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.

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.

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.

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.

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 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.

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 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.

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.