Cloud Analytic Databases 2017

Table of Contents

  1. Summary
  2. Introduction and Methodology
  3. Usage Scenarios
  4. Disruption Vectors
  5. Company Analysis
  6. Key Takeaways
  7. About William McKnight


The cloud is proving immensely useful to providing elastic, measurable, on-demand, self-service resources to organizations. The uptake in 2016 has been phenomenal, continuing the biggest transformation that technology professionals will experience in their careers.

Just about any software, including databases, can be placed in a public cloud these days by simply utilizing cloud resources as an extended data center. This may solve an immediate pressing problem, but the opportunities missed without true cloud integration are huge.

Some relational databases have undergone significant cloud-related development in their latest releases. Those will be the focus of this Sector Roadmap, along with the databases built native for the cloud.

We will also only examine true databases as in relational databases, not NoSQL or Hadoop data stores, which are much more integrated with the cloud, but serve mostly different purposes from databases. The platform category distinction should be made first and this report is written to the audience that has workloads meant for databases.

This Sector Roadmap will not develop the value proposition for the cloud itself. The cloud is important enough, and there is enough diversity of offerings, to be a given in a data platform selection process in 2017-18 and beyond.

This Sector Roadmap is focused on analytical databases and not operational and transactional databases. The market is well acquainted with this distinction in database offerings, which not only remains strong but has grown in 2016. Companies who mismatch workloads and databases pay a performance and usability price for that decision.

Vendor solutions are evaluated over six Disruption Vectors: Robustness of SQL, Built-in optimization, On-the-fly elasticity, Dynamic Environment Adaption, Separation of compute from storage, and Support for diverse data.

Key findings in our analysis include:

  • Due to the economics and functionality, use of the cloud can now be a given in most database selection in 2017 and beyond.
  • Several offerings have been able to leapfrog databases with much more history by being “born in the cloud” and tightly integrating with it through On-the-fly elasticity, Dynamic Environment Adaption, and Separation of compute from storage.
  • While traditional database functionality is still required, cloud dynamics are causing the need for more Robustness of SQL, Support for diverse data and other capabilities that may not be present in traditional databases.



  • Number indicates company’s relative strength across all vectors
  • Size of ball indicates company’s relative strength along individual vector

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