Teradata Vantage Overview - Teradata Vantage

Teradata® Vantage User Guide

Product
Teradata Vantage
Release Number
1.0
Published
January 2019
Language
English (United States)
Last Update
2020-03-11
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B700-4002
lifecycle
previous
Product Category
Teradata Vantage

Emerging use cases of advanced analytics and explosion of various types and sources of data are compelling data scientists to leverage varied data science techniques. These techniques include various user languages, such as SQL, Python, R, Scala, and so on, as well as various analytic technologies, such as SQL, Machine Learning, Graph Analytics, Neural Net Analytics, and so on, combined with multi-genre analytics that spans statistical, text, machine learning and artificial intelligence.

The need for operationalization at the speed of business adds another layer of complexity, which can be critical for data-powered and analytics-driven decision making. However, it has to be an evolutionary methodology, since starting from ground-up is not an option in constantly moving businesses.

In order to address these challenges and to deliver evolutionary methodology with revolutionary results, Teradata® delivers Teradata Vantage—an advanced analytics platform.

Teradata Vantage is a modern analytics platform that combines open source and commercial analytic technologies together to operationalize insights, solve complex business problems, and enable descriptive, predictive and prescriptive analytics, that lead to autonomous decision-making.

Teradata Vantage delivers the best analytic functions and engines, preferred tools and languages and support of multiple data types, which are supported by industry-leading database.

As a unified analytic and data framework, Teradata Vantage contains a cross-engine orchestration layer that pipelines the right data and analytic request to the right analytic engine across a high-speed data fabric.

Teradata Vantage embeds analytic engines close to the data. This eliminates the need to move data, allowing users to run analytics against larger data sets without sampling, and run models with greater speed and frequency.