What mechanisms does Spark offer for handling large-scale datasets?

Spark provides several mechanisms for handling large-scale data sets.

1. RDD (Resilient Distributed Dataset): RDD is the fundamental data structure in Spark that enables users to perform parallel computation across nodes in a cluster in memory. It is fault-tolerant, supports partitioning, and can be reused in multiple operations.
2. DataFrame and Dataset are APIs in Spark used for handling structured data, offering a SQL-like query interface for easy processing and analysis of large datasets.
3. Spark SQL is a module within Spark that is used for handling structured data, allowing data querying and analysis using SQL statements while seamlessly integrating with DataFrame and Dataset APIs.
4. MLlib is a library in Spark that is used for machine learning. It offers a variety of common machine learning algorithms and tools to help users with large-scale machine learning tasks on datasets.
5. Spark Streaming is a module in Spark that is used for real-time data processing. It can convert real-time data streams into a series of separate RDDs, allowing for processing and analysis of real-time data.
6. GraphX is a library in Spark designed for graph computation, offering a range of algorithms and tools to assist users in processing and analyzing large-scale graph data.