Which of the following properties applies to RDDs?

Resilient Distributed Datasets (RDDs) are a fundamental abstraction in Apache Spark that provide fault tolerance and scalability for distributed data processing. One of the key properties of RDDs is that they keep track of lineage. This lineage is a directed acyclic graph (DAG) that records the sequence of transformations that have been applied to the data. By maintaining this lineage information, Spark can recompute lost data after a failure, enabling efficient fault tolerance.

Additionally, RDDs are designed to be recreated on demand. This means if any partition of the data is lost, Spark can reconstruct it using the lineage information. This on-demand recreation is crucial for ensuring data integrity and managing resources effectively in a distributed environment.

Both of these properties emphasize the robustness and flexibility of RDDs in handling large-scale data processing tasks, highlighting why the response indicating both lineage tracking and the ability to recreate data on demand is the correct choice.