases where the volume is small and change is uncertain. 

   The idea of a logical data warehouse really comes into play when you have a mixed persistence solution. If all your data warehouse data is in one place, be it an old school RDBMS, or RDBMS in the cloud, then the extisting tools around metadata and data governance should be fine. 

   At some point, we'll all drive electric cars, and all computing will be in the cloud, but we're not there yet, and we need to be judicious about our resources, and our approach in adopting technology. 

   It seems to me, our technology experts are marketers as much as well, anything else. We need more informed articles that help us data and enterprise architects drive out the right decisions.



User Rank: Strategist
3/4/2016 | 10:21:42 AM
"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."

Yes, this is a trend that is being felt across the board. There's a good infographic that shows the state of cloud analytics here:

From that report: 71 percent of organizations surveyed said they would be adopting cloud anaytics over the next three years. That definitely has the ability to turn the entire industry on its ear.


Karen Bannan, commenting on behalf of IDG and Infomatica



Karen J. Bannan, commenting on behalf of IDG and Infomatica.
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