Teradata Vantage Overview | Teradata Vantage - 2.2.2 - Teradata Vantage Overview - Teradata Vantage - Teradata Vantage on AWS

Teradata Vantage™ User Guide

Teradata Vantage
Teradata Vantage on AWS
Release Number
Release Date
April 2021
Content Type
User Guide
Publication ID
English (United States)

Emerging advanced analytics and new data types require data scientists to leverage a variety of data processing techniques. These techniques include user languages, such as SQL, R, and Python, as well as various technologies, such as Artificial Intelligence, Machine Learning, and Graph Analytics.

There are significant challenges to implementing these technologies in production. For example, AI/ML requires substantial upfront data preparation before model creation. The problem is that the data is often located on different systems and requires significant data movement to bring the data to the analytics. This results in increased time and data duplication. After modeling, the data is then moved back into the database for production. This overall process is inefficient and can lead to increased costs and long development times.

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 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 an 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.