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We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding. Low-dimensional embeddings aim to encapsulate a concise vector representation for an underlying dataset with minimum loss of information. Embeddings across entities in a relational database have been less explored due to the intricate data relations and representation complexity involved. Relational databases are an inter-weaved collection of relations that not only model relationships between entities but also record complex domain-specific quantitative and temporal attributes of data defining complex relationships among entities. Recent methods for learning an embedding constitute of a naive approach to consider complete denormalization of the database by materializing the full join of all tables and representing as a knowledge graph. This popular approach has certain limitations as it fails to capture the inter-row relationships and additional semantics encoded in the relational databases. In this paper we demonstrate; a better methodology for learning representations by exploiting the underlying semantics of columns in a table while using the relation joins and the latent inter-row relationships. Empirical results over a real-world database with evaluations on similarity join and table completion tasks support our proposition.