After receiving the sorted output from the Mapper, the Reducer in MapReduce performs aggregation. It combines the intermediate key-value pairs based on the keys, producing the final output. This phase is crucial for summarizing and processing the data.

The integration of Scala with Hadoop is often facilitated through the Apache Flink framework for distributed computing. Flink is designed for stream processing and batch processing, providing high-throughput, low-latency, and stateful processing capabilities.

Hadoop Streaming API can be integrated with Apache Spark for enhanced performance in complex data processing tasks. Spark provides in-memory processing, which significantly improves the speed of data processing compared to traditional batch processing frameworks.

The primary tool used for debugging Hadoop MapReduce applications is the Hadoop Debugging Tool. It helps developers identify and troubleshoot issues in their MapReduce code by providing insights into the execution flow and intermediate outputs.

Impala supports Vectorized Query Execution as an advanced feature for optimizing distributed queries. This technique processes data in batches, leveraging CPU SIMD (Single Instruction, Multiple Data) instructions for better performance, especially in analytics and data processing tasks.

Point-in-Time recovery in Hadoop is crucial for ensuring Data Consistency. It allows users to recover data to a specific point in time, maintaining consistency and integrity in situations such as accidental data deletion or corruption.

A key characteristic of batch processing in Hadoop is high throughput. Batch processing is designed for processing large volumes of data at once, optimizing for efficiency and throughput rather than real-time response. It is suitable for tasks that can tolerate some delay in processing.

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.

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.

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.

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.

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.