Teradata

Teradata supports a wide variety of languages for analytic purposes, most notably Python and R, as well as Jupyter Notebooks. It also offers an AnalyticOps Accelerator, a collection of best practices, proven design patterns, and tried-and-tested code derived from successful Teradata projects, that is designed to make it much easier for you to implement an AnalyticsOps framework.

The platform offers graph capability, support for machine learning, and access to “Advanced SQL”, Teradata’s extension to SQL in support of advanced analytics and machine learning at scale. This includes time-series and temporal functions that, along with comprehensive geo-spatial support, are combined in what Teradata calls “4D Analytics”.

Teradata also provides a wide range of industry and analytic models.

All of the capabilities described above are delivered in the cloud just as they are on-premises, allowing Teradata Vantage to be used as a highly effective cloud data analytics platform. It can be deployed across a number of clouds, including the “big three” of AWS, Microsoft Azure, and Google Cloud, as well as VMware, Teradata Cloud, and purpose-built on-premises infrastructure.

Multi-cloud, hybrid cloud, and hybrid multi-cloud are all supported, and in terms of the cloud the product is generally delivered as a service. Any number of instances can be viewed, managed and monitored using the same web interface, which is particularly important for multi-cloud. In addition, Teradata Vantage is a highly portable solution (including its licensing – see below), largely owing to the fact that it uses the same software across all platforms. Teradata also provides data migration tools and best practices, and combined with the company’s experience with cloud migrations, moving an instance from one cloud to another should be a relatively painless process.

Fig 01 - Teradata Vantage ‘Big 3’ cloud integrations

What’s more, the platform’s relationship with these clouds reaches significantly beyond just deployment.

In fact, Teradata Vantage is closely aligned with all three major clouds, and boasts integration with a wide variety of Microsoft, Amazon and Google cloud services, as shown in Figure 1. Moreover, Teradata Vantage offers a software architecture (see Figure 2) that is well suited to the cloud, which helps to take advantage of the aforementioned cloud services provided by Azure, AWS and GCP.

Fig 02 - Teradata Vantage cloud architecture

Pricing models for Teradata Vantage in the cloud are flexible, consisting of both capacity-based pricing (‘blended’) and pay-as-you-go usage-based pricing (‘consumption’). The latter, in particular, provides automatic elasticity, and you only pay for successful queries and loads. It also boasts highly trackable usage statistics (which makes for predictable pricing), and departmental chargeback, among other things. In short, you are able to choose whichever pricing model suits your needs, most likely in terms of high or low (or unknown) utilisation. Also notable is that all Teradata Vantage cloud deployments are single-tenant, which has benefits for both performance and stability, and that Teradata’s pricing models and licenses are portable across cloud (and even on-premises) environments.

Teradata Vantage also offers separation of storage from compute, which helps support these pricing models (particularly consumption pricing). That said, it is not strictly required. Elastic scaling, dynamic resource allocation, software performance optimisation (via indexing and determining least-cost execution methods), and workload management are all available, and all contribute to performance in one way or another. Dynamic resource allocation also addresses data replication and data drift, as well as query prioritisation more complex than ‘first come, first serve’.

Teradata has been the gold standard for data warehousing for several decades. While new challengers have emerged over the last few years, they have neither the breadth nor the depth that Teradata can offer. While machine learning support is increasingly common currency other vendors cannot typically compete with the capabilities offered by 4D Analytics. This will be particularly true within IoT (Internet of Things) environments but is by no means limited to those use cases.

As a cloud solution, Teradata admixes its signature, widely-regarded analytics with the benefits of the cloud. This most notably includes increased flexibility, scalability, and ease of use. Starting with a modest Teradata cloud deployment, paying only for what you use and scaling up precisely as much as you need to also removes much of the onboarding difficulties that often come with a solution as broad and fully-featured as Teradata, while at the same time reducing risk and hastening time to value.

The Bottom Line

Teradata Vantage is well-known for its performance, scalability, high availability and reliability. These qualities are retained, and in some cases enhanced, in cloud deployments. In short, with its strong history, proven use cases and exceptional capabilities, Teradata Vantage should be considered for any cloud and/or on-premises analytic database requirements.

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.

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.

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.

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.

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.

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.