Live datastore transformation for optimizing big data applications in cloud environments

Vendor lock-in is one of the major issues preventing companies from moving their big data applications to the cloud or changing between cloud providers. A choice in provider based on used datastores can be advantageous at first, but with ever-changing applications the chosen datastore may no longer be optimal after some time. Namely, applications´ requirements change due to frequent updates and feature requests, and scalability issues arise as user numbers continuously evolve. In this paper we propose a framework for the live transformation of the schema and data of datastores. Using a canonical data model the framework can be easily extended for additional datastores. The framework performs the transformation on two different levels. It uses a batch layer to transform a snapshot of the datastore, while a speed layer transforms queries inserting new or updated data into the datastore. A transformation is given between MySQL and Cassandra as a proof-of-concept. We show the correctness of the transformation and provide performance results, in terms of transformation times and overhead.