SAN DIEGO, Aug. 8, 2016 /PRNewswire/ -- Gartner has given Teradata the highest product scores in three of the four data warehouse use case categories (Traditional, Logical and Context-Independent) in its new report, "Critical Capabilities for Data Warehouse and Data Management Solutions for Analytics," issued July 13, 2016 by analysts Rick Greenwald, Mark A. Beyer and Roxane Edjlali. Teradata Corp. (NYSE: TDC), the big data analytics company is one of 19 database vendors evaluated in the new report, which is a companion note to the Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics, published February 25, 2016, where Teradata is also positioned as a leader.

"Teradata's high scores in these use cases is great news for Teradata and our global customer community – and reflect on our powerful and progressive technology leadership at a time when the marketplace is more crowded than ever," said Oliver Ratzesberger, Executive Vice President and Chief Product Officer. "We introduced the first analytical ecosystem to deliver on what Gartner defines as a logical data warehouse with our Teradata® Unified Data Architecture™ including Teradata Aster Analytics and our Hadoop portfolio. By leading in critical capabilities for the Logical Data Warehouse, Traditional, and the Context-Independent Data Warehouse -- our customers' continuing confidence in Teradata is well-justified and, looking ahead, we are excited about the next wave of innovations we will introduce."

Gartner defines "Critical Capabilities" as "attributes that differentiate products/services in a class in terms of their quality and performance." The research note evaluates vendors in the context of four data warehouse use cases: 1) Traditional 2) Operational 3) Logical and 4) Context-Independent. Gartner defines a data warehouse as "a solution architecture that may consist of many different technologies in combination."

Based on Teradata's frequent meetings with industry consultants, a growing number of Teradata key reference accounts are augmenting data warehouses with Aster Analytics and Hadoop, demonstrating adoption of ecosystems that resemble the logical data warehouse and context-independent data warehouse use cases. Teradata believes that this phenomenon illustrates the convergence of visionary and pragmatic innovations reflected in the cutting-edge accomplishments of Teradata.

"As big data analytics becomes the single most important means of business differentiation and business performance across industries, the critical capabilities evaluated in the new Gartner report provide a valuable framework and rationale for investments in Teradata," said Chris Twogood, vice president of product and services marketing, Teradata. "Our leadership in the recent Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics as well as in a new report pertaining to Big Data Hadoop-Optimized Systems underscores the value and trust that Teradata has earned in every new big data analytics frontier."

To view the complete Gartner Critical Capabilities report go to https://www.gartner.com/doc/reprints?id=1-3BPBB1C&ct=160714&st=sb. About Gartner Research

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 or other designation. 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.

About Teradata

Teradata (NYSE: TDC) helps companies get more value from data than any other company. Teradata's leading portfolio of big data analytic solutions and services can help organizations gain a sustainable competitive advantage with data. Visit teradata.com.

Teradata, Aster, and the Teradata logo are registered trademarks of Teradata Corporation and/or its affiliates in the U.S. and worldwide.

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Gartner defined Data Warehouse Use Cases

Traditional Data Warehouse

This use case involves managing historical data coming from various structured sources. Data is mainly loaded through bulk and batch loading.

The traditional DW use case can manage large volumes of data and is primarily used for standard reporting and dashboarding. To a lesser extent, it is used for free-form querying and mining, or operational queries. It requires high capabilities for system availability and administration and management, given the mixed workload capabilities for queries and user skills breakdown.

Operational Data Warehouse

This use case manages structured data that is loaded continuously in support of embedded analytics in applications, real-time data warehousing, and operational data stores.

This use case primarily supports reporting and automated queries to support operational needs, and will require high-availability and disaster recovery capabilities to meet operational needs. Managing different types of users or workloads, such as ad hoc querying and mining, will be of less importance as the major driver is to meet operational excellence.

Logical Data Warehouse

This use case manages data variety and volume of data for both structured and other content data types.

Besides structured data coming from transactional applications, this use case includes other content data types such as machine data, text documents, images and videos. Because additional content types can drive large data volumes, managing large volumes is an important criterion. Logical DW is also required to meet diverse query capabilities and support diverse user skills. This use case supports queries reaching into other sources than the data warehouse DBMS alone.

Context-Independent Data Warehouse

This declares new data values, variants of data form and new relationships. It supports search, graph and other advanced capabilities for discovering new information models. This use case is primarily used for free-form queries to support forecasting, predictive modeling or other mining styles as well as queries supporting multiple data types and sources. It has no operational requirements and favors advanced users such as data scientists or business analysts, resulting in free-form queries across potentially multiple data types.