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TDWI Checklist Report | Preparing Your Organization for In-Memory Computing

August 7, 2014

The “decision support system” concept that emerged more than 20 years ago has evolved into the modern data warehouse (DW) that is now ubiquitous in any enterprise information architecture. However, early designers, driven by the need to preserve online transaction processing (OLTP) response times, made a practical engineering decision to replicate transaction data to a segregated platform that would house the reporting and business intelligence (BI) systems.

The conventional wisdom for BI and analytics insists on an intricate infrastructure that iteratively extracts source data sets, applies transformations, and loads the data into a segregated environment. The benefit of system segregation to protect performance is offset by the overhead and complexity associated with data integration, which in turn has contributed to skyrocketing costs for acquiring and maintaining the data warehouse ecosystem’s hardware and software.

Fortunately, in-memory database management systems promise accelerated application performance balanced by increased systemic simplicity. Combining alternative storage layouts with in-memory processing allows applications to take advantage of efficient use of available memory and reduce or even eliminate the data latencies typically associated with significantly slower disk-based storage media.

If reporting and analytics systems are still dissociated from data sources, the performance enhancements gained by keeping the data in main memory diminish as extraction and loading latencies are reintroduced. However, as RAM and cache memory costs plummet and massive multiprocessor systems enter the mainstream, it makes sense to leverage in-memory computing systems such as SAP HANA to fully integrate transaction processing, operational processing, and reporting and analytics within the same in-memory platform.

This TDWI Checklist Report will help your organization prepare for in-memory computing by discussing how it improves application performance. In-memory computing enables the peaceful coexistence of transaction processing and analytics without demanding data extraction, transformations, rampant replication, and heterogeneous computing platforms. We examine the impact of in-memory computing on the corporate data footprint and how the information technology landscape is made simpler.

A transition to in-memory computing makes information management easier by freeing hardware and staff resources for productive use, inspiring disruptive innovation that enables easier application development, improved user experience, and general increase in corporate value.

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