Gartner Research Guide

Market Guide for In-Memory DBMS

09 December 2014

Analyst(s): Roxane Edjlali | Ehtisham Zaidi | Donald Feinberg

The extreme performance promise and transformational business potential of in-memory DBMSs have made the technology very attractive. Information leaders and database managers should use this research to understand the latest in-memory database management systems (IMDBMS) developments and the use cases.

In this guide, you will learn:

• Why In-memory computing is growing in popularity and adoption
• Categorizations of IMDBMSs and the three major use cases they support
• New and untapped business opportunities emerging from hybrid transactional and analytical processing (HTAP)
• How to jump ahead of the competition with Gartner recommendations for effective use of IMDBMS

Gartner predicts “that the IMDBMS market momentum will accelerate through 2015 and 2016,” noted Roxane Edjlali, Research Director, Ehtisham Zaidi, Contributing Analyst and Donald Feinberg, vice president and Distinguished Analyst at Gartner in the report, “Market Guide for In-Memory DBMS.” They continued, “The growing number of high performance, response-time critical and low-latency use cases (such as real-time repricing, power grid rerouting, logistics optimization), which are fast becoming vital for better business insight, require faster database querying, concurrency of access and faster transactional and analytical processing. IMDBMSs provide a potential solution to all these challenging use cases, thereby accelerating its adoption.”

Gartner is “seeing the emergence of a new class of In-Memory Computing solutions that we call Hybrid Transactional and Analytical Processing (HTAP), supporting both analytical and transactional workloads.” In a complementary report titled “Hybrid Transaction/Analytical Processing Will Foster Opportunities for Dramatic Business Innovation,” by analysts Massimo Pezzini, Donald Feinberg, Nigel Rayner, and Roxane Edjlali, Gartner identified how HTAP architectures address the four major drawbacks of traditional approaches.

1. In HTAP, data doesn’t need to move from operational databases to separated data warehouses/ data marts to support analytics. This addresses architectural and technical complexity.
2. The transactional data of HTAP applications is readily available for analytics when created. This addresses analytic latency.
3. In HTAP, drill-down from analytic aggregates always points to the “fresh” HTAP application data. This addresses synchronization between separate analytical and transactional data storage.
4. In HTAP, the need to create multiple copies of the same data is eliminated (or at least reduced). This addresses data duplication.

In the “Market Guide for In-Memory DBMS” report, analysts Roxane Edjlali, Ehtisham Zaidi, Donald Feinberg state that, “HTAP will enable business leaders to perform, in the context of operational processes, much more advanced and sophisticated real-time analysis of their business data than with traditional architectures.”

Readers interested in the full Gartner Report, Market Guide for In-Memory DBMS, may visit memsql.com/gartner to view a copy (registration required).

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