Performing a detailed workload analysis is a crucial step in determining the feasibility, timeline and cost of a major data warehouse replatforming project, i.e., migration from one platform to another. A large company's data warehouse applications may include millions of queries, some of which will use features that are unsupported or have different semantics in the new warehouse, or may have poor performance there.

In recent years, increased interest in cloud-based data warehousing technologies has emerged with many enterprises moving away from on-premise data warehousing solutions. The incentives for adopting cloud data warehousing technologies are many: cost-cutting, on-demand pricing, offloading data centers, unlimited hardware resources, built-in disaster recovery, to name a few. There is inherent difference in the language surface and feature sets of on-premise and cloud data warehousing solutions. This could range from subtle syntactic and semantic differences, with potentially big impact on result correctness, to complete features that exist in one system but are missing in other systems. While there have been some efforts to help automate the migration of on-premise applications to new cloud environments, a major challenge that slows down the migration pace is the handling of features not yet supported, or partially supported, by the cloud technologies. In this paper we build on our earlier work in adaptive data virtualization and present novel techniques that allow running applications utilizing sophisticated database features within foreign query engines lacking the native support of such features. In particular, we introduce a framework to manage discrepancy of metadata across heterogeneous query engines, and various mechanisms to emulate database applications code in cloud environments without any need to rewrite or change the application code.