In complex data transformation tasks involving multiple data sources, Apache Sqoop is a preferred approach. Sqoop facilitates efficient and accurate data transfer between Hadoop and relational databases, ensuring seamless integration of diverse data sources for comprehensive transformations.

Load Balancing is key to maintaining the efficiency of a Hadoop cluster as data volume grows. It ensures that the computational load is evenly distributed among the nodes in the cluster, preventing any single node from becoming a bottleneck.

MapReduce handles large datasets in a distributed computing environment through data partitioning. The input data is divided into smaller chunks, and each chunk is processed independently by different nodes in the cluster. This parallel processing enhances the overall efficiency of data analysis.

Replication is the process by which HDFS ensures that each data block has the correct number of replicas. This helps in achieving fault tolerance by storing multiple copies of data across different nodes in the cluster.

The Partitioner in MapReduce determines how the data output from Mappers is distributed to Reducers. It partitions the data based on a specified key, ensuring that all data for a given key is processed by the same Reducer. This influences the way data is grouped and processed during the shuffle phase in the MapReduce job.

In a scenario involving streaming data, the Avro file format would be most efficient. Avro is a binary serialization format that supports schema evolution and is suitable for streaming data due to its compact structure and efficient serialization, making it well-suited for real-time data processing in Hadoop.

Authentication is the process by which Hadoop ensures that a user or service is actually who they claim to be. It involves verifying the identity of users or services before granting access to the Hadoop cluster.

HDFS ensures data integrity during transmission through checksum verification. Each block of data is associated with a checksum, and the checksums are verified during read operations to detect and correct any data corruption that may have occurred during transmission. This mechanism enhances the reliability of data stored in HDFS.

Sqoop is the tool in the Hadoop ecosystem best known for data ingestion from various sources into HDFS. It simplifies the transfer of data between Hadoop and relational databases, facilitating the import and export of data in a Hadoop cluster.

The mapred.reduce.tasks property in MapReduce allows for the customization of the number of Reduce tasks. This property can be set to control the parallelism of the Reduce phase based on the characteristics of the data and the cluster.