Earlier this year Gartner announced their 2014 Magic Quadrant for Data Warehouse Database Management Systems – and for the first time ever, Cloudera debuted on the list.
Gartner’s Magic Quadrants serve as one of Gartner’s flagship market research reports, covering dozens of IT trends for both private and public sector technology solutions. The report makes for fascinating reading, and provides some great context into what is happening within the field of data management, and what is expected to come.
Hadoop and its wealth of integrated processing capabilities, like MapReduce and Cloudera Impala, have been touted as new approaches, and in some cases, replacements for traditional solutions. Yet these emerging big data processing approaches, powered by Hadoop, have continued to change the kinds of big data solutions required for government to capitalize on data.
As the market continues to change, Gartner provides some clarity on how they define data warehouse database management systems. Gartner notes that data warehouse database management systems are:
- Complete software systems that support and manage a database or databases in some form of storage medium (which can include hard-disk drives, flash memory, and solid-state drives or even RAM).
- Systems that can perform relational data processing and can extended to support new structures and data types, such as XML, text, documents, and access to externally managed file systems.
- Support data availability to independent front-end application software, include mechanisms to isolate workload requirements and control various parameters of end-user access within managed instances of the data.
You can view the entire Gartner report here. The report includes many interesting insights on data warehousing and the future outlooks for the field. Three noteworthy observations from Gartner include:
- The demand for preprocessing of big datasets will require architectures that combine data integration, data management for analytics and high-volume batch analysis. Traditional vendors will respond by enhancing their runtime management capabilities to combine disparate engineering approaches, either organically or through acquisition. This will increase the demand on traditional vendors for superior processing management and bigger processing capacity.
- The demand for new data in analytics, and new combinations of data, will drive the organic growth of existing solutions and architectures.
- Cloud data warehousing options are becoming viable alternatives for organizations, especially for greenfield implementations. They will lower barriers to entry, but do not necessarily guarantee cost savings.
Because of these trends, to truly innovate and capitalize on data, agencies must continue to seek solutions that can safely and securely process multiple kinds of data, and be housed in a single, cost-effective, and scalable repository. In doing so, agencies can witness the benefits of centrally managing data. These include the ability to share resources, have minimal data movement, mitigate synchronization issues and improve the provisioning of new data.
This positions Cloudera well to continue to advance in the quadrant, especially as Cloudera continues to support the enterprise data hub (EDH) architecture. With the EDH, agencies can store data in its original fidelity, integrate with existing infrastructures and support the flexibility needed for various kinds of workloads – such as batch processing, interactive SQL, enterprise search and advanced analytics.
With the ability to collect multiple kinds of data from disparate systems, the EDH is a powerful tool to help agencies unlock insights.
The graphic used in this blog was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Cloudera.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Magic Quadrant for Data Warehouse Database Management Systems, Mark A. Beyer, Roxane Edjlali, 7 March 2014.
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