To address the 'Small Files Problem,' a Hadoop administrator can use CombineFileInputFormat. This technique allows the efficient processing of small files by combining them into larger input splits, reducing the overhead associated with managing numerous small files and improving overall processing efficiency.

Capacity Planning is used to estimate the processing capacity required for a Hadoop cluster based on data processing needs. It involves analyzing factors like data volume, processing speed, and storage requirements to ensure optimal cluster performance.

Hadoop's ResourceManager is responsible for resource allocation and management in the cluster. It assists in monitoring cluster performance by efficiently allocating resources to applications, ensuring optimal utilization and performance. This includes managing memory, CPU, and other resources for running tasks.

Apache Spark performs computations in-memory, which is a key improvement over the MapReduce model. This in-memory processing reduces the need for intermediate disk storage, resulting in faster data processing and analysis.

Impala's in-memory processing feature enables it to store and analyze data in memory, providing faster query performance and real-time data analysis capabilities in Hadoop clusters.

Data Serialization is a critical factor in Hadoop Streaming API when dealing with streaming data from various sources. Proper serialization ensures that the data is efficiently encoded and decoded, enhancing the performance of data processing in Hadoop Streaming.

Apache Flume facilitates building data pipelines in Hadoop by serving as a reliable and scalable data ingestion tool. It efficiently collects, aggregates, and moves large amounts of log data from various sources to Hadoop storage, making it a valuable component in data pipeline construction.

In capacity planning, the Throughput of hardware components is a key factor. Throughput measures the amount of data that can be processed in a given time, and it influences the overall performance of a Hadoop cluster. Ensuring sufficient throughput is essential for meeting performance requirements.

Data partitioning in Hadoop increases parallelism by distributing data across nodes. This enhances the efficiency of data transformation processes as multiple nodes can work on different partitions concurrently, speeding up overall processing.

To handle a very large input file efficiently, configuring the MapReduce job to adjust block size is crucial. Larger block sizes can lead to more efficient processing by reducing the number of input splits and overhead associated with task startup.