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In-Memory Data Management
(2012)
Nach 50 Jahren erfolgreicher Entwicklunghat die Business-IT einen neuenWendepunkt erreicht. Hier zeigen die Autoren erstmalig, wieIn-Memory Computing dieUnternehmensprozesse künftig verändern wird. Bisher wurden Unternehmensdaten aus Performance-Gründen auf verschiedene Datenbanken verteilt: Analytische Datenresidieren in Data Warehouses und werden regelmäßig mithilfe transaktionaler Systeme synchronisiert. Diese Aufspaltung macht flexibles Echtzeit-Reporting aktueller Daten unmöglich. Doch dank leistungsfähigerMulti-Core-CPUs, großer Hauptspeicher, Cloud Computing und immerbesserer mobiler Endgeräte lassen die Unternehmen dieses restriktive Modell zunehmend hinter sich. Die Autoren stellen Techniken vor, die eine analytische und transaktionale Verarbeitung in Echtzeit erlauben und so dem Geschäftsleben neue Wege bahnen.
The importance of reporting is ever increasing in today's fast-paced market environments and the availability of up-to-date information for reporting has become indispensable. Current reporting systems are separated from the online transaction processing systems (OLTP) with periodic updates pushed in. A pre-defined and aggregated subset of the OLTP data, however, does not provide the flexibility, detail, and timeliness needed for today's operational reporting. As technology advances, this separation has to be re-evaluated and means to study and evaluate new trends in data storage management have to be provided. This article proposes a benchmark for combined OLTP and operational reporting, providing means to evaluate the performance of enterprise data management systems for mixed workloads of OLTP and operational reporting queries. Such systems offer up-to-date information and the flexibility of the entire data set for reporting. We describe how the benchmark provokes the conflicts that are the reason for separating the two workloads on different systems. In this article, we introduce the concepts, logical data schema, transactions and queries of the benchmark, which are entirely based on the original data sets and real workloads of existing, globally operating enterprises.