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Analyze Anything with Teradata Analytics Platform

The Teradata Analytics Platform combines commercial and open source advanced analytics, including AI technologies, with the most powerful database available.

Features include:

  • Analytic functions
  • Analytic engines
  • Analytic tools and languages
  • Multiple data types

 

Analytic Functions

Operationalizing business-critical analytics requires robust scalability and elasticity. Teradata offers scalability on multiple dimensions of data, users, and performance. Teradata Analytics Platform embeds advanced analytics directly with the data and leverages the massively parallel processing (MPP) of the Teradata architecture.

The Teradata Analytics Platform embeds time series and Aster’s path, sessionize, text, attribution, and scoring analysis directly in the database. In-database analytics accelerates processing by running analytics in parallel on all data and eliminates the need to move data across systems.

 

Analytic Engines

Teradata Analytics Platform integrates the Aster engine, allowing data scientists and business analysts to execute more than 100 prebuilt advanced analytics techniques including graph, pattern, path, text, machine learning, and statistics to prepare and analyze data within a single workflow, at both speed and scale. By integrating data and analytics engines within a single platform we eliminate data movement between systems, allowing analysts to quickly iterate and refine their analysis. Future versions will incorporate Spark and TensorFlow engines to provide quick, easy access to algorithms for AI and Deep Learning. Data scientists will be able to easily combine analytics from Spark, TensorFlow, and Aster engines within a single workflow.

aster analytic functions

 

Analytic Tools and Languages

Teradata Analytics Platform provides easy access to a range of commercial and open source analytic technologies and programming languages. Python, R, or SQL savvy analysts can quickly, easily access and analyze data in their language of choice. Data scientists can explore, prototype, build, and deploy analytics on leading workbenches and platforms including Jupyter, RStudio, KNIME, SAS, and Dataiku.

Teradata® AppCenter allows analysts to share analytic applications with peers by deploying models in a web-based interface, giving self-service access to business users. Teradata Data Labs enables flexible experimentation and analysis without impacting existing workloads.

 

Multiple Data Types

Advanced analytics solutions commonly focus on algorithms, but supporting diverse data types to solve a variety of use cases is equally important. Teradata allows businesses to ingest and analyze advanced data types such as text, spatial, temporal, CSV, and JSON formats, including support of Avro, an open source data type that allows programs to dynamically process schemas. Additionally, Teradata includes advanced time series data handling for rapid ingest and analytics of IoT data.

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