In this paper we propose SparkBLAST, which uses the support of Apache Spark to parallelize and manage the execution of BLAST either on dedicated clusters or cloud environments. Spark’s pipe operator is used to invoke BLAST as an external library on partitioned data of a query. All the input data (the query file and the database) and output data of a query are treated as Spark’s RDDs. SparkBLAST was evaluated on both Google and Microsoft Azure Clouds, for several configurations and dataset sizes. Experimental results show that SparkBLAST improves scalability when compared to CloudBLAST in all scenarios presented in this paper.