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.

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.

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.

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.

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.

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.

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.

For advanced Hadoop clusters, Apache Spark is a technique used to enhance processing capabilities for complex data analytics. Spark provides in-memory processing, iterative machine learning, and interactive queries, making it suitable for advanced analytics tasks.

The concept of Data Localization is crucial in designing a Hadoop cluster. It involves placing data close to where it is most frequently accessed, reducing latency and improving overall system performance. Efficient data processing and resource utilization are achieved by strategically placing data across the cluster.

MRUnit is a Java-based framework commonly used for unit testing in Hadoop applications. It allows developers to test their MapReduce programs in an isolated environment, making it easier to identify and fix bugs before deploying the code to a Hadoop cluster.