Scaling complex transactional workloads in parallel and distributed systems is a challenging problem. When transactions span data partitions that reside in different nodes, significant overheads emerge that limit the throughput of these systems. In this paper, we present a low-overhead data partitioning approach, termed JECB, that can reduce the number of distributed transactions in complex database workloads such as TPC-E. The proposed approach analyzes the transaction source code of the given workload and the database schema to find a good partitioning solution. JECB leverages partitioning by key-foreign key relationships to automatically identify the best way to partition tables using attributes from tables. We experimentally compare our approach with the state of the art data-partitioning techniques and show that over the benchmarks considered, JECB provides better partitioning solutions with significantly less overhead.