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.

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.

Stream processing is the most effective for immediate fraud detection in a financial institution. It enables the continuous analysis of incoming data in real-time, allowing for swift identification and response to fraudulent activities as they occur.

Rack awareness in Hadoop refers to the ability of the cluster to be aware of the physical location of nodes within a rack. It contributes to efficiency by optimizing data locality, ensuring that data processing is performed on nodes that are close to the stored data. This minimizes data transfer across the network, improving performance.

The primary role of DataNodes in a Hadoop cluster is to store and manage data blocks. They are responsible for storing the actual data and are distributed across the cluster to ensure fault tolerance and parallel data processing. DataNodes report to the NameNode about the health and status of the data blocks they store.

Split-by in Sqoop specifies the database column to be used for splitting the data during import. This is particularly useful when dealing with large datasets, allowing for parallel processing and efficient data import.

In Kerberos-based authentication for Hadoop, the krb5.conf file is crucial. It defines the realms, KDCs (Key Distribution Centers), and other configuration parameters necessary for secure authentication and authorization in a Hadoop cluster.

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.