For real-time data syncing between Hadoop and RDBMS, Sqoop can be integrated with Apache Kafka. Kafka enables the seamless and real-time transfer of data between Hadoop and relational databases, supporting continuous data integration.

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.

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.

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.

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.

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.

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.

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.

For ensuring high availability in Hadoop, an administrator must configure NameNode High Availability (NameNode HA) effectively. This involves setting up multiple NameNodes and ensuring seamless failover in case of a NameNode failure, enhancing the reliability of the Hadoop cluster.

Kerberos in Hadoop primarily plays the role of authentication. It ensures that only legitimate users and services can access the Hadoop cluster by verifying their identities through a secure authentication process.

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.

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.