Apache Spark's RDD (Resilient Distributed Dataset) abstraction is a fundamental data structure that provides fault-tolerant distributed processing of data across nodes. It allows efficient data handling and transformation in a parallel and resilient manner.

Mapper functions in Hadoop are crucial for transforming unstructured data into a structured format. Mappers are responsible for processing input data and generating key-value pairs that serve as input for the subsequent stages in the MapReduce process. They play a key role in converting raw data into a format suitable for analysis.

The component responsible for distributing and balancing data across the Hadoop cluster is the ResourceManager. It manages the allocation of resources and job scheduling, ensuring efficient utilization of cluster resources and optimal data distribution.

YARN's resource management adapts to fluctuating data loads through Resource Preemption. If a high-priority application requires resources, YARN can preempt resources from lower-priority applications, ensuring that critical workloads receive the necessary resources for efficient processing.

The practice of unit testing is important for debugging and maintaining Hadoop applications. Unit tests focus on validating the functionality of individual components or modules, ensuring that each part of the application works as intended. This is essential for identifying and fixing bugs during development.

Co-processors in HBase allow users to extend the functionality of HBase servers by running custom code alongside the normal processing. This can be used for tasks like custom filtering, in-memory processing, and parallel computing, enhancing the capabilities of HBase for specific use cases.

Complex data flows in Apache Flume are managed using a topology-based approach. The topology allows the definition of a flow's structure, components, and their interconnections, ensuring efficiency and scalability in handling intricate data processing tasks.

Ambari is a powerful tool for monitoring the health and performance of a Hadoop cluster. It provides a web-based interface to manage, monitor, and secure the Hadoop ecosystem components, making it an effective choice for administrators.

The 'key-value pair' in Hadoop Streaming API is significant for parallel processing. It allows data to be split into chunks, and each chunk is processed independently, enabling parallel execution of tasks across the cluster. This parallelism contributes to the efficiency and scalability of the data processing pipeline.

Apache Pig is a platform for processing and analyzing large datasets in Hadoop. Its primary role is data transformation, providing a high-level scripting language, Pig Latin, to express data transformation tasks easily. Pig converts these scripts into a series of MapReduce jobs for execution.