Providing quality-aware techniques for reusing data available on the Web is a major concern for today's organizations. High quality data that offers higher added-value to the stakeholders is called smart data. Smart data can be obtained by combining data coming from diverse data sources on the Web such as Web APIs, SPARQL endpoints, Web pages and so on. Generating smart data involves complex data processing tasks, typically realized manually or in a static way in current organizations, with the help of statically configured workflows. In addition , despite the recent advances in this field, transfering large amounts of data to be processed still remains a tedious task due to unreliable transfer conditions or transfer rate/latency problems. In this paper, we propose an adaptive architecture to generate smart data, and focus on a solution to handle volume diversity during data processing. Our approach aims at maintaining good response time performance upon user request. It relies on the use of RESTful resources and remote code execution over temporary data storage where business data is cached. Each resource involved in data processing accesses the storage to process data on-site.