As can be seen in Figure 1, Teradata supports a wide variety of languages for analytic purposes, most notably Python and R, though not Scala yet. Jupyter Notebooks are also supported. The company also offers an AnalyticOps Accelerator, a software and services offering comprised of best practices, proven design patterns, and tried-and-tested code, which jointly enable the client to implement an AnalyticOps Framework to support robust and continuous operationalisation of analytics. These assets have been taken from successful real-world projects and Teradata services, and genericised so that they may be reused and customised to other client settings. The services and technical IP of the AnalyticOps Accelerator are designed to help organisations get up and running with AnalyticOps and model management very quickly, accelerating deployment and increasing return on investment. While the company has told us that it intends to productise some or all of its AnalyticOps capabilities, it has not done so yet.

Figure 2 - Example of Teradata Vantage's 4D Analytics
As can be seen in Figure 2 Teradata also offers both graph capability and Advanced SQL, as well as support for machine learning.
As far as “Advanced SQL” is concerned, this represents Teradata’s extension to SQL that supports advanced analytic functions and machine learning at scale. Time-series and temporal functions along with the company’s geo-spatial support are combined in what the company calls “4D Analytics”, as illustrated in Figure 2.
With respect to time-series, Teradata provides a Primary Time Index with data stored either by time, by column, or both. The data is organised into time buckets, or you can store it by, say, sensor ID. The former means that you don’t need to perform whole table scans. Round robin parallelism is provided, both to improve performance and prevent skew. Aggregation functions supported by Advanced SQL include statistical capabilities (mean, model standard deviation and so forth) as well as functions such as first, last, top, bottom and so on.
Alongside time-series and temporal capability, Teradata supports all the geo-spatial capabilities you would expect. Supported capabilities include points, curves and polygons, as well as functions such as measurements (distance, surface, perimeter), relationships (intersects, contains and so on), transformation and operation functions, and attribute abstraction such as the number of points within an area.

Figure 3 - Industry and analytic models offered by Teradata Vantage
Going beyond 4D Analytics, Teradata also offers a wide range of industry and analytic models. Some examples of the latter are shown in Figure 3.