Spark achieves fault tolerance through checkpointing. Periodically, Spark saves the state of the distributed computation to a reliable distributed file system, allowing it to recover lost data and continue processing in the event of a node failure.

A Combiner in MapReduce optimizes data processing by aggregating intermediate outputs from the Mapper before sending them to the Reducer. This reduces the volume of data transferred over the network, improving overall performance by minimizing data movement.

In the event of a NameNode failure, the system can be recovered by restoring it from a backup. Regular backups of the NameNode metadata are essential for quick and efficient recovery in case of a NameNode failure.

The initial step in debugging unexpected output in a Hadoop job should focus on reviewing the Mapper Logic. Analyzing how data is processed in the mapping phase helps identify issues that may affect the final output, such as incorrect data transformations or filtering.

Apache Arrow is a cross-language development platform that plays a significant role in optimizing data serialization in Hadoop ecosystems. It provides a standardized in-memory format for efficient data interchange between different data processing frameworks.

The replication factor in Hadoop cluster configuration is crucial for fault tolerance. It determines the number of copies of each data block stored across the cluster. By replicating data, Hadoop ensures that even if a DataNode fails, there are backup copies available, enhancing the system's resilience to node failures.

The High Availability feature in Hadoop ensures that there is a backup or standby NameNode that takes over in case the primary NameNode fails. This ensures continuous functioning and fault tolerance in the Hadoop system.

YARN's ResourceManager handles large-scale applications differently from Hadoop 1.x's JobTracker by employing dynamic resource allocation. It dynamically allocates resources to applications based on their needs, optimizing resource utilization and improving scalability compared to the fixed assignment in Hadoop 1.x.

Advanced security configurations in Hadoop often involve using Apache Ranger for fine-grained access control. Apache Ranger provides centralized security administration and fine-grained access policies, enabling administrators to define and manage access controls for Hadoop components.

A common first step in troubleshooting an unresponsive DataNode is to check network connectivity. Network issues can lead to communication problems between nodes, impacting the DataNode's responsiveness. Ensuring proper connectivity is crucial for the smooth operation of a Hadoop cluster.

Apache Ambari contributes to the Hadoop ecosystem by providing cluster management and monitoring capabilities. It simplifies the installation, configuration, and management of Hadoop clusters, making it easier for administrators to handle complex tasks related to cluster operations.

In the Mapper class, the method called for each key/value pair in the input data is map(). The map() method is responsible for processing the input and emitting intermediate key-value pairs, which are then sorted and passed to the Reducer.