OLTP DBMSs in enterprise scenarios are often facing the challenge to deal with workload peaks resulting from events such as Cyber Monday or Black Friday. The traditional solution to prevent running out of resources and thus coping with such workload peaks is to use a significant over-provisioning of the underlying infrastructure. Another direction to cope with such peak scenarios is to apply resource sharing. In a recent work, we showed that merging read statements in OLTP scenarios offers the opportunity to maintain low latency for systems under heavy load without over-provisioning. In this paper, we analyze a real enterprise OLTP workload - - SAP Hybris - - with respect to statements types, complexity, and hot-spot statements to find potential candidates for workload sharing in OLTP. We additionally share work of the Hybris workload in our system OLTPShare and report on savings with respect to CPU consumption. Another interesting effect we show is that with OLTPShare, we can increase the SAP Hybris throughput by 20%.