Apache Spark is like Python’s Pandas and is like SQL databases. It can manipulate datasets, filter, integrate, transform.
But Spark was designed from scratch with horizontal scalability and parallelism in mind, which makes it capable of handling datasets with billions or even unknown number of rows — even if a bit less flexible than Pandas.
This is not new in the industry. Enterprise editions of commercial SQL databases are parallel and scalable since a very long time, being also very expensive in all levels of the stack: service/support, software and hardware.
But Spark is free software. And can use Hadoop — also a free software — as scalable and highly available storage, on cheap commodity hardware. In addition, it has a vibrant community and a democratic ecosystem of services and support.
As with all Open Source, Apache Spark changes the economic landscape of massive data processing systems market, taking money out of a few proprietary HW and SW vendors and pulverizing it locally on people and support.