MapReduce optimizes the processing of large datasets through data partitioning. This mechanism involves dividing the input data into smaller partitions, with each partition processed independently by different nodes. It facilitates parallel processing and efficient resource utilization in the Hadoop cluster.

ZooKeeper plays the role of configuration management in the Hadoop ecosystem. It is a distributed coordination service that helps manage and synchronize configuration information across the cluster, ensuring consistency and reliability in a distributed environment.

The selection of Data Nodes is essential in determining the processing power of a Hadoop cluster. Data Nodes are responsible for storing and processing data, and the number and capacity of these nodes significantly impact the overall processing capabilities of the cluster.

ZooKeeper plays a crucial role in maintaining high availability by providing coordination services. It helps in synchronizing distributed processes and managing configuration information, making it easier to handle failover scenarios and ensuring that the Hadoop cluster operates smoothly.

Apache Pig is a high-level scripting language built for Hadoop that excels at complex data transformations and aggregations. It provides an abstraction over MapReduce and simplifies the development of intricate data processing tasks. Pig's ease of use and flexibility make it suitable for scenarios requiring complex data transformations.

The Java-based API commonly utilized to interface with Hadoop's HDFS is the HDFS API. This API allows developers to interact with HDFS programmatically, enabling tasks such as reading and writing data to the distributed file system.

For advanced data processing in Hadoop using Java, the Apache Spark API provides more flexibility than traditional MapReduce. Spark offers in-memory processing, iterative processing, and a variety of libraries, making it well-suited for complex data processing tasks.

Logging is a common practice in debugging Hadoop applications. Developers use logging statements strategically to capture information about the flow and state of the application at various points. This helps in diagnosing issues, monitoring the application's behavior, and improving overall performance.

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.

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.