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 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.

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.

Hive utilizes SerDes (Serializer/Deserializer) to process data that is not structured in a traditional database format. SerDes allow Hive to interpret and convert data between its internal representation and the external format, making it versatile for handling various data structures.

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.

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.

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.

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.

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.