Vertica

Regardless of the mode, Vertica is a massively parallel, columnar database with advanced compression capabilities. It supports workload isolation and uses projections to speed up the performance of frequently run queries. As illustrated in Figure 1, the environment uses external tables to act as a SQL engine with which you can query these other data sources. Thus, you might deploy Vertica simply as a data lake engine or Vertica might be implemented as a conventional data warehouse with federated query capabilities. Note, however that this is data federation rather than data virtualisation, as there is no push-down optimisation, which you would expect from a data virtualisation vendor. Note also that projections do not apply to these external tables.

Figure 2 - Snapshot of supported capabilities

Going beyond traditional data warehousing capabilities, Vertica supports in-database machine learning and analytics, along with support for both R and Python. Rather than provide a list of all the relevant features, Figure 2 provides a snapshot of supported capabilities, though it is not complete. It does not mention, for example, that Vertica supports geospatial data types or that you can run create, train and deploy K-means clustering. It is also worth noting that although Figure 2 shows that you can sample data sets using Vertica, this is merely an option. Indeed, the company regards it as a differentiator that you will rarely need to sample the data when using Vertica.

Two additional resources that are worth discussing are the Vertica Academy and Vertica Advisor. The former is a free e-learning platform (with certifications) while the latter is a tool that, amongst other things, uses machine learning to monitor and advise on query performance. It is currently only available as a part of support engagements but the company plans to introduce further automation into the tool and embed it within the product with capabilities that extend to cluster health, and the database configuration, as well as various performance characteristics. While on the subject of roadmap items it is also noteworthy that Vertica is intending to introduce model management capabilities, along with support for PMML (predictive modelling mark-up language).

There are several notable reasons why you might choose Vertica. The company would claim that performance is one of these though this will depend on the use case. We like the choice between a traditional architecture and one that separates compute from storage. And we especially like the fact that this is available both on-premises and in the cloud. We also appreciate that Vertica can be deployed as a data warehouse, as a SQL engine, and/or as a federation engine spanning multiple (types of) data stores. Another major strength is the in-database analytics and the depth of support for machine learning.

Also notable is Vertica’s success as an embedded database within OEM environments. The fact that Vertica has had significant success in this area is a testament, not only to its capabilities, but also to the robustness of the product and its ease of use.

The Bottom Line

Vertica exists within a crowded market. While it has significant strengths as detailed in the previous section, none of these – with the exception of on-premises storage/compute separation – is unique. It is the combination of all of these factors that makes Vertica attractive.