Databases are restricted by reliance on disk-based storage, a technology that has been in place for several decades. Even with the addition of memory caches and solid state drives, the model of relying on repeated access to information storage devices remains a hindrance in capitalizing on today’s “big data,” according to a new survey of 323 data managers and professionals who are part of the Independent Oracle Users Group (IOUG). The survey was underwritten by SAP Corp. and conducted by Unisphere Research, a division of Information Today, Inc.
The survey finds that today’s data warehouse environments are not keeping up with the sharp growth in data volume and demand for real-time analytics. Fewer than one out of 10 data warehouse sites in the survey, for example, can deliver analysis in what respondents would consider a real-time time frame. And, overall, managing existing database and data warehouse environments is time-consuming for both administrators and end users.
In-Memory Speeds Data Access and Analysis
In-memory technologies promise to significantly speed up repetitive access and analyze information, achieved by loading complete datasets into computers’ random access memory. The technologies not only can increase the speed of data processing by orders of magnitude, but they also can facilitate program changes to increase the freshness of data, as well as the cost-effectiveness of data systems.
However, while speed is the most compelling business case to be made for in-memory technology, according the study, data executives and professionals are also seeking to expand their analytics capabilities, as well as to enhance their ability to manage big data. The top benefits and innovations of in-memory technology, according to respondents, are improvement in query response time (70%), reduction in data latency (52%), accelerated access to detailed data (47%), and management of increasing data volume or big data (45%).
Important Benefits and Innovations of In-Memory Technology (Source: Accelerating Enterprise Insights: 2013 IOUG In-Memory Strategies Survey)
Improvement in query response time 70%
Reduction in data latency (i.e., real-time analytics) 52%
Accelerated access to detailed data 47%
Management of increasing data volume (i.e., big data) 45%
In-database advanced analytics (i.e., predictive analytics) 36%
Analytic capabilities for unstructured data 33%
Elimination of performance tuning (i.e., aggregates, indices) and/or duplicate data/systems 29%
Reduction in ongoing IT costs 26%
Single data store (i.e., no data movement) for multiple data engines (i.e., OLTP, OLAP, text, graphic, spatial) 22%
Simplified administration and increased IT agility for new/changing user requirements 22%
(Multiple responses permitted.)