Teradata

Last Updated: 28th February 2020
Analyst Coverage: Philip Howard

Teradata was first incorporated in 1979, in the days when a “tera” byte of data was “big data”. It released the world’s first parallel data warehouse in 1984 and has been a (if not the) leader in the data warehousing space ever since. It became a public company in 1987 but was acquired by NCR four years later. Thereafter it regained independence from NCR in 1997 and, once again, became a public company in 2007. The company has made a number of significant acquisitions over the last decade, including Claraview, Aprimo, Aster Data Systems and others. In 2014 it acquired Think Big Analytics, which now forms the heart of Teradata’s consulting practices around big data, including AnalyticOps. In late 2017, spun off Starburst as a company focused on the Presto SQL Engine.

Company Info

Headquarters: 17095 Via Del Campo, San Diego, CA 92127 USA
Telephone: +1 858 485 4000

Teradata Vantage 4D

Last Updated: 28th February 2020
Mutable Award: Platinum 2019

What is it?

Fig 01 Teradata architecture

Teradata Vantage is the latest offering from Teradata, whose architecture is illustrated in Figure 1, where the elements shown in light blue are currently provided by Teradata and those in dark blue represent potential areas of expansion. For example, Teradata is actively working on supporting storage in third-party object stores such as S3 (this has been announced). As far as this InBrief is concerned, however, we are not going to discuss Teradata Vantage in detail but focus on its support for what the company calls “4D Analytics”. This is provided primarily by what is shown on the diagram as “Advanced SQL” though also, to a lesser extent, by the machine learning and graph capabilities provided by Vantage.

How does it work?

Fig 02 Teradata Advanced SQL support

By 4D Analytics Teradata means geo-spatial capabilities, which are three-dimensional, plus time, which is the fourth dimension, with time-based support including both time-series and temporal capabilities. Advanced SQL represents Teradata’s extension to SQL (for example, GROUPBY TIME) to support the 4D functions shown in Figure 2.

Starting with 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, mode, standard deviation and so forth) as well as functions such as first, last, top, bottom and so on.

Temporal data differs from time-series data to the extent that temporal data is fixed for a particular period of time. Temporal data has a start and end date, even if the latter may be putative rather than actual. The most obvious use cases include demographics and many things associated with human activity, for example, tracking and analysing emergency vehicle response times to do trend analysis, or in logistics. Auditing (and not just financial auditing) and compliance are other environments that lend themselves to temporal analytics, especially where you need to look backwards in time. For example, when you need to distinguish between transaction time and valid time. In analytic environments a further example would be when a product, say, is moved from one product group to another. Teradata supports all of these use cases. Competitive offerings may be able to support the product change scenario via slowly changing dimensions (which you can do with Teradata also) but cannot support the “point in time” queries that 4D Analytics supports.

Finally, 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.

As one might expect you can use any of the leading business intelligence, visualisation, and analytics tools, as well as notebooks and IDEs along with 4D Analytics.

Why should you care?

Fig 03 Example of bi-temporality

Time-series analytics are essential for many predictive and prescriptive analyses as well as such things as fraud detection. Many of these use cases – especially in IoT and the industrial sector – also require geo-spatial capability to be combined with the time dimension. Temporal processing allows bi-temporal queries – for example, see Figure 3 – in a user-friendly fashion that doesn’t require knowledge of effective data fields and/or complex condition clauses.

There are plenty of vendors that have geo-spatial capabilities. There are some that have support for time-series, though only a minority of these offer sophisticated analytic functionality. There are a few that have temporal capabilities though all of these except for Teradata are primarily operational/transactional databases rather than analytic. What that adds up to is that Teradata Vantage is the only product that we are aware of that has all the 4D capabilities that you might need. Perhaps the one caveat that we would make is that if you only want the sort of DevOps metric and monitoring solution that some time-series vendors target, then Teradata Vantage would probably be overkill. But if you want to combine that sort of solution with anything more sophisticated then Vantage would certainly be appropriate.

The Bottom Line

Of course, Teradata Vantage covers much more than the 4D Analytics we have discussed. While it has significant advantages when it comes to time-series and/or temporal processing the product has lots of other good things going for it. It should pretty much be on everyone’s shortlist when time-series analytics is at issue.

Mutable Award: Platinum 2019