The ApplicationMaster in YARN architecture is responsible for dividing the job into tasks and scheduling them on different nodes. It negotiates resources with the ResourceManager and manages the execution of tasks.

In a scenario of uneven data distribution, implementing the optimization strategy of Data Shuffling is essential. Data Shuffling redistributes data across the cluster to achieve a more balanced workload, preventing hotspots and ensuring efficient parallel processing in a Hadoop cluster.

For analyzing web log data in Hive, using the ORC (Optimized Row Columnar) storage format is optimal. ORC is highly optimized for read-heavy workloads, offering efficient compression and predicate pushdown, resulting in improved query performance.

In YARN, ApplicationMaster is a critical process that optimizes the use of resources across the cluster. It negotiates resources with the ResourceManager and manages the execution of tasks on individual nodes.

In advanced Hadoop deployments, batch processing is often optimized for performance by leveraging in-memory processing. This involves storing intermediate data in memory rather than writing it to disk, reducing the time needed for data access and improving overall processing speed. In-memory processing is a key strategy for enhancing the performance of batch processing jobs in Hadoop.

DistCp (Distributed Copy) is commonly used in Hadoop for incremental backups of HDFS data. It efficiently copies large amounts of data between clusters and supports the incremental copying of only the changed data, reducing the overhead of full backups.

Adjusting the 'shuffle' parameter in Hadoop can significantly improve the performance of MapReduce jobs. The shuffle phase involves the movement of intermediate data between the Map and Reduce tasks, and tuning this parameter can optimize the data transfer process.

In a Hadoop cluster, the ResourceManager is typically scaled first for performance enhancement. It manages resource allocation and scheduling, and scaling it ensures better coordination of resources, leading to improved job execution and overall cluster performance.

In the context of handling memory-intensive applications, garbage collection is crucial in advanced Hadoop tuning. Efficient garbage collection helps reclaim memory occupied by unused objects, preventing memory leaks and enhancing the overall performance of Hadoop applications.

MapReduce handles large datasets in a distributed computing environment through data partitioning. The input data is divided into smaller chunks, and each chunk is processed independently by different nodes in the cluster. This parallel processing enhances the overall efficiency of data analysis.