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.

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.

Sqoop achieves high throughput in data transfer using the Direct Mode, which allows direct communication between the Sqoop client and the database, bypassing the need for intermediate storage in Hadoop. This results in faster data transfers with reduced latency.

The feature of YARN that helps in improving the scalability of the Hadoop ecosystem is Horizontal Scalability. YARN allows for the addition of more nodes to the cluster, providing horizontal scalability and the ability to handle larger workloads efficiently.

The tool used for simulating cluster conditions on a single machine for testing is the MiniCluster. It allows developers to test their Hadoop applications in a controlled environment, simulating the behavior of a Hadoop cluster on a local machine for ease of debugging and testing.

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.