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Magic Quadrant for Data Management Solutions for Analytics

The data management solutions for analytics market is evolving as the cloud’s position solidifies, use cases for Hadoop clarify, logical data warehouse adoption grows, and Chinese vendors expand abroad. Against this dynamic backdrop, this report will help you find the right vendor for your business.


Market Definition/ Description

We define a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or more file management systems (usually databases). DMSAs include specific optimizations to support analytical processing. This includes, but is not limited to, support for relational processing, nonrelational processing (such as graph processing), and machine learning and programming languages such as Python and R. Data is not necessarily stored in a relational structure, and multiple models can be used — for example, relational, XML, JSON, key-value, text, graph and geospatial.

Although the traditional data warehousing use case remains foundational to most organizations’ analytics initiatives, there is also interest in the ability to manage and process increasingly diverse formats for both internal and external data. A complete DMSA must therefore be able to accommodate a diverse range of data types. These may include interaction and observational data — from Internet of Things (IoT) sensors, for example — as well as nonrelational data, such as text, image, audio and video data.

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