Apache Spark is the most widely used engine for scalable computing. 

Circa of the 80% of the Fortune 500 use Apache Spark in their data analysis process.

Apache Spark web page defines it as a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

Apache Spark uses parallel capabilities with master and slaves relationships. 
This method allows run process large-scale process in short periods.

A master will receive a task and will divide it into chunks. Every chunk will be accepted and executed by workers.

Thanks to that, multiple workers can execute small tasks in parallel and reduce the total process time. 

Apache Spark also allows processing data using multiple languages such as Python, SQL, Scala, Java, or R. 

It means thats' possible to develop your data applications in your preferred language. 

Apache Spark provides stand-alone or clusters capabilities. 

That means that it is flexible enough to adapt to data size, processes complexity, and time requirements.

Another benefit is that it enables connection to multiple data sources such as Cassandra, SQL Server, Elasticsearch, Hive, and more.